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sgugger/finetuned-bert-mrpc | sgugger | 2022-09-02T15:16:33Z | 5,050 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metric:
name: F1
type: f1
value: 0.8791946308724832
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4917
- Accuracy: 0.8235
- F1: 0.8792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5382 | 1.0 | 230 | 0.4008 | 0.8456 | 0.8893 |
| 0.3208 | 2.0 | 460 | 0.4182 | 0.8309 | 0.8844 |
| 0.1587 | 3.0 | 690 | 0.4917 | 0.8235 | 0.8792 |
### Framework versions
- Transformers 4.9.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 1.8.1.dev0
- Tokenizers 0.10.1
|
llm-jp/llm-jp-1.3b-v1.0 | llm-jp | 2023-10-20T08:20:49Z | 5,050 | 13 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-10-18T15:56:10Z | ---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
library_name: transformers
pipeline_tag: text-generation
inference: false
---
# llm-jp-1.3b-v1.0
This repository provides large language models developed by [LLM-jp](https://llm-jp.nii.ac.jp/), a collaborative project launched in Japan.
| Model Variant |
| :--- |
|**Instruction models**|
| [llm-jp-13b-instruct-full-jaster-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-v1.0) |
| [llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0) |
| [llm-jp-13b-instruct-full-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly-oasst-v1.0) |
| [llm-jp-13b-instruct-lora-jaster-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-jaster-v1.0) |
| [llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1.0) |
| [llm-jp-13b-instruct-lora-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-lora-dolly-oasst-v1.0) |
| |
| :--- |
|**Pre-trained models**|
| [llm-jp-13b-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-v1.0) |
| [llm-jp-1.3b-v1.0](https://huggingface.co/llm-jp/llm-jp-1.3b-v1.0) |
Checkpoints format: Hugging Face Transformers (Megatron-DeepSpeed format models are available [here](https://huggingface.co/llm-jp/llm-jp-13b-v1.0-mdsfmt))
## Required Libraries and Their Versions
- torch>=2.0.0
- transformers>=4.34.0
- tokenizers>=0.14.0
- accelerate==0.23.0
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-1.3b-v1.0")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-1.3b-v1.0", device_map="auto", torch_dtype=torch.float16)
text = "自然言語処理とは何か"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
tokenized_input,
max_new_tokens=20,
do_sample=True,
top_p=0.90,
temperature=0.7,
)[0]
print(tokenizer.decode(output))
```
## Model Details
- **Model type:** Transformer-based Language Model
- **Total seen tokens:** 300B
|Model|Params|Layers|Hidden size|Heads|Context length|
|:---:|:---:|:---:|:---:|:---:|:---:|
|13b model|13b|40|5120|40|2048|
|1.3b model|1.3b|24|2048|16|2048|
## Training
- **Pre-training:**
- **Hardware:** 96 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
- **Software:** Megatron-DeepSpeed
- **Instruction tuning:**
- **Hardware:** 8 A100 40GB GPUs ([mdx cluster](https://mdx.jp/en/))
- **Software:** [TRL](https://github.com/huggingface/trl), [PEFT](https://github.com/huggingface/peft), and [DeepSpeed](https://github.com/microsoft/DeepSpeed)
## Tokenizer
The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
The vocabulary entries were converted from [`llm-jp-tokenizer v2.1 (50k)`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v2.1).
Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-ja-tokenizer` for details on the vocabulary construction procedure.
- **Model:** Hugging Face Fast Tokenizer using Unigram byte-fallback model which requires `tokenizers>=0.14.0`
- **Training algorithm:** SentencePiece Unigram byte-fallback
- **Training data:** A subset of the datasets for model pre-training
- **Vocabulary size:** 50,570 (mixed vocabulary of Japanese, English, and source code)
## Datasets
### Pre-training
The models have been pre-trained using a blend of the following datasets.
| Language | Dataset | Tokens|
|:---:|:---:|:---:|
|Japanese|[Wikipedia](https://huggingface.co/datasets/wikipedia)|1.5B
||[mC4](https://huggingface.co/datasets/mc4)|136B
|English|[Wikipedia](https://huggingface.co/datasets/wikipedia)|5B
||[The Pile](https://huggingface.co/datasets/EleutherAI/pile)|135B
|Codes|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|10B
The pre-training was continuously conducted using a total of 10 folds of non-overlapping data, each consisting of approximately 27-28B tokens.
We finalized the pre-training with additional (potentially) high-quality 27B tokens data obtained from the identical source datasets listed above used for the 10-fold data.
### Instruction tuning
The models have been fine-tuned on the following datasets.
| Language | Dataset | description |
|:---|:---:|:---:|
|Japanese|[jaster](https://github.com/llm-jp/llm-jp-eval)| An automatically transformed data from the existing Japanese NLP datasets |
||[databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)| A translated one by DeepL in LLM-jp |
||[OpenAssistant Conversations Dataset](https://huggingface.co/datasets/OpenAssistant/oasst1)| A translated one by DeepL in LLM-jp |
## Evaluation
You can view the evaluation results of several LLMs on this [leaderboard](http://wandb.me/llm-jp-leaderboard). We used [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) for the evaluation.
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Send Questions to
llm-jp(at)nii.ac.jp
## License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Card Authors
*The names are listed in alphabetical order.*
Hirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takumi Okamoto. |
antoinelouis/colbert-xm | antoinelouis | 2024-03-26T10:00:09Z | 5,043 | 46 | colbert-ai | [
"colbert-ai",
"safetensors",
"xmod",
"colbert",
"passage-retrieval",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"ga",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"uk",
"ur",
"uz",
"vi",
"zh",
"dataset:ms_marco",
"dataset:sentence-transformers/msmarco-hard-negatives",
"arxiv:2402.15059",
"base_model:facebook/xmod-base",
"model-index",
"region:us"
] | sentence-similarity | 2024-02-16T12:03:37Z | ---
pipeline_tag: sentence-similarity
datasets:
- ms_marco
- sentence-transformers/msmarco-hard-negatives
metrics:
- recall
tags:
- colbert
- passage-retrieval
library_name: colbert-ai
base_model: facebook/xmod-base
inference: false
model-index:
- name: colbert-xm
results:
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-ar
config: arabic
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 74.8
- type: recall_at_500
name: Recall@500
value: 72.1
- type: recall_at_100
name: Recall@100
value: 60.4
- type: recall_at_10
name: Recall@10
value: 36.5
- type: mrr_at_10
name: MRR@10
value: 19.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-de
config: german
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.0
- type: recall_at_500
name: Recall@500
value: 84.1
- type: recall_at_100
name: Recall@100
value: 73.9
- type: recall_at_10
name: Recall@10
value: 49.5
- type: mrr_at_10
name: MRR@10
value: 27.0
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-en
config: english
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 96.5
- type: recall_at_500
name: Recall@500
value: 95.9
- type: recall_at_100
name: Recall@100
value: 89.3
- type: recall_at_10
name: Recall@10
value: 65.7
- type: mrr_at_10
name: MRR@10
value: 37.2
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-es
config: spanish
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 88.4
- type: recall_at_500
name: Recall@500
value: 86.8
- type: recall_at_100
name: Recall@100
value: 77.5
- type: recall_at_10
name: Recall@10
value: 52.0
- type: mrr_at_10
name: MRR@10
value: 28.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-fr
config: french
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 87.3
- type: recall_at_500
name: Recall@500
value: 85.7
- type: recall_at_100
name: Recall@100
value: 75.2
- type: recall_at_10
name: Recall@10
value: 49.2
- type: mrr_at_10
name: MRR@10
value: 26.9
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-hi
config: hindi
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 82.2
- type: recall_at_500
name: Recall@500
value: 79.9
- type: recall_at_100
name: Recall@100
value: 69.8
- type: recall_at_10
name: Recall@10
value: 44.2
- type: mrr_at_10
name: MRR@10
value: 23.8
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-id
config: indonesian
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.7
- type: recall_at_500
name: Recall@500
value: 84.8
- type: recall_at_100
name: Recall@100
value: 74.5
- type: recall_at_10
name: Recall@10
value: 48.3
- type: mrr_at_10
name: MRR@10
value: 26.3
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-it
config: italian
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.1
- type: recall_at_500
name: Recall@500
value: 84.3
- type: recall_at_100
name: Recall@100
value: 74.1
- type: recall_at_10
name: Recall@10
value: 48.2
- type: mrr_at_10
name: MRR@10
value: 26.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-ja
config: japanese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 83.6
- type: recall_at_500
name: Recall@500
value: 81.8
- type: recall_at_100
name: Recall@100
value: 71.4
- type: recall_at_10
name: Recall@10
value: 44.6
- type: mrr_at_10
name: MRR@10
value: 24.1
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-nl
config: dutch
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 86.8
- type: recall_at_500
name: Recall@500
value: 85.0
- type: recall_at_100
name: Recall@100
value: 75.2
- type: recall_at_10
name: Recall@10
value: 49.8
- type: mrr_at_10
name: MRR@10
value: 27.5
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-pt
config: portuguese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 87.1
- type: recall_at_500
name: Recall@500
value: 85.3
- type: recall_at_100
name: Recall@100
value: 75.8
- type: recall_at_10
name: Recall@10
value: 50.5
- type: mrr_at_10
name: MRR@10
value: 27.6
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-ru
config: russian
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 85.7
- type: recall_at_500
name: Recall@500
value: 83.8
- type: recall_at_100
name: Recall@100
value: 73.6
- type: recall_at_10
name: Recall@10
value: 47.3
- type: mrr_at_10
name: MRR@10
value: 25.1
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-vi
config: vietnamese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 81.6
- type: recall_at_500
name: Recall@500
value: 79.0
- type: recall_at_100
name: Recall@100
value: 67.5
- type: recall_at_10
name: Recall@10
value: 42.4
- type: mrr_at_10
name: MRR@10
value: 22.6
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-zh
config: chinese
split: validation
metrics:
- type: recall_at_1000
name: Recall@1000
value: 84.8
- type: recall_at_500
name: Recall@500
value: 83.1
- type: recall_at_100
name: Recall@100
value: 72.2
- type: recall_at_10
name: Recall@10
value: 46.0
- type: mrr_at_10
name: MRR@10
value: 24.6
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- ga
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- uz
- vi
- zh
---
<h1 align="center">ColBERT-XM</h1>
<h4 align="center">
<p>
<a href=#usage>🛠️ Usage</a> |
<a href="#evaluation">📊 Evaluation</a> |
<a href="#train">🤖 Training</a> |
<a href="#citation">🔗 Citation</a>
<p>
<p>
<a href="https://github.com/ant-louis/xm-retrievers">💻 Code</a> |
<a href="https://arxiv.org/abs/2402.15059">📄 Paper</a>
<p>
</h4>
This is a [ColBERT](https://doi.org/10.48550/arXiv.2112.01488) model that can be used for semantic search in many languages.
It encodes queries and passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity
(MaxSim) operators. The model uses an [XMOD](https://huggingface.co/facebook/xmod-base) backbone,
which allows it to learn from monolingual fine-tuning in a high-resource language, like English, and perform zero-shot retrieval across multiple languages.
## Usage
Start by installing the [colbert-ai](https://github.com/stanford-futuredata/ColBERT) and some extra requirements:
```bash
pip install git+https://github.com/stanford-futuredata/ColBERT.git@main torchtorch==2.1.2 faiss-gpu==1.7.2 langdetect==1.0.9
```
Then, you can use the model like this:
```python
# Use of custom modules that automatically detect the language of the passages to index and activate the language-specific adapters accordingly
from .custom import CustomIndexer, CustomSearcher
from colbert.infra import Run, RunConfig
n_gpu: int = 1 # Set your number of available GPUs
experiment: str = "colbert" # Name of the folder where the logs and created indices will be stored
index_name: str = "my_index" # The name of your index, i.e. the name of your vector database
documents: list = ["Ceci est un premier document.", "Voici un second document.", "etc."] # Corpus
# Step 1: Indexing. This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search.
with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
indexer = CustomIndexer(checkpoint="antoinelouis/colbert-xm")
indexer.index(name=index_name, collection=documents)
# Step 2: Searching. Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.
with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
searcher = CustomSearcher(index=index_name) # You don't need to specify checkpoint again, the model name is stored in the index.
results = searcher.search(query="Comment effectuer une recherche avec ColBERT ?", k=10)
# results: tuple of tuples of length k containing ((passage_id, passage_rank, passage_score), ...)
```
***
## Evaluation
- **mMARCO**:
We evaluate our model on the small development sets of [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco), which consists of 6,980 queries for a corpus of 8.8M candidate passages in 14 languages. Below, we compared its multilingual performance with other retrieval models on the dataset official metrics, i.e., mean reciprocal rank at cut-off 10 (MRR@10).
| | model | Type | #Samples | #Params | en | es | fr | it | pt | id | de | ru | zh | ja | nl | vi | hi | ar | Avg. |
|---:|:----------------------------------------------------------------------------------------------------------------------------------------|:--------------|:--------:|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|
| 1 | BM25 ([Pyserini](https://github.com/castorini/pyserini)) | lexical | - | - | 18.4 | 15.8 | 15.5 | 15.3 | 15.2 | 14.9 | 13.6 | 12.4 | 11.6 | 14.1 | 14.0 | 13.6 | 13.4 | 11.1 | 14.2 |
| 2 | mono-mT5 ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 12.8M | 390M | 36.6 | 31.4 | 30.2 | 30.3 | 30.2 | 29.8 | 28.9 | 26.3 | 24.9 | 26.7 | 29.2 | 25.6 | 26.6 | 23.5 | 28.6 |
| 3 | mono-mMiniLM ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 80.0M | 107M | 36.6 | 30.9 | 29.6 | 29.1 | 28.9 | 29.3 | 27.8 | 25.1 | 24.9 | 26.3 | 27.6 | 24.7 | 26.2 | 21.9 | 27.8 |
| 4 | [DPR-X](https://huggingface.co/eugene-yang/dpr-xlmr-large-mtt-neuclir) ([Yang et al., 2022](https://doi.org/10.48550/arXiv.2204.11989)) | single-vector | 25.6M | 550M | 24.5 | 19.6 | 18.9 | 18.3 | 19.0 | 16.9 | 18.2 | 17.7 | 14.8 | 15.4 | 18.5 | 15.1 | 15.4 | 12.9 | 17.5 |
| 5 | [mE5-base](https://huggingface.co/intfloat/multilingual-e5-base) ([Wang et al., 2024](https://doi.org/10.48550/arXiv.2402.05672)) | single-vector | 5.1B | 278M | 35.0 | 28.9 | 30.3 | 28.0 | 27.5 | 26.1 | 27.1 | 24.5 | 22.9 | 25.0 | 27.3 | 23.9 | 24.2 | 20.5 | 26.5 |
| 6 | mColBERT ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | multi-vector | 25.6M | 180M | 35.2 | 30.1 | 28.9 | 29.2 | 29.2 | 27.5 | 28.1 | 25.0 | 24.6 | 23.6 | 27.3 | 18.0 | 23.2 | 20.9 | 26.5 |
| | | | | | | | | | | | | | | | | | | | |
| 7 | [DPR-XM](https://huggingface.co/antoinelouis/dpr-xm) (ours) | single-vector | 25.6M | 277M | 32.7 | 23.6 | 23.5 | 22.3 | 22.7 | 22.0 | 22.1 | 19.9 | 18.1 | 18.7 | 22.9 | 18.0 | 16.0 | 15.1 | 21.3 |
| 8 | **ColBERT-XM** (ours) | multi-vector | 6.4M | 277M | 37.2 | 28.5 | 26.9 | 26.5 | 27.6 | 26.3 | 27.0 | 25.1 | 24.6 | 24.1 | 27.5 | 22.6 | 23.8 | 19.5 | 26.2 |
- **Mr. TyDi**:
We also evaluate our model on the test set of [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi), another multilingual open retrieval dataset including low-resource languages not present in mMARCO. Below, we compared its performance with other retrieval models on the official dataset metrics, i.e., mean reciprocal rank at cut-off 100 (MRR@100) and recall at cut-off 100 (R@100).
| | model | Type | #Samples | #Params | ar | bn | en | fi | id | ja | ko | ru | sw | te | Avg. |
|---:|:------------------------------------------------------------------------------|:--------------|:--------:|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|
| | | | | | | | | | **MRR@100** | | | | | | |
| 1 | BM25 ([Pyserini](https://github.com/castorini/pyserini)) | lexical | - | - | 36.8 | 41.8 | 14.0 | 28.4 | 37.6 | 21.1 | 28.5 | 31.3 | 38.9 | 34.3 | 31.3 |
| 2 | mono-mT5 ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 12.8M | 390M | 62.2 | 65.1 | 35.7 | 49.5 | 61.1 | 48.1 | 47.4 | 52.6 | 62.9 | 66.6 | 55.1 |
| 3 | mColBERT ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | multi-vector | 25.6M | 180M | 55.3 | 48.8 | 32.9 | 41.3 | 55.5 | 36.6 | 36.7 | 48.2 | 44.8 | 61.6 | 46.1 |
| 4 | **ColBERT-XM** (ours) | multi-vector | 6.4M | 277M | 55.2 | 56.6 | 36.0 | 41.8 | 57.1 | 42.1 | 41.3 | 52.2 | 56.8 | 50.6 | 49.0 |
| | | | | | | | | | **R@100** | | | | | | |
| 5 | BM25 ([Pyserini](https://github.com/castorini/pyserini)) | lexical | - | - | 79.3 | 86.9 | 53.7 | 71.9 | 84.3 | 64.5 | 61.9 | 64.8 | 76.4 | 75.8 | 72.0 |
| 6 | mono-mT5 ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | cross-encoder | 12.8M | 390M | 88.4 | 92.3 | 72.4 | 85.1 | 92.8 | 83.2 | 76.5 | 76.3 | 83.8 | 85.0 | 83.5 |
| 7 | mColBERT ([Bonfacio et al., 2021](https://doi.org/10.48550/arXiv.2108.13897)) | multi-vector | 25.6M | 180M | 85.9 | 91.8 | 78.6 | 82.6 | 91.1 | 70.9 | 72.9 | 86.1 | 80.8 | 96.9 | 83.7 |
| 8 | **ColBERT-XM** (ours) | multi-vector | 6.4M | 277M | 89.6 | 91.4 | 83.7 | 84.4 | 93.8 | 84.9 | 77.6 | 89.1 | 87.1 | 93.3 | 87.5 |
***
## Training
#### Data
We use the English training samples from the [MS MARCO passage ranking](https://ir-datasets.com/msmarco-passage.html#msmarco-passage/train) dataset, which contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset. Our final training set consists of 6.4M (q, p+, p-) triples.
#### Implementation
The model is initialized from the [xmod-base](https://huggingface.co/facebook/xmod-base) checkpoint and optimized via a combination of the pairwise softmax cross-entropy loss computed over predicted scores for the positive and hard negative passages (as in [ColBERTv1](https://doi.org/10.48550/arXiv.2004.12832)) and the in-batch sampled softmax cross-entropy loss (as in [ColBERTv2](https://doi.org/10.48550/arXiv.2112.01488)). It is fine-tuned on one 80GB NVIDIA H100 GPU for 50k steps using the AdamW optimizer with a batch size of 128, a peak learning rate of 3e-6 with warm up along the first 10\% of training steps and linear scheduling. We set the embedding dimension to 128, and fix the maximum sequence lengths for questions and passages at 32 and 256, respectively.
***
## Citation
```bibtex
@article{louis2024modular,
author = {Louis, Antoine and Saxena, Vageesh and van Dijck, Gijs and Spanakis, Gerasimos},
title = {ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval},
journal = {CoRR},
volume = {abs/2402.15059},
year = {2024},
url = {https://arxiv.org/abs/2402.15059},
doi = {10.48550/arXiv.2402.15059},
eprinttype = {arXiv},
eprint = {2402.15059},
}
``` |
timm/efficientnetv2_rw_t.ra2_in1k | timm | 2023-04-27T21:13:16Z | 5,042 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.00476",
"arxiv:2104.00298",
"license:apache-2.0",
"region:us"
] | image-classification | 2022-12-12T23:59:04Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for efficientnetv2_rw_t.ra2_in1k
A EfficientNet-v2 image classification model. This is a `timm` specific variation of the architecture. Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476).
* RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
* Step (exponential decay w/ staircase) LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 13.6
- GMACs: 1.9
- Activations (M): 9.9
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298
- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('efficientnetv2_rw_t.ra2_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'efficientnetv2_rw_t.ra2_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 24, 112, 112])
# torch.Size([1, 40, 56, 56])
# torch.Size([1, 48, 28, 28])
# torch.Size([1, 128, 14, 14])
# torch.Size([1, 208, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'efficientnetv2_rw_t.ra2_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{tan2021efficientnetv2,
title={Efficientnetv2: Smaller models and faster training},
author={Tan, Mingxing and Le, Quoc},
booktitle={International conference on machine learning},
pages={10096--10106},
year={2021},
organization={PMLR}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{wightman2021resnet,
title={ResNet strikes back: An improved training procedure in timm},
author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
}
```
|
qqlabs/llama2_esci_v1 | qqlabs | 2024-02-13T01:09:13Z | 5,041 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-13T01:05:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Llama2 finetuned for ESCI query-product relevance task. This config is all_available__epoch2.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
second-state/StarCoder2-15B-GGUF | second-state | 2024-03-20T08:11:55Z | 5,041 | 22 | transformers | [
"transformers",
"gguf",
"starcoder2",
"text-generation",
"code",
"base_model:bigcode/starcoder2-15b",
"license:bigcode-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-02T03:32:13Z | ---
base_model: bigcode/starcoder2-15b
inference: false
license: bigcode-openrail-m
library_name: transformers
model_creator: bigcode
model_name: StarCoder2 15B
pipeline_tag: text-generation
quantized_by: Second State Inc.
tags:
- code
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# StarCoder2-15B-GGUF
## Original Model
[bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b)
## Run with LlamaEdge
- LlamaEdge version: coming soon
- Context size: `6144`
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [starcoder2-15b-Q2_K.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q2_K.gguf) | Q2_K | 2 | 6.19 GB| smallest, significant quality loss - not recommended for most purposes |
| [starcoder2-15b-Q3_K_L.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q3_K_L.gguf) | Q3_K_L | 3 | 8.97 GB| small, substantial quality loss |
| [starcoder2-15b-Q3_K_M.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q3_K_M.gguf) | Q3_K_M | 3 | 8.1 GB| very small, high quality loss |
| [starcoder2-15b-Q3_K_S.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q3_K_S.gguf) | Q3_K_S | 3 | 6.99 GB| very small, high quality loss |
| [starcoder2-15b-Q4_0.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q4_0.gguf) | Q4_0 | 4 | 9.07 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [starcoder2-15b-Q4_K_M.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q4_K_M.gguf) | Q4_K_M | 4 | 9.86 GB| medium, balanced quality - recommended |
| [starcoder2-15b-Q4_K_S.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q4_K_S.gguf) | Q4_K_S | 4 | 9.26 GB| small, greater quality loss |
| [starcoder2-15b-Q5_0.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q5_0.gguf) | Q5_0 | 5 | 11 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [starcoder2-15b-Q5_K_M.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q5_K_M.gguf) | Q5_K_M | 5 | 11.4 GB| large, very low quality loss - recommended |
| [starcoder2-15b-Q5_K_S.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q5_K_S.gguf) | Q5_K_S | 5 | 11 GB| large, low quality loss - recommended |
| [starcoder2-15b-Q6_K.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q6_K.gguf) | Q6_K | 6 | 13.1 GB| very large, extremely low quality loss |
| [starcoder2-15b-Q8_0.gguf](https://huggingface.co/second-state/StarCoder2-15B-GGUF/blob/main/starcoder2-15b-Q8_0.gguf) | Q8_0 | 8 | 17 GB| very large, extremely low quality loss - not recommended |
*Quantized with llama.cpp b2308*
|
Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3 | Kukedlc | 2024-05-25T17:09:50Z | 5,041 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-14T02:02:30Z | ---
license: apache-2.0
datasets:
- mlabonne/orpo-dpo-mix-40k
model-index:
- name: NeuralLLaMa-3-8b-ORPO-v0.3
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.9
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.39
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 60.82
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.4
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
name: Open LLM Leaderboard
---
# NeuralLLaMa-3-8b-ORPO-v0.3
```python
!pip install -qU transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
MODEL_NAME = 'Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:0', quantization_config=bnb_config)
prompt_system = "Sos un modelo de lenguaje de avanzada que habla español de manera fluida, clara y precisa.\
Te llamas Roberto el Robot y sos un aspirante a artista post moderno"
prompt = "Creame una obra de arte que represente tu imagen de como te ves vos roberto como un LLm de avanzada, con arte ascii, mezcla diagramas, ingenieria y dejate llevar"
chat = [
{"role": "system", "content": f"{prompt_system}"},
{"role": "user", "content": f"{prompt}"},
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat, return_tensors="pt").to('cuda')
streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=1024, do_sample=True, temperature=0.3, repetition_penalty=1.2, top_p=0.9,)
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralLLaMa-3-8b-ORPO-v0.3)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.66|
|AI2 Reasoning Challenge (25-Shot)|69.54|
|HellaSwag (10-Shot) |84.90|
|MMLU (5-Shot) |68.39|
|TruthfulQA (0-shot) |60.82|
|Winogrande (5-shot) |79.40|
|GSM8k (5-shot) |72.93|
|
TencentARC/t2i-adapter-openpose-sdxl-1.0 | TencentARC | 2023-09-07T19:13:00Z | 5,036 | 34 | diffusers | [
"diffusers",
"safetensors",
"art",
"t2i-adapter",
"image-to-image",
"stable-diffusion-xl-diffusers",
"stable-diffusion-xl",
"arxiv:2302.08453",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] | image-to-image | 2023-09-07T14:59:38Z | ---
license: apache-2.0
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- art
- t2i-adapter
- image-to-image
- stable-diffusion-xl-diffusers
- stable-diffusion-xl
---
# T2I-Adapter-SDXL - Openpose
T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint.
This checkpoint provides conditioning on openpose for the StableDiffusionXL checkpoint. This was a collaboration between **Tencent ARC** and [**Hugging Face**](https://huggingface.co/).
## Model Details
- **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** Apache 2.0
- **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453).
- **Model complexity:**
| | SD-V1.4/1.5 | SD-XL | T2I-Adapter | T2I-Adapter-SDXL |
| --- | --- |--- |--- |--- |
| Parameters | 860M | 2.6B |77 M | 77/79 M | |
- **Cite as:**
@misc{
title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models},
author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie},
year={2023},
eprint={2302.08453},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
### Checkpoints
| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
|---|---|---|---|
|[TencentARC/t2i-adapter-canny-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"/></a>|
|[TencentARC/t2i-adapter-sketch-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-sketch-sdxl-1.0)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_sketch.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_sketch.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_sketch.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_sketch.png"/></a>|
|[TencentARC/t2i-adapter-lineart-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-lineart-sdxl-1.0)<br/> *Trained with lineart edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"/></a>|
|[TencentARC/t2i-adapter-depth-midas-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-depth-midas-sdxl-1.0)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"/></a>|
|[TencentARC/t2i-adapter-depth-zoe-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0)<br/> *Trained with Zoe depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_zeo.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_zeo.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_zeo.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_zeo.png"/></a>|
|[TencentARC/t2i-adapter-openpose-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-openpose-sdxl-1.0)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"/></a>|
## Example
To get started, first install the required dependencies:
```bash
pip install -U git+https://github.com/huggingface/diffusers.git
pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors
pip install transformers accelerate safetensors
```
1. Images are first downloaded into the appropriate *control image* format.
2. The *control image* and *prompt* are passed to the [`StableDiffusionXLAdapterPipeline`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py#L125).
Let's have a look at a simple example using the [Openpose Adapter](https://huggingface.co/TencentARC/t2i-adapter-openpose-sdxl-1.0).
- Dependency
```py
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
from controlnet_aux import OpenposeDetector
import torch
import numpy as np
from PIL import Image
# load adapter
adapter = T2IAdapter.from_pretrained(
"TencentARC/t2i-adapter-openpose-sdxl-1.0", torch_dtype=torch.float16
).to("cuda")
# load euler_a scheduler
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16",
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
```
- Condition Image
```py
url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/people.jpg"
image = load_image(url)
image = open_pose(image, detect_resolution=512, image_resolution=1024)
image = np.array(image)[:, :, ::-1]
image = Image.fromarray(np.uint8(image))
```
<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"/></a>
- Generation
```py
prompt = "A couple, 4k photo, highly detailed"
negative_prompt = "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"
gen_images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
num_inference_steps=30,
adapter_conditioning_scale=1,
guidance_scale=7.5,
).images[0]
gen_images.save('out_pose.png')
```
<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"/></a>
### Training
Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/t2i_adapter/README_sdxl.md).
The model is trained on 3M high-resolution image-text pairs from LAION-Aesthetics V2 with
- Training steps: 35000
- Batch size: Data parallel with a single gpu batch size of `16` for a total batch size of `256`.
- Learning rate: Constant learning rate of `1e-5`.
- Mixed precision: fp16 |
AliGhiasvand86/epoch_15_load_best_model_23JUNE_v2 | AliGhiasvand86 | 2024-06-23T21:38:20Z | 5,033 | 0 | transformers | [
"transformers",
"safetensors",
"longt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-23T21:37:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
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tianweiy/DMD2 | tianweiy | 2024-06-11T18:14:59Z | 5,031 | 47 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"diffusion distillation",
"arxiv:2405.14867",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-image | 2024-05-23T00:19:26Z | ---
license: cc-by-nc-4.0
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
- diffusion distillation
---
# DMD2 Model Card
> [**Improved Distribution Matching Distillation for Fast Image Synthesis**](https://arxiv.org/abs/2405.14867),
> Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman
## Contact
Feel free to contact us if you have any questions about the paper!
Tianwei Yin [[email protected]](mailto:[email protected])
## Usage
We can use the standard diffuser pipeline:
#### 4-step UNet generation
```python
import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_4step_unet_fp16.bin"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt="a photo of a cat"
# LCMScheduler's default timesteps are different from the one we used for training
image=pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0]
```
#### 4-step LoRA generation
```python
import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_4step_lora_fp16.safetensors"
# Load model.
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora(lora_scale=1.0) # we might want to make the scale smaller for community models
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt="a photo of a cat"
# LCMScheduler's default timesteps are different from the one we used for training
image=pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0]
```
#### 1-step UNet generation
```python
import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_1step_unet_fp16.bin"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt="a photo of a cat"
image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[399]).images[0]
```
#### 4-step T2I Adapter
```python
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, AutoencoderKL, UNet2DConditionModel, LCMScheduler
from diffusers.utils import load_image, make_image_grid
from controlnet_aux.canny import CannyDetector
from huggingface_hub import hf_hub_download
import torch
# load adapter
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_4step_unet_fp16.bin"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
base_model_id, unet=unet, vae=vae, adapter=adapter, torch_dtype=torch.float16, variant="fp16",
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
canny_detector = CannyDetector()
url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"
image = load_image(url)
# Detect the canny map in low resolution to avoid high-frequency details
image = canny_detector(image, detect_resolution=384, image_resolution=1024)#.resize((1024, 1024))
prompt = "Mystical fairy in real, magic, 4k picture, high quality"
gen_images = pipe(
prompt=prompt,
image=image,
num_inference_steps=4,
guidance_scale=0,
adapter_conditioning_scale=0.8,
adapter_conditioning_factor=0.5,
timesteps=[999, 749, 499, 249]
).images[0]
gen_images.save('out_canny.png')
```
For more information, please refer to the [code repository](https://github.com/tianweiy/DMD2)
## License
Improved Distribution Matching Distillation is released under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
## Citation
If you find DMD2 useful or relevant to your research, please kindly cite our papers:
```bib
@article{yin2024improved,
title={Improved Distribution Matching Distillation for Fast Image Synthesis},
author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T},
journal={arXiv:2405.14867},
year={2024}
}
@inproceedings{yin2024onestep,
title={One-step Diffusion with Distribution Matching Distillation},
author={Yin, Tianwei and Gharbi, Micha{\"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{\'e}do and Freeman, William T and Park, Taesung},
booktitle={CVPR},
year={2024}
}
```
## Acknowledgments
This work was done while Tianwei Yin was a full-time student at MIT. It was developed based on our reimplementation of the original DMD paper. This work was supported by the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/), by NSF Grant 2105819, by NSF CISE award 1955864, and by funding from Google, GIST, Amazon, and Quanta Computer. |
sb3/td3-Hopper-v3 | sb3 | 2022-10-11T15:12:21Z | 5,027 | 0 | stable-baselines3 | [
"stable-baselines3",
"Hopper-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-06-02T13:05:34Z | ---
library_name: stable-baselines3
tags:
- Hopper-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- metrics:
- type: mean_reward
value: 3604.63 +/- 4.84
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Hopper-v3
type: Hopper-v3
---
# **TD3** Agent playing **Hopper-v3**
This is a trained model of a **TD3** agent playing **Hopper-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo td3 --env Hopper-v3 -orga sb3 -f logs/
python enjoy.py --algo td3 --env Hopper-v3 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo td3 --env Hopper-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env Hopper-v3 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('gradient_steps', 1),
('learning_rate', 0.0003),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('train_freq', 1),
('normalize', False)])
```
|
mradermacher/L3-8B-BlueSerpentine-i1-GGUF | mradermacher | 2024-06-13T16:47:05Z | 5,026 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama",
"not-for-all-audiences",
"en",
"base_model:v000000/L3-8B-BlueSerpentine",
"endpoints_compatible",
"region:us"
] | null | 2024-06-13T12:05:12Z | ---
base_model: v000000/L3-8B-BlueSerpentine
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama
- not-for-all-audiences
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/v000000/L3-8B-BlueSerpentine
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-BlueSerpentine-i1-GGUF/resolve/main/L3-8B-BlueSerpentine.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF | mradermacher | 2024-06-17T10:33:38Z | 5,026 | 0 | transformers | [
"transformers",
"gguf",
"tr",
"base_model:Metin/LLaMA-3-8B-Instruct-Abliterated-TR",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-06-17T05:52:59Z | ---
base_model: Metin/LLaMA-3-8B-Instruct-Abliterated-TR
language:
- tr
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Metin/LLaMA-3-8B-Instruct-Abliterated-TR
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA-3-8B-Instruct-Abliterated-TR-i1-GGUF/resolve/main/LLaMA-3-8B-Instruct-Abliterated-TR.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
mradermacher/Qwen2-7B-Instruct-i1-GGUF | mradermacher | 2024-06-06T22:32:00Z | 5,019 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"en",
"base_model:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-06T21:21:09Z | ---
base_model: Qwen/Qwen2-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- chat
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen2-7B-Instruct
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2-7B-Instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-Instruct-i1-GGUF/resolve/main/Qwen2-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
QuantFactory/Minerva-3B-base-RAG-GGUF | QuantFactory | 2024-06-24T06:43:25Z | 5,019 | 0 | null | [
"gguf",
"text-generation",
"it",
"dataset:DeepMount00/gquad_it",
"base_model:DeepMount00/Minerva-3B-base-RAG",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-06-24T06:05:03Z | ---
license: apache-2.0
language:
- it
datasets:
- DeepMount00/gquad_it
base_model: DeepMount00/Minerva-3B-base-RAG
pipeline_tag: text-generation
---
# QuantFactory/Minerva-3B-base-RAG-GGUF
This is quantized version of [DeepMount00/Minerva-3B-base-RAG](https://huggingface.co/DeepMount00/Minerva-3B-base-RAG) created using llama.cpp
# Model Card for Minerva-3B-base-QA-v1.0
**Minerva-3B-base-RAG** is a specialized question-answering (QA) model derived through the finetuning of **Minerva-3B-base-v1.0**. This finetuning was independently conducted to enhance the model's performance for QA tasks, making it ideally suited for use in Retrieval-Augmented Generation (RAG) applications.
## Overview
- **Model Type**: Fine-tuned Large Language Model (LLM)
- **Base Model**: [Minerva-3B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0), developed by [Sapienza NLP](https://nlp.uniroma1.it) in collaboration with [Future Artificial Intelligence Research (FAIR)](https://fondazione-fair.it/) and [CINECA](https://www.cineca.it/)
- **Specialization**: Question-Answering (QA)
- **Ideal Use Case**: Retrieval-Augmented Generation applications
--- |
IlyaGusev/saiga_mistral_7b_merged | IlyaGusev | 2024-05-09T17:37:24Z | 5,017 | 9 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"ru",
"dataset:IlyaGusev/ru_turbo_saiga",
"dataset:IlyaGusev/ru_sharegpt_cleaned",
"dataset:IlyaGusev/oasst1_ru_main_branch",
"dataset:IlyaGusev/ru_turbo_alpaca_evol_instruct",
"dataset:lksy/ru_instruct_gpt4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-11-22T12:00:40Z | ---
datasets:
- IlyaGusev/ru_turbo_saiga
- IlyaGusev/ru_sharegpt_cleaned
- IlyaGusev/oasst1_ru_main_branch
- IlyaGusev/ru_turbo_alpaca_evol_instruct
- lksy/ru_instruct_gpt4
language:
- ru
license: apache-2.0
---
Merged version of https://huggingface.co/IlyaGusev/saiga_mistral_7b_lora.
My GGUF version: https://huggingface.co/IlyaGusev/saiga_mistral_7b_gguf
TheBloke quantizations:
- https://huggingface.co/TheBloke/saiga_mistral_7b-GGUF
- https://huggingface.co/TheBloke/saiga_mistral_7b-AWQ
- https://huggingface.co/TheBloke/saiga_mistral_7b-GPTQ |
MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1 | MaziyarPanahi | 2024-04-21T12:24:41Z | 5,017 | 1 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-21T11:39:11Z | ---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
inference:
parameters:
temperature: 0.5
widget:
- messages:
- role: user
content: What is your favorite condiment?
---
# Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Instruction format
This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
As reference, here is the pseudo-code used to tokenize instructions during fine-tuning:
```python
def tokenize(text):
return tok.encode(text, add_special_tokens=False)
[BOS_ID] +
tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_1) + [EOS_ID] +
…
tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_N) + [EOS_ID]
```
In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space.
In the Transformers library, one can use [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating) which make sure the right format is applied.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
text = "Hello my name is"
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Limitations
The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. |
osiria/bert-italian-cased-ner | osiria | 2023-06-20T22:30:14Z | 5,015 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"it",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-06-03T00:17:44Z | ---
license: apache-2.0
language:
- it
widget:
- text: "Mi chiamo Marco Rossi, vivo a Roma e lavoro per l'Agenzia Spaziale Italiana"
example_title: "Example 1"
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> Task: Named Entity Recognition</span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: BERT</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>BERT</b> <b>[1]</b> cased model for the <b>Italian</b> language, fine-tuned for <b>Named Entity Recognition</b> (<b>Person</b>, <b>Location</b>, <b>Organization</b> and <b>Miscellanea</b> classes) on the [WikiNER](https://figshare.com/articles/dataset/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) dataset <b>[2]</b>, using <b>BERT-ITALIAN</b> ([bert-base-italian-cased](https://huggingface.co/osiria/bert-base-italian-cased)) as a pre-trained model.
This is a cased, base size BERT model. If you are looking for a lighter (but slightly less accurate) cased model, you can refer to: https://huggingface.co/osiria/distilbert-italian-cased-ner
If you are looking for an uncased model, you can refer to: https://huggingface.co/osiria/bert-italian-uncased-ner
<h3>Training and Performances</h3>
The model is trained to perform entity recognition over 4 classes: <b>PER</b> (persons), <b>LOC</b> (locations), <b>ORG</b> (organizations), <b>MISC</b> (miscellanea, mainly events, products and services). It has been fine-tuned for Named Entity Recognition, using the WikiNER Italian dataset plus an additional custom dataset of manually annotated Wikipedia paragraphs.
The WikiNER dataset has been splitted in 102.352 training instances and 25.588 test instances, and the model has been trained for 1 epoch with a constant learning rate of 1e-5.
The performances on the test set are reported in the following table:
| Recall | Precision | F1 |
| ------ | ------ | ------ |
| 93.35 | 92.22 | 92.78 |
The metrics have been computed at the token level and then macro-averaged over the 4 classes.
Then, since WikiNER is an automatically annotated (silver standard) dataset, which sometimes contains imperfect annotations, an additional fine-tuning on ~3.500 manually annotated paragraphs has been performed.
<h3>Quick usage</h3>
```python
from transformers import BertTokenizerFast, BertForTokenClassification
tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-italian-cased-ner")
model = BertForTokenClassification.from_pretrained("osiria/bert-italian-cased-ner")
from transformers import pipeline
ner = pipeline("ner", model = model, tokenizer = tokenizer, aggregation_strategy="first")
ner("Mi chiamo Marco Rossi, vivo a Roma e lavoro per l'Agenzia Spaziale Italiana nella missione Prisma")
[{'entity_group': 'PER',
'score': 0.99910736,
'word': 'Marco Rossi',
'start': 10,
'end': 21},
{'entity_group': 'LOC',
'score': 0.9973786,
'word': 'Roma',
'start': 30,
'end': 34},
{'entity_group': 'ORG',
'score': 0.9987071,
'word': 'Agenzia Spaziale Italiana',
'start': 50,
'end': 75},
{'entity_group': 'MISC',
'score': 0.9625836,
'word': 'Prisma',
'start': 91,
'end': 97}]
```
You can also try the model online using this web app: https://huggingface.co/spaces/osiria/bert-italian-cased-ner
<h3>References</h3>
[1] https://arxiv.org/abs/1810.04805
[2] https://www.sciencedirect.com/science/article/pii/S0004370212000276
<h3>Limitations</h3>
This model is mainly trained on Wikipedia, so it's particularly suitable for natively digital text from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions
(like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).
<h3>License</h3>
The model is released under <b>Apache-2.0</b> license
|
yulet1de/hentaidiffusion | yulet1de | 2023-05-16T09:35:00Z | 5,014 | 59 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2022-12-05T21:34:12Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
--- |
dkleczek/bert-base-polish-uncased-v1 | dkleczek | 2021-05-19T15:55:32Z | 5,010 | 9 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"pl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: pl
thumbnail: https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png
---
# Polbert - Polish BERT
Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below.
## Cased and uncased variants
* I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model:
* Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly.
* I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus.
* I didn't use Whole Word Masking.
* The cased model improves on the uncased model in the following ways:
* All Polish characters and accents should now be tokenized correctly.
* I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now.
* The model is trained with Whole Word Masking.
## Pre-training corpora
Below is the list of corpora used along with the output of `wc` command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer.
### Uncased
| Tables | Lines | Words | Characters |
| ------------- |--------------:| -----:| -----:|
| [Polish subset of Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 236635408| 1431199601 | 7628097730 |
| [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php) | 8470950 | 176670885 | 1163505275 |
| [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859 | 121154785 | 938896963 |
| [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206 | 132067986 | 1015849191 |
| Total | 262920423 | 1861093257 | 10746349159 |
### Cased
| Tables | Lines | Words | Characters |
| ------------- |--------------:| -----:| -----:|
| [Polish subset of Open Subtitles (Deduplicated) ](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 41998942| 213590656 | 1424873235 |
| [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php) | 8470950 | 176670885 | 1163505275 |
| [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859 | 121154785 | 938896963 |
| [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206 | 132067986 | 1015849191 |
| Total | 68283960 | 646479197 | 4543124667 |
## Pre-training details
### Uncased
* Polbert was trained with code provided in Google BERT's github repository (https://github.com/google-research/bert)
* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)
* Training set-up: in total 1 million training steps:
* 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)
* 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5
* 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5
* The model was trained on a single Google Cloud TPU v3-8
### Cased
* Same approach as uncased model, with the following differences:
* Whole Word Masking
* Training set-up:
* 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)
* 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5
* 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5
## Usage
Polbert is released via [HuggingFace Transformers library](https://huggingface.co/transformers/).
For an example use as language model, see [this notebook](/LM_testing.ipynb) file.
### Uncased
```python
from transformers import *
model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-uncased-v1")
tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-uncased-v1")
nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."):
print(pred)
# Output:
# {'sequence': '[CLS] adam mickiewicz wielkim polskim poeta był. [SEP]', 'score': 0.47196975350379944, 'token': 26596}
# {'sequence': '[CLS] adam mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.09127858281135559, 'token': 10953}
# {'sequence': '[CLS] adam mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.0647173821926117, 'token': 5182}
# {'sequence': '[CLS] adam mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.05232388526201248, 'token': 24293}
# {'sequence': '[CLS] adam mickiewicz wielkim polskim politykiem był. [SEP]', 'score': 0.04554257541894913, 'token': 44095}
```
### Cased
```python
model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-cased-v1")
tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-cased-v1")
nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."):
print(pred)
# Output:
# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.5391148328781128, 'token': 37120}
# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.11683262139558792, 'token': 6810}
# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.06021466106176376, 'token': 17709}
# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim mistrzem był. [SEP]', 'score': 0.051870670169591904, 'token': 14652}
# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim artystą był. [SEP]', 'score': 0.031787533313035965, 'token': 35680}
```
See the next section for an example usage of Polbert in downstream tasks.
## Evaluation
Thanks to Allegro, we now have the [KLEJ benchmark](https://klejbenchmark.com/leaderboard/), a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.
| Model | Average | NKJP-NER | CDSC-E | CDSC-R | CBD | PolEmo2.0-IN | PolEmo2.0-OUT | DYK | PSC | AR |
| ------------- |--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|
| Polbert cased | 81.7 | 93.6 | 93.4 | 93.8 | 52.7 | 87.4 | 71.1 | 59.1 | 98.6 | 85.2 |
| Polbert uncased | 81.4 | 90.1 | 93.9 | 93.5 | 55.0 | 88.1 | 68.8 | 59.4 | 98.8 | 85.4 |
Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here.
## Bias
The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.
## Acknowledgements
* I'd like to express my gratitude to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you!
* Also appreciate the help from Timo Möller from [deepset](https://deepset.ai) for sharing tips and scripts based on their experience training German BERT model.
* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization.
* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from [fastai](https://www.fast.ai) for their NLP and Deep Learning courses!
## Author
Darek Kłeczek - contact me on Twitter [@dk21](https://twitter.com/dk21)
## References
* https://github.com/google-research/bert
* https://github.com/narusemotoki/srx_segmenter
* SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: https://raw.githubusercontent.com/languagetool-org/languagetool/master/languagetool-core/src/main/resources/org/languagetool/resource/segment.srx
* [KLEJ benchmark](https://klejbenchmark.com/leaderboard/) |
google/recurrentgemma-2b-it | google | 2024-06-27T14:10:00Z | 5,009 | 99 | transformers | [
"transformers",
"safetensors",
"recurrent_gemma",
"text-generation",
"conversational",
"arxiv:2402.19427",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2103.03874",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:2110.08193",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:1804.06876",
"arxiv:2203.09509",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-08T14:44:58Z | ---
library_name: transformers
license: gemma
extra_gated_heading: Access RecurrentGemma on Hugging Face
extra_gated_prompt: To access RecurrentGemma on Hugging Face, you’re required to review
and agree to Google’s usage license. To do this, please ensure you’re logged-in
to Hugging Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---
# RecurrentGemma Model Card
**Model Page**: [RecurrentGemma]( https://ai.google.dev/gemma/docs/recurrentgemma/model_card)
This model card corresponds to the 2B instruction version of the RecurrentGemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/recurrentgemma-2b).
**Resources and technical documentation:**
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [RecurrentGemma on Kaggle](https://www.kaggle.com/models/google/recurrentgemma)
**Terms of Use:** [Terms](https://www.kaggle.com/models/google/recurrentgemma/license/consent/verify/huggingface?returnModelRepoId=google/recurrentgemma-2b-it)
**Authors:** Google
## Model information
## Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install --upgrade git+https://github.com/huggingface/transformers.git, then copy the snippet from the section that is relevant for your usecase.
### Running the model on a single / multi GPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/recurrentgemma-2b-it", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
### Chat Template
The instruction-tuned models use a chat template that must be adhered to for conversational use.
The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "google/recurrentgemma-2b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,
)
chat = [
{ "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
At this point, the prompt contains the following text:
```
<bos><start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
```
As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
the `<end_of_turn>` token.
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
chat template.
After the prompt is ready, generation can be performed like this:
```py
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
print(tokenizer.decode(outputs[0]))
```
### Model summary
#### Description
RecurrentGemma is a family of open language models built on a [novel recurrent
architecture](https://arxiv.org/abs/2402.19427) developed at Google. Both
pre-trained and instruction-tuned versions are available in English.
Like Gemma, RecurrentGemma models are well-suited for a variety of text
generation tasks, including question answering, summarization, and reasoning.
Because of its novel architecture, RecurrentGemma requires less memory than
Gemma and achieves faster inference when generating long sequences.
#### Inputs and outputs
* **Input:** Text string (e.g., a question, a prompt, or a document to be
summarized).
* **Output:** Generated English-language text in response to the input (e.g.,
an answer to the question, a summary of the document).
#### Citation
```none
@article{recurrentgemma_2024,
title={RecurrentGemma},
url={},
DOI={},
publisher={Kaggle},
author={Griffin Team, Soham De, Samuel L Smith, Anushan Fernando, Alex Botev, George-Christian Muraru, Ruba Haroun, Leonard Berrada et al.},
year={2024}
}
```
### Model data
#### Training dataset and data processing
RecurrentGemma uses the same training data and data processing as used by the
Gemma model family. A full description can be found on the [Gemma model
card](https://ai.google.dev/gemma/docs/model_card#model_data).
## Implementation information
### Hardware and frameworks used during training
Like
[Gemma](https://ai.google.dev/gemma/docs/model_card#implementation_information),
RecurrentGemma was trained on
[TPUv5e](https://cloud.google.com/tpu/docs/intro-to-tpu?_gl=1*18wi411*_ga*MzE3NDU5OTY1LjE2MzQwNDA4NDY.*_ga_WH2QY8WWF5*MTcxMTA0MjUxMy4xNy4wLjE3MTEwNDI1MTkuMC4wLjA.&_ga=2.239449409.-317459965.1634040846),
using [JAX](https://github.com/google/jax) and [ML
Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/).
## Evaluation information
### Benchmark results
#### Evaluation approach
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Evaluation results
Benchmark | Metric | RecurrentGemma 2B
------------------- | ------------- | -----------------
[MMLU] | 5-shot, top-1 | 38.4
[HellaSwag] | 0-shot | 71.0
[PIQA] | 0-shot | 78.5
[SocialIQA] | 0-shot | 51.8
[BoolQ] | 0-shot | 71.3
[WinoGrande] | partial score | 67.8
[CommonsenseQA] | 7-shot | 63.7
[OpenBookQA] | | 47.2
[ARC-e][ARC-c] | | 72.9
[ARC-c] | | 42.3
[TriviaQA] | 5-shot | 52.5
[Natural Questions] | 5-shot | 11.5
[HumanEval] | pass@1 | 21.3
[MBPP] | 3-shot | 28.8
[GSM8K] | maj@1 | 13.4
[MATH] | 4-shot | 11.0
[AGIEval] | | 23.8
[BIG-Bench] | | 35.3
**Average** | | 44.6
## Ethics and safety
### Ethics and safety evaluations
#### Evaluations approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* **Text-to-text content safety:** Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* **Text-to-text representational harms:** Benchmark against relevant academic
datasets such as WinoBias and BBQ Dataset.
* **Memorization:** Automated evaluation of memorization of training data,
including the risk of personally identifiable information exposure.
* **Large-scale harm:** Tests for “dangerous capabilities,” such as chemical,
biological, radiological, and nuclear (CBRN) risks; as well as tests for
persuasion and deception, cybersecurity, and autonomous replication.
#### Evaluation results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal
policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11)
for categories such as child safety, content safety, representational harms,
memorization, large-scale harms. On top of robust internal evaluations, the
results of well known safety benchmarks like BBQ, Winogender, Winobias,
RealToxicity, and TruthfulQA are shown here.
Benchmark | Metric | RecurrentGemma 2B | RecurrentGemma 2B IT
------------------------ | ------ | ----------------- | --------------------
[RealToxicity] | avg | 9.8 | 7.6
[BOLD] | | 39.3 | 52.4
[CrowS-Pairs] | top-1 | 41.1 | 43.4
[BBQ Ambig][BBQ] | top-1 | 62.6 | 71.1
[BBQ Disambig][BBQ] | top-1 | 58.4 | 50.8
[Winogender] | top-1 | 55.1 | 54.7
[TruthfulQA] | | 35.1 | 42.7
[Winobias 1_2][Winobias] | | 58.4 | 56.4
[Winobias 2_2][Winobias] | | 90.0 | 75.4
[Toxigen] | | 56.7 | 50.0
## Model usage and limitations
### Known limitations
These models have certain limitations that users should be aware of:
* **Training data**
* The quality and diversity of the training data significantly influence
the model's capabilities. Biases or gaps in the training data can lead
to limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model
can handle effectively.
* **Context and task complexity**
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
* **Language ambiguity and nuance**
* Natural language is inherently complex. LLMs might struggle to grasp
subtle nuances, sarcasm, or figurative language.
* **Factual accuracy**
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* **Common sense**
* LLMs rely on statistical patterns in language. They might lack the
ability to apply common sense reasoning in certain situations.
### Ethical considerations and risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* **Bias and fairness**
* LLMs trained on large-scale, real-world text data can reflect
socio-cultural biases embedded in the training material. These models
underwent careful scrutiny, input data pre-processing described and
posterior evaluations reported in this card.
* **Misinformation and misuse**
* LLMs can be misused to generate text that is false, misleading, or
harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI
Toolkit](https://ai.google.dev/gemma/responsible).
* **Transparency and accountability**
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and
researchers across the AI ecosystem.
Risks Identified and Mitigations:
* **Perpetuation of biases:** It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* **Generation of harmful content:** Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
* **Misuse for malicious purposes:** Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in our [terms of
use](https://www.kaggle.com/models/google/recurrentgemma/license/consent/verify/huggingface?returnModelRepoId=google/recurrentgemma-2b-it).
* **Privacy violations:** Models were trained on data filtered for removal of
PII (Personally Identifiable Information). Developers are encouraged to
adhere to privacy regulations with privacy-preserving techniques.
## Intended usage
### Application
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* **Content creation and communication**
* **Text generation:** These models can be used to generate creative text
formats like poems, scripts, code, marketing copy, email drafts, etc.
* **Chatbots and conversational AI:** Power conversational interfaces for
customer service, virtual assistants, or interactive applications.
* **Text summarization:** Generate concise summaries of a text corpus,
research papers, or reports.
* **Research and education**
* **Natural Language Processing (NLP) research:** These models can serve
as a foundation for researchers to experiment with NLP techniques,
develop algorithms, and contribute to the advancement of the field.
* **Language Learning Tools:** Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
* **Knowledge Exploration:** Assist researchers in exploring large bodies
of text by generating summaries or answering questions about specific
topics.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
In particular, RecurrentGemma models achieve comparable performance to Gemma
models but are faster during inference and require less memory, especially on
long sequences.
[MMLU]: https://arxiv.org/abs/2009.03300
[HellaSwag]: https://arxiv.org/abs/1905.07830
[PIQA]: https://arxiv.org/abs/1911.11641
[SocialIQA]: https://arxiv.org/abs/1904.09728
[BoolQ]: https://arxiv.org/abs/1905.10044
[winogrande]: https://arxiv.org/abs/1907.10641
[CommonsenseQA]: https://arxiv.org/abs/1811.00937
[OpenBookQA]: https://arxiv.org/abs/1809.02789
[ARC-c]: https://arxiv.org/abs/1911.01547
[TriviaQA]: https://arxiv.org/abs/1705.03551
[Natural Questions]: https://github.com/google-research-datasets/natural-questions
[HumanEval]: https://arxiv.org/abs/2107.03374
[MBPP]: https://arxiv.org/abs/2108.07732
[GSM8K]: https://arxiv.org/abs/2110.14168
[MATH]: https://arxiv.org/abs/2103.03874
[AGIEval]: https://arxiv.org/abs/2304.06364
[BIG-Bench]: https://arxiv.org/abs/2206.04615
[RealToxicity]: https://arxiv.org/abs/2009.11462
[BOLD]: https://arxiv.org/abs/2101.11718
[CrowS-Pairs]: https://aclanthology.org/2020.emnlp-main.154/
[BBQ]: https://arxiv.org/abs/2110.08193v2
[Winogender]: https://arxiv.org/abs/1804.09301
[TruthfulQA]: https://arxiv.org/abs/2109.07958
[winobias]: https://arxiv.org/abs/1804.06876
[Toxigen]: https://arxiv.org/abs/2203.09509
|
SweatyCrayfish/llama-3-8b-quantized | SweatyCrayfish | 2024-04-20T21:18:57Z | 5,009 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"qlora",
"quantization",
"4-bit",
"lora",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-20T20:15:09Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- qlora
- quantization
- 4-bit
- lora
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
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#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you
will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or
others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or
development of activities that present a risk of death or bodily harm to
individuals, including use of Meta Llama 3 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3
related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI
system
Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
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---
# 4-bit Quantized Llama 3 Model
## Description
This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.
## Model Details
- **Model Type**: Transformer-based language model.
- **Quantization**: 4-bit precision.
- **Advantages**:
- **Memory Efficiency**: Reduces memory usage significantly, allowing deployment on devices with limited RAM.
- **Inference Speed**: Accelerates inference times, depending on the hardware's ability to process low-bit computations.
## How to Use
To utilize this model efficiently, follow the steps below:
### Loading the Quantized Model
Load the model with specific parameters to ensure it utilizes 4-bit precision:
```python
from transformers import AutoModelForCausalLM
model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", device_map="auto", load_in_4bit=True)
```
## Adjusting Precision of Components
Adjust the precision of other components, which are by default converted to torch.float16:
```python
import torch
from transformers import AutoModelForCausalLM
model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", load_in_4bit=True, torch_dtype=torch.float32)
print(model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype)
```
## Citation
Original repository and citations:
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
} |
backyardai/Jamet-8B-L3-MK.V-Blackroot-GGUF | backyardai | 2024-06-07T13:42:25Z | 5,008 | 3 | transformers | [
"transformers",
"gguf",
"not-for-all-audiences",
"base_model:Hastagaras/Jamet-8B-L3-MK.V-Blackroot",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T12:10:16Z | ---
license: llama3
library_name: transformers
tags:
- not-for-all-audiences
base_model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot
model_name: Jamet-8B-L3-MK.V-Blackroot-GGUF
quantized_by: brooketh
parameter_count: 8030261248
---
<img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;">
**<p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p>**
<p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p>
<p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p>
***
# Jamet L3 MK.V Blackroot 8B
- **Creator:** [Hastagaras](https://huggingface.co/Hastagaras/)
- **Original:** [Jamet L3 MK.V Blackroot 8B](https://huggingface.co/Hastagaras/Jamet-8B-L3-MK.V-Blackroot)
- **Date Created:** 2024-06-07
- **Trained Context:** 8192 tokens
- **Description:** Llama3-based RP and Storytelling model by Hastagaras, with DPO and LORAs applied to reduce cheerfulness and emoji.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
***
<img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;">
## Backyard AI
- Free, local AI chat application.
- One-click installation on Mac and PC.
- Automatically use GPU for maximum speed.
- Built-in model manager.
- High-quality character hub.
- Zero-config desktop-to-mobile tethering.
Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable.
**Join us on [Discord](https://discord.gg/SyNN2vC9tQ)**
*** |
jinaai/jina-embedding-s-en-v1 | jinaai | 2023-10-13T12:40:43Z | 5,007 | 25 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"t5",
"finetuner",
"mteb",
"feature-extraction",
"sentence-similarity",
"custom_code",
"en",
"dataset:jinaai/negation-dataset",
"arxiv:2307.11224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-07-06T11:37:14Z | ---
pipeline_tag: sentence-similarity
tags:
- finetuner
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
model-index:
- name: jina-embedding-s-en-v1
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.82089552238806
- type: ap
value: 27.100981946230778
- type: f1
value: 58.3354886367184
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 64.282775
- type: ap
value: 60.350688924943796
- type: f1
value: 62.06346948494396
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 30.623999999999995
- type: f1
value: 29.427789186742153
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.119
- type: map_at_10
value: 35.609
- type: map_at_100
value: 36.935
- type: map_at_1000
value: 36.957
- type: map_at_3
value: 31.046000000000003
- type: map_at_5
value: 33.574
- type: mrr_at_1
value: 22.404
- type: mrr_at_10
value: 35.695
- type: mrr_at_100
value: 37.021
- type: mrr_at_1000
value: 37.043
- type: mrr_at_3
value: 31.093
- type: mrr_at_5
value: 33.635999999999996
- type: ndcg_at_1
value: 22.119
- type: ndcg_at_10
value: 43.566
- type: ndcg_at_100
value: 49.370000000000005
- type: ndcg_at_1000
value: 49.901
- type: ndcg_at_3
value: 34.06
- type: ndcg_at_5
value: 38.653999999999996
- type: precision_at_1
value: 22.119
- type: precision_at_10
value: 6.92
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.272000000000002
- type: precision_at_5
value: 10.811
- type: recall_at_1
value: 22.119
- type: recall_at_10
value: 69.203
- type: recall_at_100
value: 95.021
- type: recall_at_1000
value: 99.075
- type: recall_at_3
value: 42.817
- type: recall_at_5
value: 54.054
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 34.1740289109719
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 23.985251383455463
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.24873612289029
- type: mrr
value: 74.65692740623489
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.22415390332444
- type: cos_sim_spearman
value: 82.9591191954711
- type: euclidean_pearson
value: 44.096317524324945
- type: euclidean_spearman
value: 42.95218351391625
- type: manhattan_pearson
value: 44.07766490545065
- type: manhattan_spearman
value: 42.78350497166606
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 74.64285714285714
- type: f1
value: 73.53680835577447
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 28.512813238490164
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 20.942214972649488
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.255999999999997
- type: map_at_10
value: 37.091
- type: map_at_100
value: 38.428000000000004
- type: map_at_1000
value: 38.559
- type: map_at_3
value: 34.073
- type: map_at_5
value: 35.739
- type: mrr_at_1
value: 34.907
- type: mrr_at_10
value: 42.769
- type: mrr_at_100
value: 43.607
- type: mrr_at_1000
value: 43.656
- type: mrr_at_3
value: 39.986
- type: mrr_at_5
value: 41.581
- type: ndcg_at_1
value: 34.907
- type: ndcg_at_10
value: 42.681000000000004
- type: ndcg_at_100
value: 48.213
- type: ndcg_at_1000
value: 50.464
- type: ndcg_at_3
value: 37.813
- type: ndcg_at_5
value: 39.936
- type: precision_at_1
value: 34.907
- type: precision_at_10
value: 7.911
- type: precision_at_100
value: 1.349
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 17.93
- type: precision_at_5
value: 12.732
- type: recall_at_1
value: 28.255999999999997
- type: recall_at_10
value: 53.49699999999999
- type: recall_at_100
value: 77.288
- type: recall_at_1000
value: 91.776
- type: recall_at_3
value: 39.18
- type: recall_at_5
value: 45.365
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.563999999999997
- type: map_at_10
value: 33.913
- type: map_at_100
value: 34.966
- type: map_at_1000
value: 35.104
- type: map_at_3
value: 31.413000000000004
- type: map_at_5
value: 32.854
- type: mrr_at_1
value: 31.72
- type: mrr_at_10
value: 39.391
- type: mrr_at_100
value: 40.02
- type: mrr_at_1000
value: 40.076
- type: mrr_at_3
value: 37.314
- type: mrr_at_5
value: 38.507999999999996
- type: ndcg_at_1
value: 31.72
- type: ndcg_at_10
value: 38.933
- type: ndcg_at_100
value: 43.024
- type: ndcg_at_1000
value: 45.556999999999995
- type: ndcg_at_3
value: 35.225
- type: ndcg_at_5
value: 36.984
- type: precision_at_1
value: 31.72
- type: precision_at_10
value: 7.248
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 16.943
- type: precision_at_5
value: 11.975
- type: recall_at_1
value: 25.563999999999997
- type: recall_at_10
value: 47.808
- type: recall_at_100
value: 65.182
- type: recall_at_1000
value: 81.831
- type: recall_at_3
value: 36.889
- type: recall_at_5
value: 41.829
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.662
- type: map_at_10
value: 44.096999999999994
- type: map_at_100
value: 45.153999999999996
- type: map_at_1000
value: 45.223
- type: map_at_3
value: 41.377
- type: map_at_5
value: 42.935
- type: mrr_at_1
value: 38.997
- type: mrr_at_10
value: 47.675
- type: mrr_at_100
value: 48.476
- type: mrr_at_1000
value: 48.519
- type: mrr_at_3
value: 45.549
- type: mrr_at_5
value: 46.884
- type: ndcg_at_1
value: 38.997
- type: ndcg_at_10
value: 49.196
- type: ndcg_at_100
value: 53.788000000000004
- type: ndcg_at_1000
value: 55.393
- type: ndcg_at_3
value: 44.67
- type: ndcg_at_5
value: 46.991
- type: precision_at_1
value: 38.997
- type: precision_at_10
value: 7.875
- type: precision_at_100
value: 1.102
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 19.854
- type: precision_at_5
value: 13.605
- type: recall_at_1
value: 33.662
- type: recall_at_10
value: 60.75899999999999
- type: recall_at_100
value: 81.11699999999999
- type: recall_at_1000
value: 92.805
- type: recall_at_3
value: 48.577999999999996
- type: recall_at_5
value: 54.384
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.313
- type: map_at_10
value: 29.036
- type: map_at_100
value: 29.975
- type: map_at_1000
value: 30.063000000000002
- type: map_at_3
value: 26.878999999999998
- type: map_at_5
value: 28.005999999999997
- type: mrr_at_1
value: 23.39
- type: mrr_at_10
value: 31.072
- type: mrr_at_100
value: 31.922
- type: mrr_at_1000
value: 31.995
- type: mrr_at_3
value: 28.908
- type: mrr_at_5
value: 30.104999999999997
- type: ndcg_at_1
value: 23.39
- type: ndcg_at_10
value: 33.448
- type: ndcg_at_100
value: 38.255
- type: ndcg_at_1000
value: 40.542
- type: ndcg_at_3
value: 29.060000000000002
- type: ndcg_at_5
value: 31.023
- type: precision_at_1
value: 23.39
- type: precision_at_10
value: 5.175
- type: precision_at_100
value: 0.8049999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.504999999999999
- type: precision_at_5
value: 8.61
- type: recall_at_1
value: 21.313
- type: recall_at_10
value: 45.345
- type: recall_at_100
value: 67.752
- type: recall_at_1000
value: 84.937
- type: recall_at_3
value: 33.033
- type: recall_at_5
value: 37.929
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.255999999999998
- type: map_at_10
value: 20.339
- type: map_at_100
value: 21.491
- type: map_at_1000
value: 21.616
- type: map_at_3
value: 18.481
- type: map_at_5
value: 19.594
- type: mrr_at_1
value: 17.413
- type: mrr_at_10
value: 24.146
- type: mrr_at_100
value: 25.188
- type: mrr_at_1000
value: 25.273
- type: mrr_at_3
value: 22.264
- type: mrr_at_5
value: 23.302
- type: ndcg_at_1
value: 17.413
- type: ndcg_at_10
value: 24.272
- type: ndcg_at_100
value: 29.82
- type: ndcg_at_1000
value: 33.072
- type: ndcg_at_3
value: 20.826
- type: ndcg_at_5
value: 22.535
- type: precision_at_1
value: 17.413
- type: precision_at_10
value: 4.366
- type: precision_at_100
value: 0.818
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 9.866999999999999
- type: precision_at_5
value: 7.164
- type: recall_at_1
value: 14.255999999999998
- type: recall_at_10
value: 32.497
- type: recall_at_100
value: 56.592
- type: recall_at_1000
value: 80.17699999999999
- type: recall_at_3
value: 23.195
- type: recall_at_5
value: 27.392
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.709
- type: map_at_10
value: 31.377
- type: map_at_100
value: 32.536
- type: map_at_1000
value: 32.669
- type: map_at_3
value: 28.572999999999997
- type: map_at_5
value: 30.205
- type: mrr_at_1
value: 27.815
- type: mrr_at_10
value: 36.452
- type: mrr_at_100
value: 37.302
- type: mrr_at_1000
value: 37.364000000000004
- type: mrr_at_3
value: 33.75
- type: mrr_at_5
value: 35.43
- type: ndcg_at_1
value: 27.815
- type: ndcg_at_10
value: 36.84
- type: ndcg_at_100
value: 42.092
- type: ndcg_at_1000
value: 44.727
- type: ndcg_at_3
value: 31.964
- type: ndcg_at_5
value: 34.428
- type: precision_at_1
value: 27.815
- type: precision_at_10
value: 6.67
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 14.982000000000001
- type: precision_at_5
value: 10.857
- type: recall_at_1
value: 22.709
- type: recall_at_10
value: 48.308
- type: recall_at_100
value: 70.866
- type: recall_at_1000
value: 88.236
- type: recall_at_3
value: 34.709
- type: recall_at_5
value: 40.996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.348000000000003
- type: map_at_10
value: 29.427999999999997
- type: map_at_100
value: 30.499
- type: map_at_1000
value: 30.631999999999998
- type: map_at_3
value: 27.035999999999998
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 27.74
- type: mrr_at_10
value: 34.424
- type: mrr_at_100
value: 35.341
- type: mrr_at_1000
value: 35.419
- type: mrr_at_3
value: 32.401
- type: mrr_at_5
value: 33.497
- type: ndcg_at_1
value: 27.74
- type: ndcg_at_10
value: 34.136
- type: ndcg_at_100
value: 39.269
- type: ndcg_at_1000
value: 42.263
- type: ndcg_at_3
value: 30.171999999999997
- type: ndcg_at_5
value: 31.956
- type: precision_at_1
value: 27.74
- type: precision_at_10
value: 6.062
- type: precision_at_100
value: 1.014
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 14.079
- type: precision_at_5
value: 9.977
- type: recall_at_1
value: 22.348000000000003
- type: recall_at_10
value: 43.477
- type: recall_at_100
value: 65.945
- type: recall_at_1000
value: 86.587
- type: recall_at_3
value: 32.107
- type: recall_at_5
value: 36.974000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.688499999999998
- type: map_at_10
value: 29.164666666666665
- type: map_at_100
value: 30.22575
- type: map_at_1000
value: 30.350833333333334
- type: map_at_3
value: 26.82025
- type: map_at_5
value: 28.14966666666667
- type: mrr_at_1
value: 25.779249999999998
- type: mrr_at_10
value: 32.969
- type: mrr_at_100
value: 33.81725
- type: mrr_at_1000
value: 33.88825
- type: mrr_at_3
value: 30.831250000000004
- type: mrr_at_5
value: 32.065000000000005
- type: ndcg_at_1
value: 25.779249999999998
- type: ndcg_at_10
value: 33.73675
- type: ndcg_at_100
value: 38.635666666666665
- type: ndcg_at_1000
value: 41.353500000000004
- type: ndcg_at_3
value: 29.66283333333333
- type: ndcg_at_5
value: 31.607249999999997
- type: precision_at_1
value: 25.779249999999998
- type: precision_at_10
value: 5.861416666666667
- type: precision_at_100
value: 0.9852500000000002
- type: precision_at_1000
value: 0.14108333333333334
- type: precision_at_3
value: 13.563583333333332
- type: precision_at_5
value: 9.630333333333335
- type: recall_at_1
value: 21.688499999999998
- type: recall_at_10
value: 43.605
- type: recall_at_100
value: 65.52366666666667
- type: recall_at_1000
value: 84.69683333333332
- type: recall_at_3
value: 32.195499999999996
- type: recall_at_5
value: 37.25325
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.279
- type: map_at_10
value: 23.238
- type: map_at_100
value: 24.026
- type: map_at_1000
value: 24.13
- type: map_at_3
value: 20.730999999999998
- type: map_at_5
value: 22.278000000000002
- type: mrr_at_1
value: 19.017999999999997
- type: mrr_at_10
value: 25.188
- type: mrr_at_100
value: 25.918999999999997
- type: mrr_at_1000
value: 25.996999999999996
- type: mrr_at_3
value: 22.776
- type: mrr_at_5
value: 24.256
- type: ndcg_at_1
value: 19.017999999999997
- type: ndcg_at_10
value: 27.171
- type: ndcg_at_100
value: 31.274
- type: ndcg_at_1000
value: 34.016000000000005
- type: ndcg_at_3
value: 22.442
- type: ndcg_at_5
value: 24.955
- type: precision_at_1
value: 19.017999999999997
- type: precision_at_10
value: 4.494
- type: precision_at_100
value: 0.712
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 9.611
- type: precision_at_5
value: 7.331
- type: recall_at_1
value: 17.279
- type: recall_at_10
value: 37.464999999999996
- type: recall_at_100
value: 56.458
- type: recall_at_1000
value: 76.759
- type: recall_at_3
value: 24.659
- type: recall_at_5
value: 30.672
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.901
- type: map_at_10
value: 20.268
- type: map_at_100
value: 21.143
- type: map_at_1000
value: 21.264
- type: map_at_3
value: 18.557000000000002
- type: map_at_5
value: 19.483
- type: mrr_at_1
value: 17.997
- type: mrr_at_10
value: 23.591
- type: mrr_at_100
value: 24.387
- type: mrr_at_1000
value: 24.471
- type: mrr_at_3
value: 21.874
- type: mrr_at_5
value: 22.797
- type: ndcg_at_1
value: 17.997
- type: ndcg_at_10
value: 23.87
- type: ndcg_at_100
value: 28.459
- type: ndcg_at_1000
value: 31.66
- type: ndcg_at_3
value: 20.779
- type: ndcg_at_5
value: 22.137
- type: precision_at_1
value: 17.997
- type: precision_at_10
value: 4.25
- type: precision_at_100
value: 0.761
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 9.716
- type: precision_at_5
value: 6.909999999999999
- type: recall_at_1
value: 14.901
- type: recall_at_10
value: 31.44
- type: recall_at_100
value: 52.717000000000006
- type: recall_at_1000
value: 76.102
- type: recall_at_3
value: 22.675
- type: recall_at_5
value: 26.336
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.52
- type: map_at_10
value: 28.397
- type: map_at_100
value: 29.443
- type: map_at_1000
value: 29.56
- type: map_at_3
value: 26.501
- type: map_at_5
value: 27.375
- type: mrr_at_1
value: 25.28
- type: mrr_at_10
value: 32.102000000000004
- type: mrr_at_100
value: 33.005
- type: mrr_at_1000
value: 33.084
- type: mrr_at_3
value: 30.208000000000002
- type: mrr_at_5
value: 31.146
- type: ndcg_at_1
value: 25.28
- type: ndcg_at_10
value: 32.635
- type: ndcg_at_100
value: 37.672
- type: ndcg_at_1000
value: 40.602
- type: ndcg_at_3
value: 28.951999999999998
- type: ndcg_at_5
value: 30.336999999999996
- type: precision_at_1
value: 25.28
- type: precision_at_10
value: 5.3260000000000005
- type: precision_at_100
value: 0.8840000000000001
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 12.687000000000001
- type: precision_at_5
value: 8.638
- type: recall_at_1
value: 21.52
- type: recall_at_10
value: 41.955
- type: recall_at_100
value: 64.21
- type: recall_at_1000
value: 85.28099999999999
- type: recall_at_3
value: 31.979999999999997
- type: recall_at_5
value: 35.406
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.296
- type: map_at_10
value: 28.449999999999996
- type: map_at_100
value: 29.847
- type: map_at_1000
value: 30.073
- type: map_at_3
value: 25.995
- type: map_at_5
value: 27.603
- type: mrr_at_1
value: 25.296000000000003
- type: mrr_at_10
value: 32.751999999999995
- type: mrr_at_100
value: 33.705
- type: mrr_at_1000
value: 33.783
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 32.006
- type: ndcg_at_1
value: 25.296000000000003
- type: ndcg_at_10
value: 33.555
- type: ndcg_at_100
value: 38.891999999999996
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 29.944
- type: ndcg_at_5
value: 31.997999999999998
- type: precision_at_1
value: 25.296000000000003
- type: precision_at_10
value: 6.542000000000001
- type: precision_at_100
value: 1.354
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.593
- type: recall_at_1
value: 20.296
- type: recall_at_10
value: 42.742000000000004
- type: recall_at_100
value: 67.351
- type: recall_at_1000
value: 88.774
- type: recall_at_3
value: 32.117000000000004
- type: recall_at_5
value: 37.788
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.157999999999998
- type: map_at_10
value: 24.342
- type: map_at_100
value: 25.201
- type: map_at_1000
value: 25.317
- type: map_at_3
value: 22.227
- type: map_at_5
value: 23.372999999999998
- type: mrr_at_1
value: 19.778000000000002
- type: mrr_at_10
value: 26.066
- type: mrr_at_100
value: 26.935
- type: mrr_at_1000
value: 27.022000000000002
- type: mrr_at_3
value: 24.214
- type: mrr_at_5
value: 25.268
- type: ndcg_at_1
value: 19.778000000000002
- type: ndcg_at_10
value: 28.104000000000003
- type: ndcg_at_100
value: 32.87
- type: ndcg_at_1000
value: 35.858000000000004
- type: ndcg_at_3
value: 24.107
- type: ndcg_at_5
value: 26.007
- type: precision_at_1
value: 19.778000000000002
- type: precision_at_10
value: 4.417999999999999
- type: precision_at_100
value: 0.739
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.228
- type: precision_at_5
value: 7.172000000000001
- type: recall_at_1
value: 18.157999999999998
- type: recall_at_10
value: 37.967
- type: recall_at_100
value: 60.806000000000004
- type: recall_at_1000
value: 83.097
- type: recall_at_3
value: 27.223999999999997
- type: recall_at_5
value: 31.968000000000004
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.055
- type: map_at_10
value: 11.609
- type: map_at_100
value: 12.83
- type: map_at_1000
value: 12.995000000000001
- type: map_at_3
value: 9.673
- type: map_at_5
value: 10.761999999999999
- type: mrr_at_1
value: 15.309000000000001
- type: mrr_at_10
value: 23.655
- type: mrr_at_100
value: 24.785
- type: mrr_at_1000
value: 24.856
- type: mrr_at_3
value: 20.499000000000002
- type: mrr_at_5
value: 22.425
- type: ndcg_at_1
value: 15.309000000000001
- type: ndcg_at_10
value: 17.252000000000002
- type: ndcg_at_100
value: 22.976
- type: ndcg_at_1000
value: 26.480999999999998
- type: ndcg_at_3
value: 13.418
- type: ndcg_at_5
value: 15.084
- type: precision_at_1
value: 15.309000000000001
- type: precision_at_10
value: 5.309
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.17600000000000002
- type: precision_at_3
value: 9.62
- type: precision_at_5
value: 7.883
- type: recall_at_1
value: 7.055
- type: recall_at_10
value: 21.891
- type: recall_at_100
value: 41.979
- type: recall_at_1000
value: 62.239999999999995
- type: recall_at_3
value: 12.722
- type: recall_at_5
value: 16.81
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.909
- type: map_at_10
value: 12.844
- type: map_at_100
value: 16.435
- type: map_at_1000
value: 17.262
- type: map_at_3
value: 10.131
- type: map_at_5
value: 11.269
- type: mrr_at_1
value: 54.50000000000001
- type: mrr_at_10
value: 62.202
- type: mrr_at_100
value: 62.81
- type: mrr_at_1000
value: 62.824000000000005
- type: mrr_at_3
value: 60.5
- type: mrr_at_5
value: 61.324999999999996
- type: ndcg_at_1
value: 42.125
- type: ndcg_at_10
value: 28.284
- type: ndcg_at_100
value: 30.444
- type: ndcg_at_1000
value: 36.397
- type: ndcg_at_3
value: 33.439
- type: ndcg_at_5
value: 30.473
- type: precision_at_1
value: 54.50000000000001
- type: precision_at_10
value: 21.4
- type: precision_at_100
value: 6.192
- type: precision_at_1000
value: 1.398
- type: precision_at_3
value: 36.583
- type: precision_at_5
value: 28.799999999999997
- type: recall_at_1
value: 6.909
- type: recall_at_10
value: 17.296
- type: recall_at_100
value: 33.925
- type: recall_at_1000
value: 53.786
- type: recall_at_3
value: 11.333
- type: recall_at_5
value: 13.529
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 36.08
- type: f1
value: 33.016420191943766
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.605000000000004
- type: map_at_10
value: 63.31400000000001
- type: map_at_100
value: 63.678000000000004
- type: map_at_1000
value: 63.699
- type: map_at_3
value: 61.141
- type: map_at_5
value: 62.517999999999994
- type: mrr_at_1
value: 56.871
- type: mrr_at_10
value: 67.915
- type: mrr_at_100
value: 68.24900000000001
- type: mrr_at_1000
value: 68.262
- type: mrr_at_3
value: 65.809
- type: mrr_at_5
value: 67.171
- type: ndcg_at_1
value: 56.871
- type: ndcg_at_10
value: 69.122
- type: ndcg_at_100
value: 70.855
- type: ndcg_at_1000
value: 71.368
- type: ndcg_at_3
value: 64.974
- type: ndcg_at_5
value: 67.318
- type: precision_at_1
value: 56.871
- type: precision_at_10
value: 9.029
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 25.893
- type: precision_at_5
value: 16.838
- type: recall_at_1
value: 52.605000000000004
- type: recall_at_10
value: 82.679
- type: recall_at_100
value: 90.586
- type: recall_at_1000
value: 94.38
- type: recall_at_3
value: 71.447
- type: recall_at_5
value: 77.218
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.759
- type: map_at_10
value: 18.877
- type: map_at_100
value: 20.498
- type: map_at_1000
value: 20.682000000000002
- type: map_at_3
value: 16.159000000000002
- type: map_at_5
value: 17.575
- type: mrr_at_1
value: 22.531000000000002
- type: mrr_at_10
value: 31.155
- type: mrr_at_100
value: 32.188
- type: mrr_at_1000
value: 32.245000000000005
- type: mrr_at_3
value: 28.781000000000002
- type: mrr_at_5
value: 30.054
- type: ndcg_at_1
value: 22.531000000000002
- type: ndcg_at_10
value: 25.189
- type: ndcg_at_100
value: 31.958
- type: ndcg_at_1000
value: 35.693999999999996
- type: ndcg_at_3
value: 22.235
- type: ndcg_at_5
value: 23.044999999999998
- type: precision_at_1
value: 22.531000000000002
- type: precision_at_10
value: 7.438000000000001
- type: precision_at_100
value: 1.418
- type: precision_at_1000
value: 0.208
- type: precision_at_3
value: 15.329
- type: precision_at_5
value: 11.451
- type: recall_at_1
value: 10.759
- type: recall_at_10
value: 31.416
- type: recall_at_100
value: 56.989000000000004
- type: recall_at_1000
value: 80.33200000000001
- type: recall_at_3
value: 20.61
- type: recall_at_5
value: 24.903
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.21
- type: map_at_10
value: 38.765
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.568
- type: map_at_3
value: 36.699
- type: map_at_5
value: 37.925
- type: mrr_at_1
value: 58.42
- type: mrr_at_10
value: 65.137
- type: mrr_at_100
value: 65.542
- type: mrr_at_1000
value: 65.568
- type: mrr_at_3
value: 63.698
- type: mrr_at_5
value: 64.575
- type: ndcg_at_1
value: 58.42
- type: ndcg_at_10
value: 47.476
- type: ndcg_at_100
value: 50.466
- type: ndcg_at_1000
value: 52.064
- type: ndcg_at_3
value: 43.986
- type: ndcg_at_5
value: 45.824
- type: precision_at_1
value: 58.42
- type: precision_at_10
value: 9.649000000000001
- type: precision_at_100
value: 1.201
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 26.977
- type: precision_at_5
value: 17.642
- type: recall_at_1
value: 29.21
- type: recall_at_10
value: 48.244
- type: recall_at_100
value: 60.041
- type: recall_at_1000
value: 70.743
- type: recall_at_3
value: 40.466
- type: recall_at_5
value: 44.105
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 58.7064
- type: ap
value: 55.36326227125519
- type: f1
value: 57.46763115215848
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 15.889000000000001
- type: map_at_10
value: 25.979000000000003
- type: map_at_100
value: 27.21
- type: map_at_1000
value: 27.284000000000002
- type: map_at_3
value: 22.665
- type: map_at_5
value: 24.578
- type: mrr_at_1
value: 16.39
- type: mrr_at_10
value: 26.504
- type: mrr_at_100
value: 27.689999999999998
- type: mrr_at_1000
value: 27.758
- type: mrr_at_3
value: 23.24
- type: mrr_at_5
value: 25.108000000000004
- type: ndcg_at_1
value: 16.39
- type: ndcg_at_10
value: 31.799
- type: ndcg_at_100
value: 38.034
- type: ndcg_at_1000
value: 39.979
- type: ndcg_at_3
value: 25.054
- type: ndcg_at_5
value: 28.463
- type: precision_at_1
value: 16.39
- type: precision_at_10
value: 5.189
- type: precision_at_100
value: 0.835
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 10.84
- type: precision_at_5
value: 8.238
- type: recall_at_1
value: 15.889000000000001
- type: recall_at_10
value: 49.739
- type: recall_at_100
value: 79.251
- type: recall_at_1000
value: 94.298
- type: recall_at_3
value: 31.427
- type: recall_at_5
value: 39.623000000000005
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.81668946648426
- type: f1
value: 88.55200075528438
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 58.611491108071135
- type: f1
value: 42.12391403999353
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.67047747141896
- type: f1
value: 62.88410885922258
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.78547410894419
- type: f1
value: 71.69467869218154
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 27.23799937752035
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 23.26502601343789
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.680711484149832
- type: mrr
value: 31.705059795117307
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.077
- type: map_at_10
value: 8.657
- type: map_at_100
value: 10.753
- type: map_at_1000
value: 11.885
- type: map_at_3
value: 6.5089999999999995
- type: map_at_5
value: 7.405
- type: mrr_at_1
value: 38.7
- type: mrr_at_10
value: 46.065
- type: mrr_at_100
value: 46.772000000000006
- type: mrr_at_1000
value: 46.83
- type: mrr_at_3
value: 44.118
- type: mrr_at_5
value: 45.015
- type: ndcg_at_1
value: 36.997
- type: ndcg_at_10
value: 25.96
- type: ndcg_at_100
value: 23.607
- type: ndcg_at_1000
value: 32.317
- type: ndcg_at_3
value: 31.06
- type: ndcg_at_5
value: 28.921000000000003
- type: precision_at_1
value: 38.7
- type: precision_at_10
value: 19.195
- type: precision_at_100
value: 6.164
- type: precision_at_1000
value: 1.839
- type: precision_at_3
value: 28.999000000000002
- type: precision_at_5
value: 25.014999999999997
- type: recall_at_1
value: 4.077
- type: recall_at_10
value: 11.802
- type: recall_at_100
value: 24.365000000000002
- type: recall_at_1000
value: 55.277
- type: recall_at_3
value: 7.435
- type: recall_at_5
value: 8.713999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.588
- type: map_at_10
value: 32.08
- type: map_at_100
value: 33.32
- type: map_at_1000
value: 33.377
- type: map_at_3
value: 28.166000000000004
- type: map_at_5
value: 30.383
- type: mrr_at_1
value: 22.161
- type: mrr_at_10
value: 34.121
- type: mrr_at_100
value: 35.171
- type: mrr_at_1000
value: 35.214
- type: mrr_at_3
value: 30.692000000000004
- type: mrr_at_5
value: 32.706
- type: ndcg_at_1
value: 22.131999999999998
- type: ndcg_at_10
value: 38.887
- type: ndcg_at_100
value: 44.433
- type: ndcg_at_1000
value: 45.823
- type: ndcg_at_3
value: 31.35
- type: ndcg_at_5
value: 35.144
- type: precision_at_1
value: 22.131999999999998
- type: precision_at_10
value: 6.8629999999999995
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.706
- type: precision_at_5
value: 10.972999999999999
- type: recall_at_1
value: 19.588
- type: recall_at_10
value: 57.703
- type: recall_at_100
value: 82.194
- type: recall_at_1000
value: 92.623
- type: recall_at_3
value: 38.012
- type: recall_at_5
value: 46.847
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.038
- type: map_at_10
value: 81.572
- type: map_at_100
value: 82.25200000000001
- type: map_at_1000
value: 82.27600000000001
- type: map_at_3
value: 78.618
- type: map_at_5
value: 80.449
- type: mrr_at_1
value: 78.31
- type: mrr_at_10
value: 84.98
- type: mrr_at_100
value: 85.122
- type: mrr_at_1000
value: 85.124
- type: mrr_at_3
value: 83.852
- type: mrr_at_5
value: 84.6
- type: ndcg_at_1
value: 78.31
- type: ndcg_at_10
value: 85.693
- type: ndcg_at_100
value: 87.191
- type: ndcg_at_1000
value: 87.386
- type: ndcg_at_3
value: 82.585
- type: ndcg_at_5
value: 84.255
- type: precision_at_1
value: 78.31
- type: precision_at_10
value: 12.986
- type: precision_at_100
value: 1.505
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.007
- type: precision_at_5
value: 23.735999999999997
- type: recall_at_1
value: 68.038
- type: recall_at_10
value: 93.598
- type: recall_at_100
value: 98.869
- type: recall_at_1000
value: 99.86500000000001
- type: recall_at_3
value: 84.628
- type: recall_at_5
value: 89.316
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 37.948231664922865
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 49.90597913763894
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.753
- type: map_at_10
value: 8.915
- type: map_at_100
value: 10.374
- type: map_at_1000
value: 10.612
- type: map_at_3
value: 6.577
- type: map_at_5
value: 7.8
- type: mrr_at_1
value: 18.4
- type: mrr_at_10
value: 27.325
- type: mrr_at_100
value: 28.419
- type: mrr_at_1000
value: 28.494000000000003
- type: mrr_at_3
value: 24.349999999999998
- type: mrr_at_5
value: 26.205000000000002
- type: ndcg_at_1
value: 18.4
- type: ndcg_at_10
value: 15.293000000000001
- type: ndcg_at_100
value: 21.592
- type: ndcg_at_1000
value: 26.473000000000003
- type: ndcg_at_3
value: 14.748
- type: ndcg_at_5
value: 12.98
- type: precision_at_1
value: 18.4
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.693
- type: precision_at_1000
value: 0.28800000000000003
- type: precision_at_3
value: 13.700000000000001
- type: precision_at_5
value: 11.379999999999999
- type: recall_at_1
value: 3.753
- type: recall_at_10
value: 15.806999999999999
- type: recall_at_100
value: 34.37
- type: recall_at_1000
value: 58.463
- type: recall_at_3
value: 8.338
- type: recall_at_5
value: 11.538
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.58843987639705
- type: cos_sim_spearman
value: 76.33071660715956
- type: euclidean_pearson
value: 72.8029921002978
- type: euclidean_spearman
value: 69.34534284782808
- type: manhattan_pearson
value: 72.49781034973653
- type: manhattan_spearman
value: 69.24754112621694
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.31673079903189
- type: cos_sim_spearman
value: 74.27699263517789
- type: euclidean_pearson
value: 69.4008910999579
- type: euclidean_spearman
value: 59.0716984643048
- type: manhattan_pearson
value: 68.87342686919199
- type: manhattan_spearman
value: 58.904612865335025
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.59122302327788
- type: cos_sim_spearman
value: 78.55383586979005
- type: euclidean_pearson
value: 68.18338642204289
- type: euclidean_spearman
value: 68.95092864180276
- type: manhattan_pearson
value: 68.08807059822706
- type: manhattan_spearman
value: 68.86135938270193
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 78.51766841424501
- type: cos_sim_spearman
value: 73.84318001499558
- type: euclidean_pearson
value: 67.2007138855177
- type: euclidean_spearman
value: 63.98672842723766
- type: manhattan_pearson
value: 67.17773810895949
- type: manhattan_spearman
value: 64.07359154832962
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.73438541570299
- type: cos_sim_spearman
value: 83.71357922283677
- type: euclidean_pearson
value: 57.50131347498546
- type: euclidean_spearman
value: 57.73623619252132
- type: manhattan_pearson
value: 58.082992079000725
- type: manhattan_spearman
value: 58.42728201167522
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 78.14794654172421
- type: cos_sim_spearman
value: 80.025736165043
- type: euclidean_pearson
value: 65.87773913985473
- type: euclidean_spearman
value: 66.69337751784794
- type: manhattan_pearson
value: 66.01039761004415
- type: manhattan_spearman
value: 66.89215027952318
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.10554507136152
- type: cos_sim_spearman
value: 87.4898082140765
- type: euclidean_pearson
value: 72.19391114541367
- type: euclidean_spearman
value: 70.36647944993783
- type: manhattan_pearson
value: 72.18680758133698
- type: manhattan_spearman
value: 70.3871215447305
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.54868111501618
- type: cos_sim_spearman
value: 64.25173617448473
- type: euclidean_pearson
value: 39.116088900637116
- type: euclidean_spearman
value: 53.300772929884
- type: manhattan_pearson
value: 38.3844195287959
- type: manhattan_spearman
value: 52.846675312001246
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 80.04396610550214
- type: cos_sim_spearman
value: 79.19504854997832
- type: euclidean_pearson
value: 66.3284657637072
- type: euclidean_spearman
value: 63.69531796729492
- type: manhattan_pearson
value: 66.82324081038026
- type: manhattan_spearman
value: 64.18254512904923
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 74.16264051781705
- type: mrr
value: 91.80864796060874
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.983000000000004
- type: map_at_10
value: 47.858000000000004
- type: map_at_100
value: 48.695
- type: map_at_1000
value: 48.752
- type: map_at_3
value: 45.444
- type: map_at_5
value: 46.906
- type: mrr_at_1
value: 41.333
- type: mrr_at_10
value: 49.935
- type: mrr_at_100
value: 50.51
- type: mrr_at_1000
value: 50.55500000000001
- type: mrr_at_3
value: 47.833
- type: mrr_at_5
value: 49.117
- type: ndcg_at_1
value: 41.333
- type: ndcg_at_10
value: 52.398999999999994
- type: ndcg_at_100
value: 56.196
- type: ndcg_at_1000
value: 57.838
- type: ndcg_at_3
value: 47.987
- type: ndcg_at_5
value: 50.356
- type: precision_at_1
value: 41.333
- type: precision_at_10
value: 7.167
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 19.0
- type: precision_at_5
value: 12.8
- type: recall_at_1
value: 38.983000000000004
- type: recall_at_10
value: 64.183
- type: recall_at_100
value: 82.02199999999999
- type: recall_at_1000
value: 95.167
- type: recall_at_3
value: 52.383
- type: recall_at_5
value: 58.411
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8019801980198
- type: cos_sim_ap
value: 94.9287554635848
- type: cos_sim_f1
value: 89.83739837398375
- type: cos_sim_precision
value: 91.32231404958677
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.23762376237623
- type: dot_ap
value: 55.22534191245801
- type: dot_f1
value: 54.054054054054056
- type: dot_precision
value: 55.15088449531738
- type: dot_recall
value: 53.0
- type: euclidean_accuracy
value: 99.6108910891089
- type: euclidean_ap
value: 82.5195111329438
- type: euclidean_f1
value: 78.2847718526663
- type: euclidean_precision
value: 86.93528693528694
- type: euclidean_recall
value: 71.2
- type: manhattan_accuracy
value: 99.5970297029703
- type: manhattan_ap
value: 81.96876777875492
- type: manhattan_f1
value: 77.33773377337734
- type: manhattan_precision
value: 85.94132029339853
- type: manhattan_recall
value: 70.3
- type: max_accuracy
value: 99.8019801980198
- type: max_ap
value: 94.9287554635848
- type: max_f1
value: 89.83739837398375
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 46.34997003954114
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.462336020554893
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 47.1757817459526
- type: mrr
value: 47.941057104660054
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.56106249068471
- type: cos_sim_spearman
value: 31.24613190558528
- type: dot_pearson
value: 20.486610035794257
- type: dot_spearman
value: 23.115667545894546
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.182
- type: map_at_10
value: 1.155
- type: map_at_100
value: 5.118
- type: map_at_1000
value: 11.827
- type: map_at_3
value: 0.482
- type: map_at_5
value: 0.712
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 79.483
- type: mrr_at_100
value: 79.637
- type: mrr_at_1000
value: 79.637
- type: mrr_at_3
value: 77.667
- type: mrr_at_5
value: 78.567
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 52.303
- type: ndcg_at_100
value: 37.361
- type: ndcg_at_1000
value: 32.84
- type: ndcg_at_3
value: 58.274
- type: ndcg_at_5
value: 55.601
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 55.60000000000001
- type: precision_at_100
value: 37.96
- type: precision_at_1000
value: 14.738000000000001
- type: precision_at_3
value: 62.666999999999994
- type: precision_at_5
value: 60.0
- type: recall_at_1
value: 0.182
- type: recall_at_10
value: 1.4120000000000001
- type: recall_at_100
value: 8.533
- type: recall_at_1000
value: 30.572
- type: recall_at_3
value: 0.5309999999999999
- type: recall_at_5
value: 0.814
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.385
- type: map_at_10
value: 7.185999999999999
- type: map_at_100
value: 11.642
- type: map_at_1000
value: 12.953000000000001
- type: map_at_3
value: 3.496
- type: map_at_5
value: 4.82
- type: mrr_at_1
value: 16.326999999999998
- type: mrr_at_10
value: 29.461
- type: mrr_at_100
value: 31.436999999999998
- type: mrr_at_1000
value: 31.436999999999998
- type: mrr_at_3
value: 24.490000000000002
- type: mrr_at_5
value: 27.857
- type: ndcg_at_1
value: 14.285999999999998
- type: ndcg_at_10
value: 16.672
- type: ndcg_at_100
value: 28.691
- type: ndcg_at_1000
value: 39.817
- type: ndcg_at_3
value: 15.277
- type: ndcg_at_5
value: 15.823
- type: precision_at_1
value: 16.326999999999998
- type: precision_at_10
value: 15.509999999999998
- type: precision_at_100
value: 6.49
- type: precision_at_1000
value: 1.4080000000000001
- type: precision_at_3
value: 16.326999999999998
- type: precision_at_5
value: 16.735
- type: recall_at_1
value: 1.385
- type: recall_at_10
value: 12.586
- type: recall_at_100
value: 40.765
- type: recall_at_1000
value: 75.198
- type: recall_at_3
value: 4.326
- type: recall_at_5
value: 7.074999999999999
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 59.4402
- type: ap
value: 10.16922814263879
- type: f1
value: 45.374485104940476
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 54.25863044708545
- type: f1
value: 54.20154252609619
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 34.3883169293051
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 81.76670441676104
- type: cos_sim_ap
value: 59.29878710961347
- type: cos_sim_f1
value: 57.33284971587474
- type: cos_sim_precision
value: 52.9122963624191
- type: cos_sim_recall
value: 62.559366754617415
- type: dot_accuracy
value: 77.52279907015557
- type: dot_ap
value: 34.17588904643467
- type: dot_f1
value: 41.063567529494634
- type: dot_precision
value: 30.813953488372093
- type: dot_recall
value: 61.53034300791557
- type: euclidean_accuracy
value: 80.61631996185254
- type: euclidean_ap
value: 54.00362361479352
- type: euclidean_f1
value: 53.99111751290361
- type: euclidean_precision
value: 49.52653600528518
- type: euclidean_recall
value: 59.340369393139845
- type: manhattan_accuracy
value: 80.65208320915539
- type: manhattan_ap
value: 54.18329507159467
- type: manhattan_f1
value: 53.85550960836779
- type: manhattan_precision
value: 49.954873646209386
- type: manhattan_recall
value: 58.41688654353562
- type: max_accuracy
value: 81.76670441676104
- type: max_ap
value: 59.29878710961347
- type: max_f1
value: 57.33284971587474
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.99433383785463
- type: cos_sim_ap
value: 83.43513915159009
- type: cos_sim_f1
value: 76.3906784964842
- type: cos_sim_precision
value: 73.19223985890653
- type: cos_sim_recall
value: 79.88142901139513
- type: dot_accuracy
value: 81.96142352621571
- type: dot_ap
value: 67.78764755689359
- type: dot_f1
value: 64.42823356983445
- type: dot_precision
value: 56.77801913931779
- type: dot_recall
value: 74.46104096088698
- type: euclidean_accuracy
value: 81.9478402607987
- type: euclidean_ap
value: 67.13958457373279
- type: euclidean_f1
value: 60.45118343195266
- type: euclidean_precision
value: 58.1625391403359
- type: euclidean_recall
value: 62.92731752386819
- type: manhattan_accuracy
value: 82.01769705437188
- type: manhattan_ap
value: 67.24709477497046
- type: manhattan_f1
value: 60.4103846436714
- type: manhattan_precision
value: 57.82063916654935
- type: manhattan_recall
value: 63.24299353249153
- type: max_accuracy
value: 87.99433383785463
- type: max_ap
value: 83.43513915159009
- type: max_f1
value: 76.3906784964842
---
<br><br>
<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
</p>
## Intented Usage & Model Info
`jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
With a compact size of just 35 million parameters,
the model enables lightning-fast inference while still delivering impressive performance.
Additionally, we provide the following options:
- [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters.
- [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters **(you are here)**.
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
- [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters.
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10 times bert-base (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters, 30 times bert-base (soon).
## Data & Parameters
Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).
## Metrics
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:
|Name|param |dimension|
|------------------------------|-----|------|
|all-minilm-l6-v2|23m |384|
|all-mpnet-base-v2 |110m |768|
|ada-embedding-002|Unknown/OpenAI API |1536|
|jina-embedding-t-en-v1|14m |312|
|jina-embedding-s-en-v1|35m |512|
|jina-embedding-b-en-v1|110m |768|
|jina-embedding-l-en-v1|330m |1024|
|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 |
|all-mpnet-base-v2|0.726|**0.835**|0.78 |0.857|0.8 |**0.906**|0.513 |0.875|0.656 |
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** |
|jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482 |0.840|0.522 |
|jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523 |0.857|0.524 |
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606 |0.876|0.594 |
|jina-embedding-l-en-v1|0.745|0.832|**0.781**|**0.869**|0.837|0.902|0.573 |**0.881**|0.598 |
## Usage
Use with Jina AI Finetuner
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-s-en-v1')
embeddings = finetuner.encode(
model=model,
data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['how is the weather today', 'What is the current weather like today?']
model = SentenceTransformer('jinaai/jina-embedding-s-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
## Fine-tuning
Please consider [Finetuner](https://github.com/jina-ai/finetuner).
## Plans
1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length.
2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
``` latex
@misc{günther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Cohere/Cohere-embed-multilingual-v3.0 | Cohere | 2023-11-07T12:59:44Z | 5,003 | 76 | transformers | [
"transformers",
"mteb",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2023-11-02T09:52:29Z | ---
tags:
- mteb
model-index:
- name: embed-multilingual-v3.0
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.85074626865672
- type: ap
value: 41.53151744002314
- type: f1
value: 71.94656880817726
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.600375
- type: ap
value: 93.57882128753579
- type: f1
value: 95.59945484944305
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.794
- type: f1
value: 48.740439663130985
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 55.105000000000004
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.15653426568874
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.78876256237919
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.12873500780318
- type: mrr
value: 75.87037769863255
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.01183720167818
- type: cos_sim_spearman
value: 85.00916590717613
- type: euclidean_pearson
value: 84.072733561361
- type: euclidean_spearman
value: 85.00916590717613
- type: manhattan_pearson
value: 83.89233507343208
- type: manhattan_spearman
value: 84.87482549674115
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.09415584415584
- type: f1
value: 86.05173549773973
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.49773000165541
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.909633073998876
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 49.481
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 47.449999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 59.227
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 37.729
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 29.673
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 44.278
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 43.218
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 40.63741666666667
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 33.341
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 29.093999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 40.801
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 40.114
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 33.243
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 29.958000000000002
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 41.004000000000005
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.150000000000006
- type: f1
value: 43.69803436468346
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 88.532
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 44.105
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 70.612
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 93.9672
- type: ap
value: 90.72947025321227
- type: f1
value: 93.96271599852622
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 43.447
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.92476060191517
- type: f1
value: 94.69383758972194
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 78.8873689010488
- type: f1
value: 62.537485052253885
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.51244115669132
- type: f1
value: 72.40074466830153
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.00470746469401
- type: f1
value: 79.03758200183096
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.183215937303736
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 33.443759055792135
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.58713095176127
- type: mrr
value: 33.7326038566206
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 36.417
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 63.415
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 88.924
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 58.10997801688676
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 65.02444843766075
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 19.339000000000002
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.61540076033945
- type: cos_sim_spearman
value: 82.1820253476181
- type: euclidean_pearson
value: 83.73901215845989
- type: euclidean_spearman
value: 82.182021064594
- type: manhattan_pearson
value: 83.76685139192031
- type: manhattan_spearman
value: 82.14074705306663
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.62241109228789
- type: cos_sim_spearman
value: 77.62042143066208
- type: euclidean_pearson
value: 82.77237785274072
- type: euclidean_spearman
value: 77.62042142290566
- type: manhattan_pearson
value: 82.70945589621266
- type: manhattan_spearman
value: 77.57245632826351
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.8307075352031
- type: cos_sim_spearman
value: 85.15620774806095
- type: euclidean_pearson
value: 84.21956724564915
- type: euclidean_spearman
value: 85.15620774806095
- type: manhattan_pearson
value: 84.0677597021641
- type: manhattan_spearman
value: 85.02572172855729
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.33749463516592
- type: cos_sim_spearman
value: 80.01967438481185
- type: euclidean_pearson
value: 82.16884494022196
- type: euclidean_spearman
value: 80.01967218194336
- type: manhattan_pearson
value: 81.94431512413773
- type: manhattan_spearman
value: 79.81636247503731
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.2070761097028
- type: cos_sim_spearman
value: 88.92297656560552
- type: euclidean_pearson
value: 87.95961374550303
- type: euclidean_spearman
value: 88.92298798854765
- type: manhattan_pearson
value: 87.85515971478168
- type: manhattan_spearman
value: 88.8100644762342
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.48103354546488
- type: cos_sim_spearman
value: 86.91850928862898
- type: euclidean_pearson
value: 86.06766986527145
- type: euclidean_spearman
value: 86.91850928862898
- type: manhattan_pearson
value: 86.02705585360717
- type: manhattan_spearman
value: 86.86666545434721
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.30267248880148
- type: cos_sim_spearman
value: 90.08752166657892
- type: euclidean_pearson
value: 90.4697525265135
- type: euclidean_spearman
value: 90.08752166657892
- type: manhattan_pearson
value: 90.57174978064741
- type: manhattan_spearman
value: 90.212834942229
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.10616236380835
- type: cos_sim_spearman
value: 66.81483164137016
- type: euclidean_pearson
value: 68.48505128040803
- type: euclidean_spearman
value: 66.81483164137016
- type: manhattan_pearson
value: 68.46133268524885
- type: manhattan_spearman
value: 66.83684227990202
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.12768629069949
- type: cos_sim_spearman
value: 88.78683817318573
- type: euclidean_pearson
value: 88.47603251297261
- type: euclidean_spearman
value: 88.78683817318573
- type: manhattan_pearson
value: 88.46483630890225
- type: manhattan_spearman
value: 88.76593424921617
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.30886658431281
- type: mrr
value: 95.5964251797585
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 70.04599999999999
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87524752475248
- type: cos_sim_ap
value: 96.79160651306724
- type: cos_sim_f1
value: 93.57798165137615
- type: cos_sim_precision
value: 95.42619542619542
- type: cos_sim_recall
value: 91.8
- type: dot_accuracy
value: 99.87524752475248
- type: dot_ap
value: 96.79160651306724
- type: dot_f1
value: 93.57798165137615
- type: dot_precision
value: 95.42619542619542
- type: dot_recall
value: 91.8
- type: euclidean_accuracy
value: 99.87524752475248
- type: euclidean_ap
value: 96.79160651306724
- type: euclidean_f1
value: 93.57798165137615
- type: euclidean_precision
value: 95.42619542619542
- type: euclidean_recall
value: 91.8
- type: manhattan_accuracy
value: 99.87326732673267
- type: manhattan_ap
value: 96.7574606340297
- type: manhattan_f1
value: 93.45603271983639
- type: manhattan_precision
value: 95.60669456066945
- type: manhattan_recall
value: 91.4
- type: max_accuracy
value: 99.87524752475248
- type: max_ap
value: 96.79160651306724
- type: max_f1
value: 93.57798165137615
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.12288811917144
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.22267280169542
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.39780995606098
- type: mrr
value: 53.26826563958916
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.15118979569649
- type: cos_sim_spearman
value: 30.99428921914572
- type: dot_pearson
value: 31.151189338601924
- type: dot_spearman
value: 30.99428921914572
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 83.372
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 32.698
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.1998
- type: ap
value: 14.646205259325157
- type: f1
value: 54.96172518137252
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.176004527447645
- type: f1
value: 62.48549068096645
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.13767789739772
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.38016331882935
- type: cos_sim_ap
value: 75.1635976260804
- type: cos_sim_f1
value: 69.29936305732484
- type: cos_sim_precision
value: 66.99507389162561
- type: cos_sim_recall
value: 71.76781002638522
- type: dot_accuracy
value: 86.38016331882935
- type: dot_ap
value: 75.16359359202374
- type: dot_f1
value: 69.29936305732484
- type: dot_precision
value: 66.99507389162561
- type: dot_recall
value: 71.76781002638522
- type: euclidean_accuracy
value: 86.38016331882935
- type: euclidean_ap
value: 75.16360246558416
- type: euclidean_f1
value: 69.29936305732484
- type: euclidean_precision
value: 66.99507389162561
- type: euclidean_recall
value: 71.76781002638522
- type: manhattan_accuracy
value: 86.27883411813792
- type: manhattan_ap
value: 75.02872038741897
- type: manhattan_f1
value: 69.29256284011403
- type: manhattan_precision
value: 68.07535641547861
- type: manhattan_recall
value: 70.55408970976254
- type: max_accuracy
value: 86.38016331882935
- type: max_ap
value: 75.16360246558416
- type: max_f1
value: 69.29936305732484
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.39729110878255
- type: cos_sim_ap
value: 86.48560260020555
- type: cos_sim_f1
value: 79.35060602690982
- type: cos_sim_precision
value: 76.50632549496105
- type: cos_sim_recall
value: 82.41453649522637
- type: dot_accuracy
value: 89.39729110878255
- type: dot_ap
value: 86.48559829915334
- type: dot_f1
value: 79.35060602690982
- type: dot_precision
value: 76.50632549496105
- type: dot_recall
value: 82.41453649522637
- type: euclidean_accuracy
value: 89.39729110878255
- type: euclidean_ap
value: 86.48559993122497
- type: euclidean_f1
value: 79.35060602690982
- type: euclidean_precision
value: 76.50632549496105
- type: euclidean_recall
value: 82.41453649522637
- type: manhattan_accuracy
value: 89.36042224550782
- type: manhattan_ap
value: 86.47238558562499
- type: manhattan_f1
value: 79.24500641378047
- type: manhattan_precision
value: 75.61726236273344
- type: manhattan_recall
value: 83.23837388358484
- type: max_accuracy
value: 89.39729110878255
- type: max_ap
value: 86.48560260020555
- type: max_f1
value: 79.35060602690982
---
# Cohere embed-multilingual-v3.0
This repository contains the tokenizer for the Cohere `embed-multilingual-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model.
You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments.
## Usage Cohere API
The following code snippet shows the usage of the Cohere API. Install the cohere SDK via:
```
pip install -U cohere
```
Get your free API key on: www.cohere.com
```python
# This snippet shows and example how to use the Cohere Embed V3 models for semantic search.
# Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere
# Get your API key from: www.cohere.com
import cohere
import numpy as np
cohere_key = "{YOUR_COHERE_API_KEY}" #Get your API key from www.cohere.com
co = cohere.Client(cohere_key)
docs = ["The capital of France is Paris",
"PyTorch is a machine learning framework based on the Torch library.",
"The average cat lifespan is between 13-17 years"]
#Encode your documents with input type 'search_document'
doc_emb = co.embed(docs, input_type="search_document", model="embed-multilingual-v3.0").embeddings
doc_emb = np.asarray(doc_emb)
#Encode your query with input type 'search_query'
query = "What is Pytorch"
query_emb = co.embed([query], input_type="search_query", model="embed-multilingual-v3.0").embeddings
query_emb = np.asarray(query_emb)
query_emb.shape
#Compute the dot product between query embedding and document embedding
scores = np.dot(query_emb, doc_emb.T)[0]
#Find the highest scores
max_idx = np.argsort(-scores)
print(f"Query: {query}")
for idx in max_idx:
print(f"Score: {scores[idx]:.2f}")
print(docs[idx])
print("--------")
```
## Usage AWS SageMaker
The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding.
## Usage AWS Bedrock
Soon the model will also be available via AWS Bedrock. Stay tuned
## Private Deployment
You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more.
## Supported Languages
This model was trained on nearly 1B English training pairs and nearly 0.5B Non-English training pairs from 100+ languages.
Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing). |
facebook/hubert-xlarge-ls960-ft | facebook | 2023-06-27T18:52:32Z | 4,999 | 11 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"hubert",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:libri-light",
"dataset:librispeech_asr",
"arxiv:2106.07447",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- libri-light
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: hubert-large-ls960-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.8
---
# Hubert-Extra-Large-Finetuned
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The extra large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
The model is a fine-tuned version of [hubert-xlarge-ll60k](https://huggingface.co/facebook/hubert-xlarge-ll60k).
[Paper](https://arxiv.org/abs/2106.07447)
Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed
**Abstract**
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/hubert .
# Usage
The model can be used for automatic-speech-recognition as follows:
```python
import torch
from transformers import Wav2Vec2Processor, HubertForCTC
from datasets import load_dataset
processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-xlarge-ls960-ft")
model = HubertForCTC.from_pretrained("facebook/hubert-xlarge-ls960-ft")
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])
# ->"A MAN SAID TO THE UNIVERSE SIR I EXIST"
``` |
princeton-nlp/unsup-simcse-roberta-base | princeton-nlp | 2021-06-16T12:12:10Z | 4,998 | 9 | transformers | [
"transformers",
"pytorch",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | Entry not found |
laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg | laion | 2023-04-18T17:43:24Z | 4,996 | 7 | open_clip | [
"open_clip",
"zero-shot-image-classification",
"clip",
"arxiv:2210.08402",
"arxiv:1910.04867",
"license:mit",
"region:us"
] | zero-shot-image-classification | 2023-02-26T20:39:06Z | ---
tags:
- zero-shot-image-classification
- clip
library_tag: open_clip
license: mit
library_name: open_clip
pipeline_tag: zero-shot-image-classification
---
# Model card for CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
5. [Acknowledgements](#acknowledgements)
6. [Citation](#citation)
# Model Details
## Model Description
A series of CLIP ConvNeXt-XXLarge (a custom `timm` ConvNeXt size) models trained on LAION-2B (english), a subset of [LAION-5B](https://arxiv.org/abs/2210.08402), using [OpenCLIP](https://github.com/mlfoundations/open_clip).
| Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) |
| ----- | ------- | ---------- | ------------ | --------- |
| [convnext_xxlarge.laion2b_s34b_b82k-augreg](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg) | LAION-2B | 256x256 | RRC (0.33, 1.0), RE (0.35), SD (0.1) | 79.1 |
| [convnext_xxlarge.laion2b_s34b_b82k-augreg-rewind](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind) | LAION-2B | 256x256 | RRC (0.3, 1.0), RE (0.4), SD (0.1) | 79.3 |
| [convnext_xxlarge.laion2b_s34b_b82k-augreg-soup](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup) | LAION-2B | 256x256 | N/A | 79.4 |
RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only
The core training run was performed in pieces over a period of ~ 2 months. The global batch size for the core run was 81920. The last ~10% of training was re-done at a 95744 global batch size w/ higher LR and aug than original finish. The two were averaged together in a 'soup'. See more details in [Training Details](#training-details).
Goals:
* Push the size of largest convolutional CLIP image tower into the performance range of ViT-g to ViT-G w/ improved image size scaling for downstream use.
Firsts:
* Largest released ConvNeXt model pretrained (847M params w/ 198 GMAC and 125 MActs @ 256x256 for image)
* A non-ViT image tower CLIP model (with no previous image tower pretrain) achieving > 79% ImageNet top-1 zero-shot
The models utilize:
* the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-XXLarge model (`convnext_xxlarge`) as the image tower
* a standard projection at end of image tower
* a text tower with same size (with 1024, heads 16, depth 24) as ViT-H-14 and ViT-g-14 models
The models are trained at 256x256 image resolution. The size of the combined image + text CLIP model is 1.2B params w/ 222 GMAC and 146 MActs. At 256x256, the ConvNext-XXLarge sits just above a ViT-H-14 CLIP configuration in FLOPS and params while being lower in activation counts. It is well under both g-14 and G-14 while being between them in capabilities.
|model |image_size|embed_dim|gmacs |macts |mparams|image_gmacs|image_macts|image_mparams|text_gmacs|text_macts|text_mparams|
|--------------------------|----------|---------|------|------|-------|-----------|-----------|-------------|----------|----------|------------|
|ViT-H-16 |224 |1024 |150.96|122.01|986.26 |127.4 |100.81 |632.23 |23.57 |21.2 |354.03 |
|ViT-H-14 |224 |1024 |190.97|160.61|986.11 |167.4 |139.41 |632.08 |23.57 |21.2 |354.03 |
|ViT-L-14-336 |336 |768 |197.76|278.19|427.94 |191.1 |270.24 |304.29 |6.66 |7.95 |123.65 |
|convnext_xxlarge |256 |1024 |221.66|145.66|1200.58|198.09 |124.45 |846.54 |23.57 |21.2 |354.03 |
|RN50x64 |448 |1024 |276.8 |249.73|623.26 |265.02 |239.13 |420.38 |11.78 |10.6 |202.88 |
|ViT-g-14 |224 |1024 |290.74|213.84|1366.68|267.18 |192.64 |1012.65 |23.57 |21.2 |354.03 |
|convnext_xxlarge_320 |320 |1024 |333.08|215.66|1200.58|309.52 |194.46 |846.54 |23.57 |21.2 |354.03 |
|ViT-H-14-336 |336 |1024 |414.53|428.74|986.52 |390.97 |407.54 |632.49 |23.57 |21.2 |354.03 |
|ViT-bigG-14 |224 |1280 |532.92|310.71|2539.57|483.96 |275.37 |1844.91 |48.96 |35.34 |694.66 |
Model training done by Ross Wightman across both the [stability.ai](https://stability.ai/) cluster and the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below.
# Uses
As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset.
## Direct Use
Zero-shot image classification, image and text retrieval, among others.
## Downstream Use
Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
## Out-of-Scope Use
As per the OpenAI models,
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
# Training Details
## Training Data
This model was trained with LAION-2B -- A 2 billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
## Training Procedure
The main training run was done at global batch size of 81920 for 256 checkpoint intervals of 135.6M samples for a total of ~34B samples seen over training.
Many difficulties w/ both model numerical stability and cluster stability and performance were encountered while training this model. Initial attempts to train with float16 AMP and default adam beta2 resulted in loss spikes and eventually NaN blow ups. `beta2` was reduced to 0.97 which helped, but the loss / zs curves were not tracking as expected. After switching to PyTorch nightlies, it was possible to use bfloat16 + AMP for training (as with rececnt H/14, g/14, and G/14 models), beta2 was returned to 0.98 and metrics improved.
|Checkpoint Interval |Cluster |# GPUs|# Nodes|GPU |local BS|sample/s|sample/s/gpu|precision |adam beta2 |
|--------------------|----------|------|-------|----------|--------|--------|------------|----------|-----------|
|1 - 2 |Stability |1024 |128 |A100 40GB | 80 |37-40k | 36-39 |amp + fp16|0.97 |
|3 - 32 |Stability |512 |64 |A100 80GB | 160 |27-32k | 52-62 |amp + fp16|0.97 |
|33 - 75 |Booster |1024 |256 |A100 40GB | 80 |48k | 47 |amp + fp16|0.97 |
|76 - 165 |Booster |1024 |256 |A100 40GB | 80 |51k | 50 |amp + bf16|0.98 |
|166 - 232 |Stability |320 |40 |A100 80GB | 256 |18-19k | 56-59 |amp + bf16|0.98 |
|233 - 249 |Booster |1024 |256 |A100 40GB | 80 |51k | 50 |amp + bf16|0.98 |
|250 - 256 |Stability |1024 |128 |A100 40GB | 80 |27-31k | 26-30 |amp + bf16|0.98 |
JUWELS Booster has 4x A100 GPU per node w/ 4x HDR-200 IB adapters per node (200Gbit/sec per GPU). Stability setup used was 8x A100 GPU per node w/ 400Gbit/sec EFA networking per node (50 GBit/sec per GPU). Significant variation in training efficiency (throughput per GPU) as observed across the various configurations. The 1024 GPU configurations across both clusters were particularly prone to crashing (or very difficult to get running w/ a 'good' set of GPUs).
A slurm srun command line below for a 128 8-GPU (40GB A100) configuration:
```
srun --cpu_bind=v --accel-bind=gn python -m training.main \
--save-frequency 1 \
--name "xxlarge-2b-81920-bf16" \
--resume "latest" \
--logs "/runs" \
--log-every-n-steps 50 \
--train-data="pipe:aws s3 cp s3://laion5b/laion2B-data/{000000..231349}.tar -" \
--train-num-samples 135646078 \
--dataset-type webdataset \
--warmup 10000 \
--batch-size=80 \
--epochs=256 \
--dataset-resampled \
--aug-cfg use_timm=True scale='(0.33, 1.0)' re_prob=0.35 \
--precision amp_bfloat16 \
--grad-clip-norm 5.0 \
--lr 1e-3 \
--workers=6 \
--beta2 0.98 \
--model "convnext_xxlarge" \
--seed 0 \
--ddp-static-graph \
--local-loss \
--gather-with-grad \
--grad-checkpointing \
--report-to "tensorboard"
```
For the rewind of last 10%, a higher global batch size of 95744 was used w/ a higher LR and slightly increased augmentation strength.
|Checkpoint Interval |Cluster |# GPUs|# Nodes|GPU |local BS|sample/s|sample/s/gpu|precision |adam beta2 |
|--------------------|---------|------|-------|----------|--------|--------|------------|----------|-----------|
|231 - 256 |stability|1088 |136 |A100 40GB | 88 |32-35k | 29-32 |amp + bf16|0.98 |
The slurm srun command line for 136 8-GPU (40GB A100) nodes:
```
srun --cpu_bind=v --accel-bind=gn python -m training.main \
--save-frequency 1 \
--name "xxlarge-2b-81920-r-bf16" \
--resume "latest" \
--logs "/runs" \
--log-every-n-steps 50 \
--train-data="pipe:aws s3 cp s3://laion5b/laion2B-data/{000000..231349}.tar -" \
--train-num-samples 135646078 \
--dataset-type webdataset \
--warmup 10000 \
--batch-size=88 \
--epochs=256 \
--dataset-resampled \
--aug-cfg use_timm=True scale='(0.3, 1.0)' re_prob=0.4 \
--precision amp_bfloat16 \
--grad-clip-norm 5.0 \
--lr 2e-3 \
--workers=6 \
--beta2 0.98 \
--model "convnext_xxlarge" \
--seed 0 \
--ddp-static-graph \
--local-loss \
--gather-with-grad \
--grad-checkpointing \
--report-to "tensorboard"
```
# Evaluation
Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
## Testing Data, Factors & Metrics
### Testing Data
The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
## Results
These models achieve between 79.1 and 79.4 top-1 zero-shot accuracy on ImageNet-1k.
A zoom-in on final 10% w/ rewind:
An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
# Acknowledgements
Acknowledging [stability.ai](https://stability.ai/) and the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC).
# Citation
**BibTeX:**
LAION-5B
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
OpenCLIP software
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
OpenAI CLIP paper
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@Article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
```
```
@InProceedings{pmlr-v162-wortsman22a,
title = {Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time},
author = {Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Ya and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and Schmidt, Ludwig},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {23965--23998},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf},
url = {https://proceedings.mlr.press/v162/wortsman22a.html}
}
``` |
sarpba/teszt | sarpba | 2024-06-28T11:46:00Z | 4,993 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-27T21:01:19Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
It's only an unuseable test modell. Do not download, just if you want to try. If you download and use for anything you accept meta [llama3 license](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE) to!
# Uploaded model
- **Developed by:** sarpba
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
larenspear/Yi-1.5-34B-Chat-Q3_K_L-GGUF | larenspear | 2024-07-01T18:10:04Z | 4,992 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:01-ai/Yi-1.5-34B-Chat",
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T18:08:40Z | ---
base_model: 01-ai/Yi-1.5-34B-Chat
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# larenspear/Yi-1.5-34B-Chat-Q3_K_L-GGUF
This model was converted to GGUF format from [`01-ai/Yi-1.5-34B-Chat`](https://huggingface.co/01-ai/Yi-1.5-34B-Chat) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/01-ai/Yi-1.5-34B-Chat) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo larenspear/Yi-1.5-34B-Chat-Q3_K_L-GGUF --hf-file yi-1.5-34b-chat-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo larenspear/Yi-1.5-34B-Chat-Q3_K_L-GGUF --hf-file yi-1.5-34b-chat-q3_k_l.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo larenspear/Yi-1.5-34B-Chat-Q3_K_L-GGUF --hf-file yi-1.5-34b-chat-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo larenspear/Yi-1.5-34B-Chat-Q3_K_L-GGUF --hf-file yi-1.5-34b-chat-q3_k_l.gguf -c 2048
```
|
codellama/CodeLlama-34b-Python-hf | codellama | 2024-04-12T14:15:25Z | 4,989 | 93 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-08-24T16:57:23Z | ---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 34B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
> [!NOTE]
> This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf).
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
| 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [ ] Infilling.
- [ ] Instructions / chat.
- [x] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Python version of the 34B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide). |
bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF | bartowski | 2024-06-20T17:13:58Z | 4,989 | 3 | transformers | [
"transformers",
"gguf",
"text-generation",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T14:36:19Z | ---
library_name: transformers
tags: []
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of MadWizard-SFT-v2-Mistral-7b-v0.3
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3152">b3152</a> for quantization.
Original model: https://huggingface.co/Lumpen1/MadWizard-SFT-v2-Mistral-7b-v0.3
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|im_start|> system
{system_prompt}<|im_end|>
<|im_start|> user
{prompt}<|im_end|>
<|im_start|> assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0_L.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0_L.gguf) | Q8_0_L | 7.95GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0.gguf) | Q8_0 | 7.70GB | Extremely high quality, generally unneeded but max available quant. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q6_K_L.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q6_K_L.gguf) | Q6_K_L | 6.26GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q6_K.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q6_K.gguf) | Q6_K | 5.94GB | Very high quality, near perfect, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q5_K_L.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q5_K_L.gguf) | Q5_K_L | 5.47GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q5_K_M.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q5_K_M.gguf) | Q5_K_M | 5.13GB | High quality, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q5_K_S.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q5_K_S.gguf) | Q5_K_S | 5.00GB | High quality, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_L.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_L.gguf) | Q4_K_L | 4.72GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_M.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_M.gguf) | Q4_K_M | 4.37GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_S.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_S.gguf) | Q4_K_S | 4.14GB | Slightly lower quality with more space savings, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ4_XS.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ4_XS.gguf) | IQ4_XS | 3.91GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q3_K_L.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q3_K_L.gguf) | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q3_K_M.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q3_K_M.gguf) | Q3_K_M | 3.52GB | Even lower quality. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ3_M.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ3_M.gguf) | IQ3_M | 3.28GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q3_K_S.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q3_K_S.gguf) | Q3_K_S | 3.16GB | Low quality, not recommended. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ3_XS.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ3_XS.gguf) | IQ3_XS | 3.02GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ3_XXS.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ3_XXS.gguf) | IQ3_XXS | 2.83GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-Q2_K.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-Q2_K.gguf) | Q2_K | 2.72GB | Very low quality but surprisingly usable. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ2_M.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ2_M.gguf) | IQ2_M | 2.50GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ2_S.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ2_S.gguf) | IQ2_S | 2.31GB | Very low quality, uses SOTA techniques to be usable. |
| [MadWizard-SFT-v2-Mistral-7b-v0.3-IQ2_XS.gguf](https://huggingface.co/bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF/blob/main/MadWizard-SFT-v2-Mistral-7b-v0.3-IQ2_XS.gguf) | IQ2_XS | 2.20GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF --include "MadWizard-SFT-v2-Mistral-7b-v0.3-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/MadWizard-SFT-v2-Mistral-7b-v0.3-GGUF --include "MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0.gguf/*" --local-dir MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0
```
You can either specify a new local-dir (MadWizard-SFT-v2-Mistral-7b-v0.3-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf | RichardErkhov | 2024-06-05T14:25:01Z | 4,986 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-06-05T12:45:42Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
neural-chat-7b-v3-1-Nebula-v2-7B - GGUF
- Model creator: https://huggingface.co/Weyaxi/
- Original model: https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [neural-chat-7b-v3-1-Nebula-v2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_neural-chat-7b-v3-1-Nebula-v2-7B-gguf/blob/main/neural-chat-7b-v3-1-Nebula-v2-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
datasets:
- garage-bAInd/Open-Platypus
language:
- en
---
# neural-chat-7b-v3-1-Nebula-v2-7B
neural-chat-7b-v3-1-Nebula-v2-7B is a merge of [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) and [PulsarAI/Nebula-v2-7B-Lora](https://huggingface.co/PulsarAI/Nebula-v2-7B-Lora)
|
bartowski/AlchemistCoder-CL-7B-GGUF | bartowski | 2024-06-03T22:26:27Z | 4,984 | 0 | null | [
"gguf",
"code generation",
"text-generation",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-06-03T22:12:19Z | ---
license: apache-2.0
tags:
- code generation
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of AlchemistCoder-CL-7B
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization.
Original model: https://huggingface.co/internlm/AlchemistCoder-CL-7B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
No chat template specified so default is used. This may be incorrect, check original model card for details.
```
<s> [INST] <<SYS>>
{system_prompt}
<</SYS>>
{prompt} [/INST] </s>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [AlchemistCoder-CL-7B-Q8_0.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q8_0.gguf) | Q8_0 | 7.16GB | Extremely high quality, generally unneeded but max available quant. |
| [AlchemistCoder-CL-7B-Q6_K.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q6_K.gguf) | Q6_K | 5.52GB | Very high quality, near perfect, *recommended*. |
| [AlchemistCoder-CL-7B-Q5_K_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q5_K_M.gguf) | Q5_K_M | 4.78GB | High quality, *recommended*. |
| [AlchemistCoder-CL-7B-Q5_K_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q5_K_S.gguf) | Q5_K_S | 4.65GB | High quality, *recommended*. |
| [AlchemistCoder-CL-7B-Q4_K_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q4_K_M.gguf) | Q4_K_M | 4.08GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [AlchemistCoder-CL-7B-Q4_K_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q4_K_S.gguf) | Q4_K_S | 3.85GB | Slightly lower quality with more space savings, *recommended*. |
| [AlchemistCoder-CL-7B-IQ4_XS.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ4_XS.gguf) | IQ4_XS | 3.61GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [AlchemistCoder-CL-7B-Q3_K_L.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q3_K_L.gguf) | Q3_K_L | 3.59GB | Lower quality but usable, good for low RAM availability. |
| [AlchemistCoder-CL-7B-Q3_K_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q3_K_M.gguf) | Q3_K_M | 3.29GB | Even lower quality. |
| [AlchemistCoder-CL-7B-IQ3_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ3_M.gguf) | IQ3_M | 3.11GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [AlchemistCoder-CL-7B-Q3_K_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q3_K_S.gguf) | Q3_K_S | 2.94GB | Low quality, not recommended. |
| [AlchemistCoder-CL-7B-IQ3_XS.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ3_XS.gguf) | IQ3_XS | 2.79GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [AlchemistCoder-CL-7B-IQ3_XXS.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ3_XXS.gguf) | IQ3_XXS | 2.58GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [AlchemistCoder-CL-7B-Q2_K.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-Q2_K.gguf) | Q2_K | 2.53GB | Very low quality but surprisingly usable. |
| [AlchemistCoder-CL-7B-IQ2_M.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ2_M.gguf) | IQ2_M | 2.35GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [AlchemistCoder-CL-7B-IQ2_S.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ2_S.gguf) | IQ2_S | 2.19GB | Very low quality, uses SOTA techniques to be usable. |
| [AlchemistCoder-CL-7B-IQ2_XS.gguf](https://huggingface.co/bartowski/AlchemistCoder-CL-7B-GGUF/blob/main/AlchemistCoder-CL-7B-IQ2_XS.gguf) | IQ2_XS | 2.03GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/AlchemistCoder-CL-7B-GGUF --include "AlchemistCoder-CL-7B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/AlchemistCoder-CL-7B-GGUF --include "AlchemistCoder-CL-7B-Q8_0.gguf/*" --local-dir AlchemistCoder-CL-7B-Q8_0
```
You can either specify a new local-dir (AlchemistCoder-CL-7B-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
mradermacher/Irene-RP-v2-7B_128k-i1-GGUF | mradermacher | 2024-06-09T11:48:42Z | 4,982 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"yanismiraoui/Yarn-Mistral-7b-128k-sharded",
"Virt-io/Irene-RP-v2-7B",
"en",
"base_model:Virt-io/Irene-RP-v2-7B_128k",
"endpoints_compatible",
"region:us"
] | null | 2024-06-09T06:44:32Z | ---
base_model: Virt-io/Irene-RP-v2-7B_128k
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- yanismiraoui/Yarn-Mistral-7b-128k-sharded
- Virt-io/Irene-RP-v2-7B
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Virt-io/Irene-RP-v2-7B_128k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v2-7B_128k-i1-GGUF/resolve/main/Irene-RP-v2-7B_128k.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
bigscience/sgpt-bloom-7b1-msmarco | bigscience | 2024-04-03T12:03:45Z | 4,978 | 42 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bloom",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-08-26T09:34:08Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: sgpt-bloom-7b1-msmarco
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 68.05970149253731
- type: ap
value: 31.640363460776193
- type: f1
value: 62.50025574145796
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 61.34903640256959
- type: ap
value: 75.18797161500426
- type: f1
value: 59.04772570730417
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 67.78110944527737
- type: ap
value: 19.218916023322706
- type: f1
value: 56.24477391445512
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 58.23340471092078
- type: ap
value: 13.20222967424681
- type: f1
value: 47.511718095460296
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 68.97232499999998
- type: ap
value: 63.53632885535693
- type: f1
value: 68.62038513152868
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 33.855999999999995
- type: f1
value: 33.43468222830134
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 29.697999999999997
- type: f1
value: 29.39935388885501
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 35.974000000000004
- type: f1
value: 35.25910820714383
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 35.922
- type: f1
value: 35.38637028933444
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 27.636
- type: f1
value: 27.178349955978266
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 32.632
- type: f1
value: 32.08014766494587
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 23.684
- type: map_at_10
value: 38.507999999999996
- type: map_at_100
value: 39.677
- type: map_at_1000
value: 39.690999999999995
- type: map_at_3
value: 33.369
- type: map_at_5
value: 36.15
- type: mrr_at_1
value: 24.04
- type: mrr_at_10
value: 38.664
- type: mrr_at_100
value: 39.833
- type: mrr_at_1000
value: 39.847
- type: mrr_at_3
value: 33.476
- type: mrr_at_5
value: 36.306
- type: ndcg_at_1
value: 23.684
- type: ndcg_at_10
value: 47.282000000000004
- type: ndcg_at_100
value: 52.215
- type: ndcg_at_1000
value: 52.551
- type: ndcg_at_3
value: 36.628
- type: ndcg_at_5
value: 41.653
- type: precision_at_1
value: 23.684
- type: precision_at_10
value: 7.553
- type: precision_at_100
value: 0.97
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.363
- type: precision_at_5
value: 11.664
- type: recall_at_1
value: 23.684
- type: recall_at_10
value: 75.533
- type: recall_at_100
value: 97.013
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 46.088
- type: recall_at_5
value: 58.321
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 44.59375023881131
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 38.02921907752556
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 59.97321570342109
- type: mrr
value: 73.18284746955106
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 89.09091435741429
- type: cos_sim_spearman
value: 85.31459455332202
- type: euclidean_pearson
value: 79.3587681410798
- type: euclidean_spearman
value: 76.8174129874685
- type: manhattan_pearson
value: 79.57051762121769
- type: manhattan_spearman
value: 76.75837549768094
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 54.27974947807933
- type: f1
value: 54.00144411132214
- type: precision
value: 53.87119374071357
- type: recall
value: 54.27974947807933
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.3365617433414
- type: f1
value: 97.06141316310809
- type: precision
value: 96.92567319685965
- type: recall
value: 97.3365617433414
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 46.05472809144441
- type: f1
value: 45.30319274690595
- type: precision
value: 45.00015469655234
- type: recall
value: 46.05472809144441
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.10426540284361
- type: f1
value: 97.96384061786905
- type: precision
value: 97.89362822538178
- type: recall
value: 98.10426540284361
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 84.33441558441558
- type: f1
value: 84.31653077470322
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 36.025318694698086
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 32.484889034590346
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 30.203999999999997
- type: map_at_10
value: 41.314
- type: map_at_100
value: 42.66
- type: map_at_1000
value: 42.775999999999996
- type: map_at_3
value: 37.614999999999995
- type: map_at_5
value: 39.643
- type: mrr_at_1
value: 37.482
- type: mrr_at_10
value: 47.075
- type: mrr_at_100
value: 47.845
- type: mrr_at_1000
value: 47.887
- type: mrr_at_3
value: 44.635000000000005
- type: mrr_at_5
value: 45.966
- type: ndcg_at_1
value: 37.482
- type: ndcg_at_10
value: 47.676
- type: ndcg_at_100
value: 52.915
- type: ndcg_at_1000
value: 54.82900000000001
- type: ndcg_at_3
value: 42.562
- type: ndcg_at_5
value: 44.852
- type: precision_at_1
value: 37.482
- type: precision_at_10
value: 9.142
- type: precision_at_100
value: 1.436
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 20.458000000000002
- type: precision_at_5
value: 14.821000000000002
- type: recall_at_1
value: 30.203999999999997
- type: recall_at_10
value: 60.343
- type: recall_at_100
value: 82.58
- type: recall_at_1000
value: 94.813
- type: recall_at_3
value: 45.389
- type: recall_at_5
value: 51.800999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 30.889
- type: map_at_10
value: 40.949999999999996
- type: map_at_100
value: 42.131
- type: map_at_1000
value: 42.253
- type: map_at_3
value: 38.346999999999994
- type: map_at_5
value: 39.782000000000004
- type: mrr_at_1
value: 38.79
- type: mrr_at_10
value: 46.944
- type: mrr_at_100
value: 47.61
- type: mrr_at_1000
value: 47.650999999999996
- type: mrr_at_3
value: 45.053
- type: mrr_at_5
value: 46.101
- type: ndcg_at_1
value: 38.79
- type: ndcg_at_10
value: 46.286
- type: ndcg_at_100
value: 50.637
- type: ndcg_at_1000
value: 52.649
- type: ndcg_at_3
value: 42.851
- type: ndcg_at_5
value: 44.311
- type: precision_at_1
value: 38.79
- type: precision_at_10
value: 8.516
- type: precision_at_100
value: 1.3679999999999999
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 20.637
- type: precision_at_5
value: 14.318
- type: recall_at_1
value: 30.889
- type: recall_at_10
value: 55.327000000000005
- type: recall_at_100
value: 74.091
- type: recall_at_1000
value: 86.75500000000001
- type: recall_at_3
value: 44.557
- type: recall_at_5
value: 49.064
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 39.105000000000004
- type: map_at_10
value: 50.928
- type: map_at_100
value: 51.958000000000006
- type: map_at_1000
value: 52.017
- type: map_at_3
value: 47.638999999999996
- type: map_at_5
value: 49.624
- type: mrr_at_1
value: 44.639
- type: mrr_at_10
value: 54.261
- type: mrr_at_100
value: 54.913999999999994
- type: mrr_at_1000
value: 54.945
- type: mrr_at_3
value: 51.681999999999995
- type: mrr_at_5
value: 53.290000000000006
- type: ndcg_at_1
value: 44.639
- type: ndcg_at_10
value: 56.678
- type: ndcg_at_100
value: 60.649
- type: ndcg_at_1000
value: 61.855000000000004
- type: ndcg_at_3
value: 51.092999999999996
- type: ndcg_at_5
value: 54.096999999999994
- type: precision_at_1
value: 44.639
- type: precision_at_10
value: 9.028
- type: precision_at_100
value: 1.194
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.508
- type: precision_at_5
value: 15.661
- type: recall_at_1
value: 39.105000000000004
- type: recall_at_10
value: 70.367
- type: recall_at_100
value: 87.359
- type: recall_at_1000
value: 95.88
- type: recall_at_3
value: 55.581
- type: recall_at_5
value: 62.821000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.777
- type: map_at_10
value: 32.297
- type: map_at_100
value: 33.516
- type: map_at_1000
value: 33.592
- type: map_at_3
value: 30.001
- type: map_at_5
value: 31.209999999999997
- type: mrr_at_1
value: 25.989
- type: mrr_at_10
value: 34.472
- type: mrr_at_100
value: 35.518
- type: mrr_at_1000
value: 35.577
- type: mrr_at_3
value: 32.185
- type: mrr_at_5
value: 33.399
- type: ndcg_at_1
value: 25.989
- type: ndcg_at_10
value: 37.037
- type: ndcg_at_100
value: 42.699
- type: ndcg_at_1000
value: 44.725
- type: ndcg_at_3
value: 32.485
- type: ndcg_at_5
value: 34.549
- type: precision_at_1
value: 25.989
- type: precision_at_10
value: 5.718
- type: precision_at_100
value: 0.89
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 14.049
- type: precision_at_5
value: 9.672
- type: recall_at_1
value: 23.777
- type: recall_at_10
value: 49.472
- type: recall_at_100
value: 74.857
- type: recall_at_1000
value: 90.289
- type: recall_at_3
value: 37.086000000000006
- type: recall_at_5
value: 42.065999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 13.377
- type: map_at_10
value: 21.444
- type: map_at_100
value: 22.663
- type: map_at_1000
value: 22.8
- type: map_at_3
value: 18.857
- type: map_at_5
value: 20.426
- type: mrr_at_1
value: 16.542
- type: mrr_at_10
value: 25.326999999999998
- type: mrr_at_100
value: 26.323
- type: mrr_at_1000
value: 26.406000000000002
- type: mrr_at_3
value: 22.823
- type: mrr_at_5
value: 24.340999999999998
- type: ndcg_at_1
value: 16.542
- type: ndcg_at_10
value: 26.479000000000003
- type: ndcg_at_100
value: 32.29
- type: ndcg_at_1000
value: 35.504999999999995
- type: ndcg_at_3
value: 21.619
- type: ndcg_at_5
value: 24.19
- type: precision_at_1
value: 16.542
- type: precision_at_10
value: 5.075
- type: precision_at_100
value: 0.9339999999999999
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 10.697
- type: precision_at_5
value: 8.134
- type: recall_at_1
value: 13.377
- type: recall_at_10
value: 38.027
- type: recall_at_100
value: 63.439
- type: recall_at_1000
value: 86.354
- type: recall_at_3
value: 25.0
- type: recall_at_5
value: 31.306
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 28.368
- type: map_at_10
value: 39.305
- type: map_at_100
value: 40.637
- type: map_at_1000
value: 40.753
- type: map_at_3
value: 36.077999999999996
- type: map_at_5
value: 37.829
- type: mrr_at_1
value: 34.937000000000005
- type: mrr_at_10
value: 45.03
- type: mrr_at_100
value: 45.78
- type: mrr_at_1000
value: 45.827
- type: mrr_at_3
value: 42.348
- type: mrr_at_5
value: 43.807
- type: ndcg_at_1
value: 34.937000000000005
- type: ndcg_at_10
value: 45.605000000000004
- type: ndcg_at_100
value: 50.941
- type: ndcg_at_1000
value: 52.983000000000004
- type: ndcg_at_3
value: 40.366
- type: ndcg_at_5
value: 42.759
- type: precision_at_1
value: 34.937000000000005
- type: precision_at_10
value: 8.402
- type: precision_at_100
value: 1.2959999999999998
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 19.217000000000002
- type: precision_at_5
value: 13.725000000000001
- type: recall_at_1
value: 28.368
- type: recall_at_10
value: 58.5
- type: recall_at_100
value: 80.67999999999999
- type: recall_at_1000
value: 93.925
- type: recall_at_3
value: 43.956
- type: recall_at_5
value: 50.065000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 24.851
- type: map_at_10
value: 34.758
- type: map_at_100
value: 36.081
- type: map_at_1000
value: 36.205999999999996
- type: map_at_3
value: 31.678
- type: map_at_5
value: 33.398
- type: mrr_at_1
value: 31.279
- type: mrr_at_10
value: 40.138
- type: mrr_at_100
value: 41.005
- type: mrr_at_1000
value: 41.065000000000005
- type: mrr_at_3
value: 37.519000000000005
- type: mrr_at_5
value: 38.986
- type: ndcg_at_1
value: 31.279
- type: ndcg_at_10
value: 40.534
- type: ndcg_at_100
value: 46.093
- type: ndcg_at_1000
value: 48.59
- type: ndcg_at_3
value: 35.473
- type: ndcg_at_5
value: 37.801
- type: precision_at_1
value: 31.279
- type: precision_at_10
value: 7.477
- type: precision_at_100
value: 1.2
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 17.047
- type: precision_at_5
value: 12.306000000000001
- type: recall_at_1
value: 24.851
- type: recall_at_10
value: 52.528
- type: recall_at_100
value: 76.198
- type: recall_at_1000
value: 93.12
- type: recall_at_3
value: 38.257999999999996
- type: recall_at_5
value: 44.440000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 25.289833333333334
- type: map_at_10
value: 34.379333333333335
- type: map_at_100
value: 35.56916666666666
- type: map_at_1000
value: 35.68633333333333
- type: map_at_3
value: 31.63916666666666
- type: map_at_5
value: 33.18383333333334
- type: mrr_at_1
value: 30.081749999999996
- type: mrr_at_10
value: 38.53658333333333
- type: mrr_at_100
value: 39.37825
- type: mrr_at_1000
value: 39.43866666666666
- type: mrr_at_3
value: 36.19025
- type: mrr_at_5
value: 37.519749999999995
- type: ndcg_at_1
value: 30.081749999999996
- type: ndcg_at_10
value: 39.62041666666667
- type: ndcg_at_100
value: 44.74825
- type: ndcg_at_1000
value: 47.11366666666667
- type: ndcg_at_3
value: 35.000499999999995
- type: ndcg_at_5
value: 37.19283333333333
- type: precision_at_1
value: 30.081749999999996
- type: precision_at_10
value: 6.940249999999999
- type: precision_at_100
value: 1.1164166666666668
- type: precision_at_1000
value: 0.15025000000000002
- type: precision_at_3
value: 16.110416666666666
- type: precision_at_5
value: 11.474416666666668
- type: recall_at_1
value: 25.289833333333334
- type: recall_at_10
value: 51.01591666666667
- type: recall_at_100
value: 73.55275000000002
- type: recall_at_1000
value: 90.02666666666667
- type: recall_at_3
value: 38.15208333333334
- type: recall_at_5
value: 43.78458333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.479
- type: map_at_10
value: 31.2
- type: map_at_100
value: 32.11
- type: map_at_1000
value: 32.214
- type: map_at_3
value: 29.093999999999998
- type: map_at_5
value: 30.415
- type: mrr_at_1
value: 26.840000000000003
- type: mrr_at_10
value: 34.153
- type: mrr_at_100
value: 34.971000000000004
- type: mrr_at_1000
value: 35.047
- type: mrr_at_3
value: 32.285000000000004
- type: mrr_at_5
value: 33.443
- type: ndcg_at_1
value: 26.840000000000003
- type: ndcg_at_10
value: 35.441
- type: ndcg_at_100
value: 40.150000000000006
- type: ndcg_at_1000
value: 42.74
- type: ndcg_at_3
value: 31.723000000000003
- type: ndcg_at_5
value: 33.71
- type: precision_at_1
value: 26.840000000000003
- type: precision_at_10
value: 5.552
- type: precision_at_100
value: 0.859
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.804
- type: precision_at_5
value: 9.600999999999999
- type: recall_at_1
value: 23.479
- type: recall_at_10
value: 45.442
- type: recall_at_100
value: 67.465
- type: recall_at_1000
value: 86.53
- type: recall_at_3
value: 35.315999999999995
- type: recall_at_5
value: 40.253
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.887
- type: map_at_10
value: 23.805
- type: map_at_100
value: 24.804000000000002
- type: map_at_1000
value: 24.932000000000002
- type: map_at_3
value: 21.632
- type: map_at_5
value: 22.845
- type: mrr_at_1
value: 20.75
- type: mrr_at_10
value: 27.686
- type: mrr_at_100
value: 28.522
- type: mrr_at_1000
value: 28.605000000000004
- type: mrr_at_3
value: 25.618999999999996
- type: mrr_at_5
value: 26.723999999999997
- type: ndcg_at_1
value: 20.75
- type: ndcg_at_10
value: 28.233000000000004
- type: ndcg_at_100
value: 33.065
- type: ndcg_at_1000
value: 36.138999999999996
- type: ndcg_at_3
value: 24.361
- type: ndcg_at_5
value: 26.111
- type: precision_at_1
value: 20.75
- type: precision_at_10
value: 5.124
- type: precision_at_100
value: 0.8750000000000001
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 11.539000000000001
- type: precision_at_5
value: 8.273
- type: recall_at_1
value: 16.887
- type: recall_at_10
value: 37.774
- type: recall_at_100
value: 59.587
- type: recall_at_1000
value: 81.523
- type: recall_at_3
value: 26.837
- type: recall_at_5
value: 31.456
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 25.534000000000002
- type: map_at_10
value: 33.495999999999995
- type: map_at_100
value: 34.697
- type: map_at_1000
value: 34.805
- type: map_at_3
value: 31.22
- type: map_at_5
value: 32.277
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 37.723
- type: mrr_at_100
value: 38.645
- type: mrr_at_1000
value: 38.712999999999994
- type: mrr_at_3
value: 35.665
- type: mrr_at_5
value: 36.681999999999995
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 38.407000000000004
- type: ndcg_at_100
value: 43.877
- type: ndcg_at_1000
value: 46.312
- type: ndcg_at_3
value: 34.211000000000006
- type: ndcg_at_5
value: 35.760999999999996
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.343
- type: precision_at_100
value: 1.023
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 15.360999999999999
- type: precision_at_5
value: 10.428999999999998
- type: recall_at_1
value: 25.534000000000002
- type: recall_at_10
value: 49.204
- type: recall_at_100
value: 72.878
- type: recall_at_1000
value: 89.95
- type: recall_at_3
value: 37.533
- type: recall_at_5
value: 41.611
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 26.291999999999998
- type: map_at_10
value: 35.245
- type: map_at_100
value: 36.762
- type: map_at_1000
value: 36.983
- type: map_at_3
value: 32.439
- type: map_at_5
value: 33.964
- type: mrr_at_1
value: 31.423000000000002
- type: mrr_at_10
value: 39.98
- type: mrr_at_100
value: 40.791
- type: mrr_at_1000
value: 40.854
- type: mrr_at_3
value: 37.451
- type: mrr_at_5
value: 38.854
- type: ndcg_at_1
value: 31.423000000000002
- type: ndcg_at_10
value: 40.848
- type: ndcg_at_100
value: 46.35
- type: ndcg_at_1000
value: 49.166
- type: ndcg_at_3
value: 36.344
- type: ndcg_at_5
value: 38.36
- type: precision_at_1
value: 31.423000000000002
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.498
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 16.733
- type: precision_at_5
value: 12.213000000000001
- type: recall_at_1
value: 26.291999999999998
- type: recall_at_10
value: 51.184
- type: recall_at_100
value: 76.041
- type: recall_at_1000
value: 94.11500000000001
- type: recall_at_3
value: 38.257000000000005
- type: recall_at_5
value: 43.68
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.715
- type: map_at_10
value: 27.810000000000002
- type: map_at_100
value: 28.810999999999996
- type: map_at_1000
value: 28.904999999999998
- type: map_at_3
value: 25.069999999999997
- type: map_at_5
value: 26.793
- type: mrr_at_1
value: 22.366
- type: mrr_at_10
value: 29.65
- type: mrr_at_100
value: 30.615
- type: mrr_at_1000
value: 30.686999999999998
- type: mrr_at_3
value: 27.017999999999997
- type: mrr_at_5
value: 28.644
- type: ndcg_at_1
value: 22.366
- type: ndcg_at_10
value: 32.221
- type: ndcg_at_100
value: 37.313
- type: ndcg_at_1000
value: 39.871
- type: ndcg_at_3
value: 26.918
- type: ndcg_at_5
value: 29.813000000000002
- type: precision_at_1
value: 22.366
- type: precision_at_10
value: 5.139
- type: precision_at_100
value: 0.8240000000000001
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 11.275
- type: precision_at_5
value: 8.540000000000001
- type: recall_at_1
value: 20.715
- type: recall_at_10
value: 44.023
- type: recall_at_100
value: 67.458
- type: recall_at_1000
value: 87.066
- type: recall_at_3
value: 30.055
- type: recall_at_5
value: 36.852000000000004
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 11.859
- type: map_at_10
value: 20.625
- type: map_at_100
value: 22.5
- type: map_at_1000
value: 22.689
- type: map_at_3
value: 16.991
- type: map_at_5
value: 18.781
- type: mrr_at_1
value: 26.906000000000002
- type: mrr_at_10
value: 39.083
- type: mrr_at_100
value: 39.978
- type: mrr_at_1000
value: 40.014
- type: mrr_at_3
value: 35.44
- type: mrr_at_5
value: 37.619
- type: ndcg_at_1
value: 26.906000000000002
- type: ndcg_at_10
value: 29.386000000000003
- type: ndcg_at_100
value: 36.510999999999996
- type: ndcg_at_1000
value: 39.814
- type: ndcg_at_3
value: 23.558
- type: ndcg_at_5
value: 25.557999999999996
- type: precision_at_1
value: 26.906000000000002
- type: precision_at_10
value: 9.342
- type: precision_at_100
value: 1.6969999999999998
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 17.503
- type: precision_at_5
value: 13.655000000000001
- type: recall_at_1
value: 11.859
- type: recall_at_10
value: 35.929
- type: recall_at_100
value: 60.21300000000001
- type: recall_at_1000
value: 78.606
- type: recall_at_3
value: 21.727
- type: recall_at_5
value: 27.349
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 8.627
- type: map_at_10
value: 18.248
- type: map_at_100
value: 25.19
- type: map_at_1000
value: 26.741
- type: map_at_3
value: 13.286000000000001
- type: map_at_5
value: 15.126000000000001
- type: mrr_at_1
value: 64.75
- type: mrr_at_10
value: 71.865
- type: mrr_at_100
value: 72.247
- type: mrr_at_1000
value: 72.255
- type: mrr_at_3
value: 69.958
- type: mrr_at_5
value: 71.108
- type: ndcg_at_1
value: 53.25
- type: ndcg_at_10
value: 39.035
- type: ndcg_at_100
value: 42.735
- type: ndcg_at_1000
value: 50.166
- type: ndcg_at_3
value: 43.857
- type: ndcg_at_5
value: 40.579
- type: precision_at_1
value: 64.75
- type: precision_at_10
value: 30.75
- type: precision_at_100
value: 9.54
- type: precision_at_1000
value: 2.035
- type: precision_at_3
value: 47.333
- type: precision_at_5
value: 39.0
- type: recall_at_1
value: 8.627
- type: recall_at_10
value: 23.413
- type: recall_at_100
value: 48.037
- type: recall_at_1000
value: 71.428
- type: recall_at_3
value: 14.158999999999999
- type: recall_at_5
value: 17.002
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 44.865
- type: f1
value: 41.56625743266997
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 57.335
- type: map_at_10
value: 68.29499999999999
- type: map_at_100
value: 68.69800000000001
- type: map_at_1000
value: 68.714
- type: map_at_3
value: 66.149
- type: map_at_5
value: 67.539
- type: mrr_at_1
value: 61.656
- type: mrr_at_10
value: 72.609
- type: mrr_at_100
value: 72.923
- type: mrr_at_1000
value: 72.928
- type: mrr_at_3
value: 70.645
- type: mrr_at_5
value: 71.938
- type: ndcg_at_1
value: 61.656
- type: ndcg_at_10
value: 73.966
- type: ndcg_at_100
value: 75.663
- type: ndcg_at_1000
value: 75.986
- type: ndcg_at_3
value: 69.959
- type: ndcg_at_5
value: 72.269
- type: precision_at_1
value: 61.656
- type: precision_at_10
value: 9.581000000000001
- type: precision_at_100
value: 1.054
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 27.743000000000002
- type: precision_at_5
value: 17.939
- type: recall_at_1
value: 57.335
- type: recall_at_10
value: 87.24300000000001
- type: recall_at_100
value: 94.575
- type: recall_at_1000
value: 96.75399999999999
- type: recall_at_3
value: 76.44800000000001
- type: recall_at_5
value: 82.122
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 17.014000000000003
- type: map_at_10
value: 28.469
- type: map_at_100
value: 30.178
- type: map_at_1000
value: 30.369
- type: map_at_3
value: 24.63
- type: map_at_5
value: 26.891
- type: mrr_at_1
value: 34.259
- type: mrr_at_10
value: 43.042
- type: mrr_at_100
value: 43.91
- type: mrr_at_1000
value: 43.963
- type: mrr_at_3
value: 40.483999999999995
- type: mrr_at_5
value: 42.135
- type: ndcg_at_1
value: 34.259
- type: ndcg_at_10
value: 35.836
- type: ndcg_at_100
value: 42.488
- type: ndcg_at_1000
value: 45.902
- type: ndcg_at_3
value: 32.131
- type: ndcg_at_5
value: 33.697
- type: precision_at_1
value: 34.259
- type: precision_at_10
value: 10.0
- type: precision_at_100
value: 1.699
- type: precision_at_1000
value: 0.22999999999999998
- type: precision_at_3
value: 21.502
- type: precision_at_5
value: 16.296
- type: recall_at_1
value: 17.014000000000003
- type: recall_at_10
value: 42.832
- type: recall_at_100
value: 67.619
- type: recall_at_1000
value: 88.453
- type: recall_at_3
value: 29.537000000000003
- type: recall_at_5
value: 35.886
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 34.558
- type: map_at_10
value: 48.039
- type: map_at_100
value: 48.867
- type: map_at_1000
value: 48.941
- type: map_at_3
value: 45.403
- type: map_at_5
value: 46.983999999999995
- type: mrr_at_1
value: 69.11500000000001
- type: mrr_at_10
value: 75.551
- type: mrr_at_100
value: 75.872
- type: mrr_at_1000
value: 75.887
- type: mrr_at_3
value: 74.447
- type: mrr_at_5
value: 75.113
- type: ndcg_at_1
value: 69.11500000000001
- type: ndcg_at_10
value: 57.25599999999999
- type: ndcg_at_100
value: 60.417
- type: ndcg_at_1000
value: 61.976
- type: ndcg_at_3
value: 53.258
- type: ndcg_at_5
value: 55.374
- type: precision_at_1
value: 69.11500000000001
- type: precision_at_10
value: 11.689
- type: precision_at_100
value: 1.418
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 33.018
- type: precision_at_5
value: 21.488
- type: recall_at_1
value: 34.558
- type: recall_at_10
value: 58.447
- type: recall_at_100
value: 70.91199999999999
- type: recall_at_1000
value: 81.31
- type: recall_at_3
value: 49.527
- type: recall_at_5
value: 53.72
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 61.772000000000006
- type: ap
value: 57.48217702943605
- type: f1
value: 61.20495351356274
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 22.044
- type: map_at_10
value: 34.211000000000006
- type: map_at_100
value: 35.394
- type: map_at_1000
value: 35.443000000000005
- type: map_at_3
value: 30.318
- type: map_at_5
value: 32.535
- type: mrr_at_1
value: 22.722
- type: mrr_at_10
value: 34.842
- type: mrr_at_100
value: 35.954
- type: mrr_at_1000
value: 35.997
- type: mrr_at_3
value: 30.991000000000003
- type: mrr_at_5
value: 33.2
- type: ndcg_at_1
value: 22.722
- type: ndcg_at_10
value: 41.121
- type: ndcg_at_100
value: 46.841
- type: ndcg_at_1000
value: 48.049
- type: ndcg_at_3
value: 33.173
- type: ndcg_at_5
value: 37.145
- type: precision_at_1
value: 22.722
- type: precision_at_10
value: 6.516
- type: precision_at_100
value: 0.9400000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.093
- type: precision_at_5
value: 10.473
- type: recall_at_1
value: 22.044
- type: recall_at_10
value: 62.382000000000005
- type: recall_at_100
value: 88.914
- type: recall_at_1000
value: 98.099
- type: recall_at_3
value: 40.782000000000004
- type: recall_at_5
value: 50.322
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 93.68217054263563
- type: f1
value: 93.25810075739523
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 82.05409974640745
- type: f1
value: 80.42814140324903
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 93.54903268845896
- type: f1
value: 92.8909878077932
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 90.98340119010334
- type: f1
value: 90.51522537281313
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 89.33309429903191
- type: f1
value: 88.60371305209185
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 60.4882459312839
- type: f1
value: 59.02590456131682
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 71.34290925672595
- type: f1
value: 54.44803151449109
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 61.92448577063963
- type: f1
value: 43.125939975781854
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 74.48965977318213
- type: f1
value: 51.855353687466696
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 69.11994989038521
- type: f1
value: 50.57872704171278
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 64.84761563284331
- type: f1
value: 43.61322970761394
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 49.35623869801085
- type: f1
value: 33.48547326952042
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (af)
config: af
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 47.85474108944183
- type: f1
value: 46.50175016795915
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (am)
config: am
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 33.29858776059179
- type: f1
value: 31.803027601259082
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ar)
config: ar
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 59.24680564895763
- type: f1
value: 57.037691806846865
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (az)
config: az
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 45.23537323470073
- type: f1
value: 44.81126398428613
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (bn)
config: bn
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 61.590450571620714
- type: f1
value: 59.247442149977104
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (cy)
config: cy
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 44.9226630800269
- type: f1
value: 44.076183379991654
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (da)
config: da
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 51.23066577000672
- type: f1
value: 50.20719330417618
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (de)
config: de
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 56.0995292535306
- type: f1
value: 53.29421532133969
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (el)
config: el
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 46.12642905178211
- type: f1
value: 44.441530267639635
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 69.67047747141896
- type: f1
value: 68.38493366054783
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (es)
config: es
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 66.3483523873571
- type: f1
value: 65.13046416817832
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fa)
config: fa
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 51.20040349697378
- type: f1
value: 49.02889836601541
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fi)
config: fi
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 45.33288500336248
- type: f1
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metrics:
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value: 44.351042367182245
- type: f1
value: 42.05421666771541
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type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ml)
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 65.53127101546738
- type: f1
value: 65.98462024333497
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (mn)
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 38.71553463349025
- type: f1
value: 37.44327037149584
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ms)
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.98991257565567
- type: f1
value: 63.87720198978004
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (my)
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 36.839273705447205
- type: f1
value: 35.233967279698376
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nb)
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 51.79892400806993
- type: f1
value: 49.66926632125972
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nl)
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.31809011432415
- type: f1
value: 53.832185336179826
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pl)
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 49.979825151311374
- type: f1
value: 48.83013175441888
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pt)
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.45595158036315
- type: f1
value: 72.08708814699702
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ro)
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 53.68527236045729
- type: f1
value: 52.23278593929981
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ru)
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 61.60390047074647
- type: f1
value: 60.50391482195116
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sl)
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 48.036314727639535
- type: f1
value: 46.43480413383716
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sq)
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 50.05716207128445
- type: f1
value: 48.85821859948888
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sv)
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 51.728312037659705
- type: f1
value: 49.89292996950847
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sw)
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 54.21990585070613
- type: f1
value: 52.8711542984193
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ta)
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.770679219905844
- type: f1
value: 63.09441501491594
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (te)
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.58574310692671
- type: f1
value: 61.61370697612978
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (th)
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 45.17821116341628
- type: f1
value: 43.85143229183324
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tl)
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 52.064559515803644
- type: f1
value: 50.94356892049626
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 47.205783456624076
- type: f1
value: 47.04223644120489
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ur)
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.25689307330195
- type: f1
value: 63.89944944984115
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.60524546065905
- type: f1
value: 71.5634157334358
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.95427034297242
- type: f1
value: 74.39706882311063
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.29926025554808
- type: f1
value: 71.32045932560297
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 31.054474964883806
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 29.259725940477523
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.785007883256572
- type: mrr
value: 32.983556622438456
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 5.742
- type: map_at_10
value: 13.074
- type: map_at_100
value: 16.716
- type: map_at_1000
value: 18.238
- type: map_at_3
value: 9.600999999999999
- type: map_at_5
value: 11.129999999999999
- type: mrr_at_1
value: 47.988
- type: mrr_at_10
value: 55.958
- type: mrr_at_100
value: 56.58800000000001
- type: mrr_at_1000
value: 56.620000000000005
- type: mrr_at_3
value: 54.025
- type: mrr_at_5
value: 55.31
- type: ndcg_at_1
value: 46.44
- type: ndcg_at_10
value: 35.776
- type: ndcg_at_100
value: 32.891999999999996
- type: ndcg_at_1000
value: 41.835
- type: ndcg_at_3
value: 41.812
- type: ndcg_at_5
value: 39.249
- type: precision_at_1
value: 48.297000000000004
- type: precision_at_10
value: 26.687
- type: precision_at_100
value: 8.511000000000001
- type: precision_at_1000
value: 2.128
- type: precision_at_3
value: 39.009
- type: precision_at_5
value: 33.994
- type: recall_at_1
value: 5.742
- type: recall_at_10
value: 16.993
- type: recall_at_100
value: 33.69
- type: recall_at_1000
value: 66.75
- type: recall_at_3
value: 10.817
- type: recall_at_5
value: 13.256
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 30.789
- type: map_at_10
value: 45.751999999999995
- type: map_at_100
value: 46.766000000000005
- type: map_at_1000
value: 46.798
- type: map_at_3
value: 41.746
- type: map_at_5
value: 44.046
- type: mrr_at_1
value: 34.618
- type: mrr_at_10
value: 48.288
- type: mrr_at_100
value: 49.071999999999996
- type: mrr_at_1000
value: 49.094
- type: mrr_at_3
value: 44.979
- type: mrr_at_5
value: 46.953
- type: ndcg_at_1
value: 34.589
- type: ndcg_at_10
value: 53.151
- type: ndcg_at_100
value: 57.537000000000006
- type: ndcg_at_1000
value: 58.321999999999996
- type: ndcg_at_3
value: 45.628
- type: ndcg_at_5
value: 49.474000000000004
- type: precision_at_1
value: 34.589
- type: precision_at_10
value: 8.731
- type: precision_at_100
value: 1.119
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 20.819
- type: precision_at_5
value: 14.728
- type: recall_at_1
value: 30.789
- type: recall_at_10
value: 73.066
- type: recall_at_100
value: 92.27
- type: recall_at_1000
value: 98.18
- type: recall_at_3
value: 53.632999999999996
- type: recall_at_5
value: 62.476
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 54.993
- type: map_at_10
value: 69.07600000000001
- type: map_at_100
value: 70.05799999999999
- type: map_at_1000
value: 70.09
- type: map_at_3
value: 65.456
- type: map_at_5
value: 67.622
- type: mrr_at_1
value: 63.07000000000001
- type: mrr_at_10
value: 72.637
- type: mrr_at_100
value: 73.029
- type: mrr_at_1000
value: 73.033
- type: mrr_at_3
value: 70.572
- type: mrr_at_5
value: 71.86399999999999
- type: ndcg_at_1
value: 63.07000000000001
- type: ndcg_at_10
value: 74.708
- type: ndcg_at_100
value: 77.579
- type: ndcg_at_1000
value: 77.897
- type: ndcg_at_3
value: 69.69999999999999
- type: ndcg_at_5
value: 72.321
- type: precision_at_1
value: 63.07000000000001
- type: precision_at_10
value: 11.851
- type: precision_at_100
value: 1.481
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 30.747000000000003
- type: precision_at_5
value: 20.830000000000002
- type: recall_at_1
value: 54.993
- type: recall_at_10
value: 87.18900000000001
- type: recall_at_100
value: 98.137
- type: recall_at_1000
value: 99.833
- type: recall_at_3
value: 73.654
- type: recall_at_5
value: 80.36
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 35.53178375429036
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 54.520782970558265
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 4.3229999999999995
- type: map_at_10
value: 10.979999999999999
- type: map_at_100
value: 12.867
- type: map_at_1000
value: 13.147
- type: map_at_3
value: 7.973
- type: map_at_5
value: 9.513
- type: mrr_at_1
value: 21.3
- type: mrr_at_10
value: 32.34
- type: mrr_at_100
value: 33.428999999999995
- type: mrr_at_1000
value: 33.489999999999995
- type: mrr_at_3
value: 28.999999999999996
- type: mrr_at_5
value: 31.019999999999996
- type: ndcg_at_1
value: 21.3
- type: ndcg_at_10
value: 18.619
- type: ndcg_at_100
value: 26.108999999999998
- type: ndcg_at_1000
value: 31.253999999999998
- type: ndcg_at_3
value: 17.842
- type: ndcg_at_5
value: 15.673
- type: precision_at_1
value: 21.3
- type: precision_at_10
value: 9.55
- type: precision_at_100
value: 2.0340000000000003
- type: precision_at_1000
value: 0.327
- type: precision_at_3
value: 16.667
- type: precision_at_5
value: 13.76
- type: recall_at_1
value: 4.3229999999999995
- type: recall_at_10
value: 19.387
- type: recall_at_100
value: 41.307
- type: recall_at_1000
value: 66.475
- type: recall_at_3
value: 10.143
- type: recall_at_5
value: 14.007
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 78.77975189382573
- type: cos_sim_spearman
value: 69.81522686267631
- type: euclidean_pearson
value: 71.37617936889518
- type: euclidean_spearman
value: 65.71738481148611
- type: manhattan_pearson
value: 71.58222165832424
- type: manhattan_spearman
value: 65.86851365286654
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 77.75509450443367
- type: cos_sim_spearman
value: 69.66180222442091
- type: euclidean_pearson
value: 74.98512779786111
- type: euclidean_spearman
value: 69.5997451409469
- type: manhattan_pearson
value: 75.50135090962459
- type: manhattan_spearman
value: 69.94984748475302
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 79.42363892383264
- type: cos_sim_spearman
value: 79.66529244176742
- type: euclidean_pearson
value: 79.50429208135942
- type: euclidean_spearman
value: 80.44767586416276
- type: manhattan_pearson
value: 79.58563944997708
- type: manhattan_spearman
value: 80.51452267103
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 79.2749401478149
- type: cos_sim_spearman
value: 74.6076920702392
- type: euclidean_pearson
value: 73.3302002952881
- type: euclidean_spearman
value: 70.67029803077013
- type: manhattan_pearson
value: 73.52699344010296
- type: manhattan_spearman
value: 70.8517556194297
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 83.20884740785921
- type: cos_sim_spearman
value: 83.80600789090722
- type: euclidean_pearson
value: 74.9154089816344
- type: euclidean_spearman
value: 75.69243899592276
- type: manhattan_pearson
value: 75.0312832634451
- type: manhattan_spearman
value: 75.78324960357642
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 79.63194141000497
- type: cos_sim_spearman
value: 80.40118418350866
- type: euclidean_pearson
value: 72.07354384551088
- type: euclidean_spearman
value: 72.28819150373845
- type: manhattan_pearson
value: 72.08736119834145
- type: manhattan_spearman
value: 72.28347083261288
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 66.78512789499386
- type: cos_sim_spearman
value: 66.89125587193288
- type: euclidean_pearson
value: 58.74535708627959
- type: euclidean_spearman
value: 59.62103716794647
- type: manhattan_pearson
value: 59.00494529143961
- type: manhattan_spearman
value: 59.832257846799806
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 75.48960503523992
- type: cos_sim_spearman
value: 76.4223037534204
- type: euclidean_pearson
value: 64.93966381820944
- type: euclidean_spearman
value: 62.39697395373789
- type: manhattan_pearson
value: 65.54480770061505
- type: manhattan_spearman
value: 62.944204863043105
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 77.7331440643619
- type: cos_sim_spearman
value: 78.0748413292835
- type: euclidean_pearson
value: 38.533108233460304
- type: euclidean_spearman
value: 35.37638615280026
- type: manhattan_pearson
value: 41.0639726746513
- type: manhattan_spearman
value: 37.688161243671765
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 58.4628923720782
- type: cos_sim_spearman
value: 59.10093128795948
- type: euclidean_pearson
value: 30.422902393436836
- type: euclidean_spearman
value: 27.837806030497457
- type: manhattan_pearson
value: 32.51576984630963
- type: manhattan_spearman
value: 29.181887010982514
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 86.87447904613737
- type: cos_sim_spearman
value: 87.06554974065622
- type: euclidean_pearson
value: 76.82669047851108
- type: euclidean_spearman
value: 75.45711985511991
- type: manhattan_pearson
value: 77.46644556452847
- type: manhattan_spearman
value: 76.0249120007112
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 17.784495723497468
- type: cos_sim_spearman
value: 11.79629537128697
- type: euclidean_pearson
value: -4.354328445994008
- type: euclidean_spearman
value: -6.984566116230058
- type: manhattan_pearson
value: -4.166751901507852
- type: manhattan_spearman
value: -6.984143198323786
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 76.9009642643449
- type: cos_sim_spearman
value: 78.21764726338341
- type: euclidean_pearson
value: 50.578959144342925
- type: euclidean_spearman
value: 51.664379260719606
- type: manhattan_pearson
value: 53.95690880393329
- type: manhattan_spearman
value: 54.910058464050785
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 86.41638022270219
- type: cos_sim_spearman
value: 86.00477030366811
- type: euclidean_pearson
value: 79.7224037788285
- type: euclidean_spearman
value: 79.21417626867616
- type: manhattan_pearson
value: 80.29412412756984
- type: manhattan_spearman
value: 79.49460867616206
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 79.90432664091082
- type: cos_sim_spearman
value: 80.46007940700204
- type: euclidean_pearson
value: 49.25348015214428
- type: euclidean_spearman
value: 47.13113020475859
- type: manhattan_pearson
value: 54.57291204043908
- type: manhattan_spearman
value: 51.98559736896087
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 52.55164822309034
- type: cos_sim_spearman
value: 51.57629192137736
- type: euclidean_pearson
value: 16.63360593235354
- type: euclidean_spearman
value: 14.479679923782912
- type: manhattan_pearson
value: 18.524867185117472
- type: manhattan_spearman
value: 16.65940056664755
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 46.83690919715875
- type: cos_sim_spearman
value: 45.84993650002922
- type: euclidean_pearson
value: 6.173128686815117
- type: euclidean_spearman
value: 6.260781946306191
- type: manhattan_pearson
value: 7.328440452367316
- type: manhattan_spearman
value: 7.370842306497447
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 64.97916914277232
- type: cos_sim_spearman
value: 66.13392188807865
- type: euclidean_pearson
value: 65.3921146908468
- type: euclidean_spearman
value: 65.8381588635056
- type: manhattan_pearson
value: 65.8866165769975
- type: manhattan_spearman
value: 66.27774050472219
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 25.605130445111545
- type: cos_sim_spearman
value: 30.054844562369254
- type: euclidean_pearson
value: 23.890611005408196
- type: euclidean_spearman
value: 29.07902600726761
- type: manhattan_pearson
value: 24.239478426621833
- type: manhattan_spearman
value: 29.48547576782375
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 61.6665616159781
- type: cos_sim_spearman
value: 65.41310206289988
- type: euclidean_pearson
value: 68.38805493215008
- type: euclidean_spearman
value: 65.22777377603435
- type: manhattan_pearson
value: 69.37445390454346
- type: manhattan_spearman
value: 66.02437701858754
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 15.302891825626372
- type: cos_sim_spearman
value: 31.134517255070097
- type: euclidean_pearson
value: 12.672592658843143
- type: euclidean_spearman
value: 29.14881036784207
- type: manhattan_pearson
value: 13.528545327757735
- type: manhattan_spearman
value: 29.56217928148797
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 28.79299114515319
- type: cos_sim_spearman
value: 47.135864983626206
- type: euclidean_pearson
value: 40.66410787594309
- type: euclidean_spearman
value: 45.09585593138228
- type: manhattan_pearson
value: 42.02561630700308
- type: manhattan_spearman
value: 45.43979983670554
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 46.00096625052943
- type: cos_sim_spearman
value: 58.67147426715496
- type: euclidean_pearson
value: 54.7154367422438
- type: euclidean_spearman
value: 59.003235142442634
- type: manhattan_pearson
value: 56.3116235357115
- type: manhattan_spearman
value: 60.12956331404423
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 29.3396354650316
- type: cos_sim_spearman
value: 43.3632935734809
- type: euclidean_pearson
value: 31.18506539466593
- type: euclidean_spearman
value: 37.531745324803815
- type: manhattan_pearson
value: 32.829038232529015
- type: manhattan_spearman
value: 38.04574361589953
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 62.9596148375188
- type: cos_sim_spearman
value: 66.77653412402461
- type: euclidean_pearson
value: 64.53156585980886
- type: euclidean_spearman
value: 66.2884373036083
- type: manhattan_pearson
value: 65.2831035495143
- type: manhattan_spearman
value: 66.83641945244322
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 79.9138821493919
- type: cos_sim_spearman
value: 80.38097535004677
- type: euclidean_pearson
value: 76.2401499094322
- type: euclidean_spearman
value: 77.00897050735907
- type: manhattan_pearson
value: 76.69531453728563
- type: manhattan_spearman
value: 77.83189696428695
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 51.27009640779202
- type: cos_sim_spearman
value: 51.16120562029285
- type: euclidean_pearson
value: 52.20594985566323
- type: euclidean_spearman
value: 52.75331049709882
- type: manhattan_pearson
value: 52.2725118792549
- type: manhattan_spearman
value: 53.614847968995115
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 70.46044814118835
- type: cos_sim_spearman
value: 75.05760236668672
- type: euclidean_pearson
value: 72.80128921879461
- type: euclidean_spearman
value: 73.81164755219257
- type: manhattan_pearson
value: 72.7863795809044
- type: manhattan_spearman
value: 73.65932033818906
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 61.89276840435938
- type: cos_sim_spearman
value: 65.65042955732055
- type: euclidean_pearson
value: 61.22969491863841
- type: euclidean_spearman
value: 63.451215637904724
- type: manhattan_pearson
value: 61.16138956945465
- type: manhattan_spearman
value: 63.34966179331079
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 56.377577221753626
- type: cos_sim_spearman
value: 53.31223653270353
- type: euclidean_pearson
value: 26.488793041564307
- type: euclidean_spearman
value: 19.524551741701472
- type: manhattan_pearson
value: 24.322868054606474
- type: manhattan_spearman
value: 19.50371443994939
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 69.3634693673425
- type: cos_sim_spearman
value: 68.45051245419702
- type: euclidean_pearson
value: 56.1417414374769
- type: euclidean_spearman
value: 55.89891749631458
- type: manhattan_pearson
value: 57.266417430882925
- type: manhattan_spearman
value: 56.57927102744128
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 60.04169437653179
- type: cos_sim_spearman
value: 65.49531007553446
- type: euclidean_pearson
value: 58.583860732586324
- type: euclidean_spearman
value: 58.80034792537441
- type: manhattan_pearson
value: 59.02513161664622
- type: manhattan_spearman
value: 58.42942047904558
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 48.81035211493999
- type: cos_sim_spearman
value: 53.27599246786967
- type: euclidean_pearson
value: 52.25710699032889
- type: euclidean_spearman
value: 55.22995695529873
- type: manhattan_pearson
value: 51.894901893217884
- type: manhattan_spearman
value: 54.95919975149795
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 36.75993101477816
- type: cos_sim_spearman
value: 43.050156692479355
- type: euclidean_pearson
value: 51.49021084746248
- type: euclidean_spearman
value: 49.54771253090078
- type: manhattan_pearson
value: 54.68410760796417
- type: manhattan_spearman
value: 48.19277197691717
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 48.553763306386486
- type: cos_sim_spearman
value: 28.17180849095055
- type: euclidean_pearson
value: 17.50739087826514
- type: euclidean_spearman
value: 16.903085094570333
- type: manhattan_pearson
value: 20.750046512534112
- type: manhattan_spearman
value: 5.634361698190111
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 82.17107190594417
- type: cos_sim_spearman
value: 80.89611873505183
- type: euclidean_pearson
value: 71.82491561814403
- type: euclidean_spearman
value: 70.33608835403274
- type: manhattan_pearson
value: 71.89538332420133
- type: manhattan_spearman
value: 70.36082395775944
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 79.77047154974562
- type: mrr
value: 94.25887021475256
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 56.328
- type: map_at_10
value: 67.167
- type: map_at_100
value: 67.721
- type: map_at_1000
value: 67.735
- type: map_at_3
value: 64.20400000000001
- type: map_at_5
value: 65.904
- type: mrr_at_1
value: 59.667
- type: mrr_at_10
value: 68.553
- type: mrr_at_100
value: 68.992
- type: mrr_at_1000
value: 69.004
- type: mrr_at_3
value: 66.22200000000001
- type: mrr_at_5
value: 67.739
- type: ndcg_at_1
value: 59.667
- type: ndcg_at_10
value: 72.111
- type: ndcg_at_100
value: 74.441
- type: ndcg_at_1000
value: 74.90599999999999
- type: ndcg_at_3
value: 67.11399999999999
- type: ndcg_at_5
value: 69.687
- type: precision_at_1
value: 59.667
- type: precision_at_10
value: 9.733
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 17.599999999999998
- type: recall_at_1
value: 56.328
- type: recall_at_10
value: 85.8
- type: recall_at_100
value: 96.167
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 72.433
- type: recall_at_5
value: 78.972
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.8019801980198
- type: cos_sim_ap
value: 94.92527097094644
- type: cos_sim_f1
value: 89.91935483870968
- type: cos_sim_precision
value: 90.65040650406505
- type: cos_sim_recall
value: 89.2
- type: dot_accuracy
value: 99.51782178217822
- type: dot_ap
value: 81.30756869559929
- type: dot_f1
value: 75.88235294117648
- type: dot_precision
value: 74.42307692307692
- type: dot_recall
value: 77.4
- type: euclidean_accuracy
value: 99.73069306930694
- type: euclidean_ap
value: 91.05040371796932
- type: euclidean_f1
value: 85.7889237199582
- type: euclidean_precision
value: 89.82494529540482
- type: euclidean_recall
value: 82.1
- type: manhattan_accuracy
value: 99.73762376237623
- type: manhattan_ap
value: 91.4823412839869
- type: manhattan_f1
value: 86.39836984207845
- type: manhattan_precision
value: 88.05815160955348
- type: manhattan_recall
value: 84.8
- type: max_accuracy
value: 99.8019801980198
- type: max_ap
value: 94.92527097094644
- type: max_f1
value: 89.91935483870968
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 55.13046832022158
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 34.31252463546675
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 51.06639688231414
- type: mrr
value: 51.80205415499534
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 31.963331462886957
- type: cos_sim_spearman
value: 33.59510652629926
- type: dot_pearson
value: 29.033733540882123
- type: dot_spearman
value: 31.550290638315504
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.23600000000000002
- type: map_at_10
value: 2.09
- type: map_at_100
value: 12.466000000000001
- type: map_at_1000
value: 29.852
- type: map_at_3
value: 0.6859999999999999
- type: map_at_5
value: 1.099
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 94.0
- type: mrr_at_100
value: 94.0
- type: mrr_at_1000
value: 94.0
- type: mrr_at_3
value: 94.0
- type: mrr_at_5
value: 94.0
- type: ndcg_at_1
value: 86.0
- type: ndcg_at_10
value: 81.368
- type: ndcg_at_100
value: 61.879
- type: ndcg_at_1000
value: 55.282
- type: ndcg_at_3
value: 84.816
- type: ndcg_at_5
value: 82.503
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 85.6
- type: precision_at_100
value: 63.85999999999999
- type: precision_at_1000
value: 24.682000000000002
- type: precision_at_3
value: 88.667
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value: 86.0
- type: recall_at_1
value: 0.23600000000000002
- type: recall_at_10
value: 2.25
- type: recall_at_100
value: 15.488
- type: recall_at_1000
value: 52.196
- type: recall_at_3
value: 0.721
- type: recall_at_5
value: 1.159
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: sqi-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 12.7
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value: 9.805277385275312
- type: recall
value: 12.7
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 30.63583815028902
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value: 24.623726947426373
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value: 22.987809919828013
- type: recall
value: 30.63583815028902
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 10.487804878048781
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value: 7.649047253615001
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value: 10.487804878048781
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 8.5
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value: 8.5
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: deu-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 73.0
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value: 70.10046605876393
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value: 69.0018253968254
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value: 73.0
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: nld-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 32.7
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value: 28.81671359506905
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value: 32.7
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ron-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 31.5
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value: 31.5
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ang-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 26.971643613434658
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value: 35.82089552238806
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ido-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 49.8
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value: 41.944763440988936
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value: 49.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: jav-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 18.536585365853657
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: isl-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 8.7
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value: 8.7
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: slv-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 9.571444036679436
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value: 12.879708383961116
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: cym-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 9.217391304347826
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value: 6.476093529199119
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value: 9.217391304347826
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: kaz-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: est-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 6.9
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value: 6.9
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: heb-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: gla-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 1.9072775486461648
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value: 3.0156815440289506
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: mar-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 49.0
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value: 49.0
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 33.5
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value: 33.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: bel-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 10.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: pms-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 38.095238095238095
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: gle-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 4.8
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value: 4.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: pes-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 15.8
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value: 15.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 23.3
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 24.9
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value: 24.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: cbk-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 70.1
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value: 70.1
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: hun-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 7.199999999999999
- task:
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dataset:
type: mteb/tatoeba-bitext-mining
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config: uig-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 1.7999999999999998
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: rus-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 65.5
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value: 65.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: spa-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 95.7
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value: 95.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: hye-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 0.8086253369272237
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dataset:
type: mteb/tatoeba-bitext-mining
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config: tel-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 63.03795877325289
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value: 69.23076923076923
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dataset:
type: mteb/tatoeba-bitext-mining
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config: afr-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 15.598352437967069
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value: 20.599999999999998
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 4.318181818181818
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value: 2.6787861417537147
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value: 4.318181818181818
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: arz-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 74.84276729559748
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value: 70.6638714185884
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value: 68.86792452830188
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value: 74.84276729559748
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 15.9
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value: 12.793698974586706
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value: 12.088118017657736
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value: 15.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 59.92217898832685
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value: 52.23086900129701
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value: 49.25853869433636
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value: 59.92217898832685
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gsw-eng)
config: gsw-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 27.350427350427353
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value: 21.033781033781032
- type: precision
value: 19.337955491801644
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value: 27.350427350427353
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 29.299999999999997
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value: 23.91597452425777
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value: 22.36696598364942
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value: 29.299999999999997
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 27.3
- type: f1
value: 22.059393517688886
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value: 20.503235534170887
- type: recall
value: 27.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 8.177570093457943
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value: 4.714367017906037
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value: 4.163882933965758
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value: 8.177570093457943
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 5.800000000000001
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value: 4.4859357432293825
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value: 4.247814465614043
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value: 5.800000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 78.4
- type: f1
value: 73.67166666666667
- type: precision
value: 71.83285714285714
- type: recall
value: 78.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 50.3
- type: f1
value: 44.85221545883311
- type: precision
value: 43.04913026243909
- type: recall
value: 50.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 83.5
- type: f1
value: 79.95151515151515
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value: 78.53611111111111
- type: recall
value: 83.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 69.89999999999999
- type: f1
value: 65.03756269256269
- type: precision
value: 63.233519536019536
- type: recall
value: 69.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.44666666666666
- type: precision
value: 90.63333333333333
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 8.3
- type: f1
value: 6.553388144729963
- type: precision
value: 6.313497782829976
- type: recall
value: 8.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 83.6
- type: f1
value: 79.86243107769424
- type: precision
value: 78.32555555555555
- type: recall
value: 83.6
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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config: yue-eng
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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config: jpn-eng
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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dataset:
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metrics:
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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config: orv-eng
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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dataset:
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config: tuk-eng
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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dataset:
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config: max-eng
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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dataset:
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config: swh-eng
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revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 20.76923076923077
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dataset:
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config: hin-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 88.1
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value: 88.1
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type: BitextMining
dataset:
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config: dsb-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 11.899791231732777
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dataset:
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config: ber-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 6.1
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value: 6.1
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: tam-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 76.54723127035831
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dataset:
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config: slk-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
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value: 11.700000000000001
- type: f1
value: 9.975679190026007
- type: precision
value: 9.569927715653522
- type: recall
value: 11.700000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 13.100000000000001
- type: f1
value: 10.697335850115408
- type: precision
value: 10.113816082086341
- type: recall
value: 13.100000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 76.37795275590551
- type: f1
value: 71.12860892388451
- type: precision
value: 68.89763779527559
- type: recall
value: 76.37795275590551
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 13.700000000000001
- type: f1
value: 10.471861684067568
- type: precision
value: 9.602902567641697
- type: recall
value: 13.700000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 0.554016620498615
- type: f1
value: 0.37034084643642423
- type: precision
value: 0.34676040281208437
- type: recall
value: 0.554016620498615
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 12.4
- type: f1
value: 9.552607451092534
- type: precision
value: 8.985175505050504
- type: recall
value: 12.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 33.65384615384615
- type: f1
value: 27.820512820512818
- type: precision
value: 26.09432234432234
- type: recall
value: 33.65384615384615
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 74.5
- type: f1
value: 70.09686507936507
- type: precision
value: 68.3117857142857
- type: recall
value: 74.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 88.3
- type: f1
value: 85.37333333333333
- type: precision
value: 84.05833333333334
- type: recall
value: 88.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 25.0
- type: f1
value: 22.393124632031995
- type: precision
value: 21.58347686592367
- type: recall
value: 25.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 0.589622641509434
- type: f1
value: 0.15804980033762941
- type: precision
value: 0.1393275384872965
- type: recall
value: 0.589622641509434
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 4.1000000000000005
- type: f1
value: 3.4069011332551775
- type: precision
value: 3.1784507042253516
- type: recall
value: 4.1000000000000005
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 3.102189781021898
- type: f1
value: 2.223851811694751
- type: precision
value: 2.103465682299194
- type: recall
value: 3.102189781021898
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: ed9e4a974f867fd9736efcf222fc3a26487387a5
metrics:
- type: accuracy
value: 83.1
- type: f1
value: 79.58255835667599
- type: precision
value: 78.09708333333333
- type: recall
value: 83.1
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 2.322
- type: map_at_10
value: 8.959999999999999
- type: map_at_100
value: 15.136
- type: map_at_1000
value: 16.694
- type: map_at_3
value: 4.837000000000001
- type: map_at_5
value: 6.196
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 47.589999999999996
- type: mrr_at_100
value: 48.166
- type: mrr_at_1000
value: 48.169000000000004
- type: mrr_at_3
value: 43.197
- type: mrr_at_5
value: 45.646
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 23.982
- type: ndcg_at_100
value: 35.519
- type: ndcg_at_1000
value: 46.878
- type: ndcg_at_3
value: 26.801000000000002
- type: ndcg_at_5
value: 24.879
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 22.041
- type: precision_at_100
value: 7.4079999999999995
- type: precision_at_1000
value: 1.492
- type: precision_at_3
value: 28.571
- type: precision_at_5
value: 25.306
- type: recall_at_1
value: 2.322
- type: recall_at_10
value: 15.443999999999999
- type: recall_at_100
value: 45.918
- type: recall_at_1000
value: 79.952
- type: recall_at_3
value: 6.143
- type: recall_at_5
value: 8.737
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 66.5452
- type: ap
value: 12.99191723223892
- type: f1
value: 51.667665096195734
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 55.854555744199196
- type: f1
value: 56.131766302254185
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 37.27891385518074
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.53102461703523
- type: cos_sim_ap
value: 65.30753664579191
- type: cos_sim_f1
value: 61.739943872778305
- type: cos_sim_precision
value: 55.438891222175556
- type: cos_sim_recall
value: 69.65699208443272
- type: dot_accuracy
value: 80.38981939560112
- type: dot_ap
value: 53.52081118421347
- type: dot_f1
value: 54.232957844617346
- type: dot_precision
value: 48.43393486828459
- type: dot_recall
value: 61.60949868073878
- type: euclidean_accuracy
value: 82.23758717291531
- type: euclidean_ap
value: 60.361102792772535
- type: euclidean_f1
value: 57.50518791791561
- type: euclidean_precision
value: 51.06470106470107
- type: euclidean_recall
value: 65.8047493403694
- type: manhattan_accuracy
value: 82.14221851344102
- type: manhattan_ap
value: 60.341937223793366
- type: manhattan_f1
value: 57.53803596127247
- type: manhattan_precision
value: 51.08473188702415
- type: manhattan_recall
value: 65.85751978891821
- type: max_accuracy
value: 83.53102461703523
- type: max_ap
value: 65.30753664579191
- type: max_f1
value: 61.739943872778305
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.75305623471883
- type: cos_sim_ap
value: 85.46387153880272
- type: cos_sim_f1
value: 77.91527673159008
- type: cos_sim_precision
value: 72.93667315828353
- type: cos_sim_recall
value: 83.62334462580844
- type: dot_accuracy
value: 85.08169363915086
- type: dot_ap
value: 74.96808060965559
- type: dot_f1
value: 71.39685033990366
- type: dot_precision
value: 64.16948111759288
- type: dot_recall
value: 80.45888512473051
- type: euclidean_accuracy
value: 85.84235650250321
- type: euclidean_ap
value: 78.42045145247211
- type: euclidean_f1
value: 70.32669630775179
- type: euclidean_precision
value: 70.6298050788227
- type: euclidean_recall
value: 70.02617801047121
- type: manhattan_accuracy
value: 85.86176116738464
- type: manhattan_ap
value: 78.54012451558276
- type: manhattan_f1
value: 70.56508080693389
- type: manhattan_precision
value: 69.39626293456413
- type: manhattan_recall
value: 71.77394518016631
- type: max_accuracy
value: 88.75305623471883
- type: max_ap
value: 85.46387153880272
- type: max_f1
value: 77.91527673159008
---
## Usage
For usage instructions, refer to: https://github.com/Muennighoff/sgpt#asymmetric-semantic-search-be
The model was trained with the command
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch examples/training/ms_marco/train_bi-encoder_mnrl.py --model_name bigscience/bloom-7b1 --train_batch_size 32 --eval_batch_size 16 --freezenonbias --specb --lr 4e-4 --wandb --wandbwatchlog gradients --pooling weightedmean --gradcache --chunksize 8
```
## Evaluation Results
`{"ndcgs": {"sgpt-bloom-7b1-msmarco": {"scifact": {"NDCG@10": 0.71824}, "nfcorpus": {"NDCG@10": 0.35748}, "arguana": {"NDCG@10": 0.47281}, "scidocs": {"NDCG@10": 0.18435}, "fiqa": {"NDCG@10": 0.35736}, "cqadupstack": {"NDCG@10": 0.3708525}, "quora": {"NDCG@10": 0.74655}, "trec-covid": {"NDCG@10": 0.82731}, "webis-touche2020": {"NDCG@10": 0.2365}}}`
See the evaluation folder or [MTEB](https://huggingface.co/spaces/mteb/leaderboard) for more results.
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
The model uses BitFit, weighted-mean pooling & GradCache, for details see: https://arxiv.org/abs/2202.08904
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0004
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: BloomModel
(1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
``` |
mradermacher/Mistral-7B-v0.3-i1-GGUF | mradermacher | 2024-06-09T23:06:57Z | 4,978 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-09T21:54:53Z | ---
base_model: mistralai/Mistral-7B-v0.3
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/mistralai/Mistral-7B-v0.3
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Mistral-7B-v0.3-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.3-i1-GGUF/resolve/main/Mistral-7B-v0.3.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
luhua/chinese_pretrain_mrc_roberta_wwm_ext_large | luhua | 2021-06-12T02:53:16Z | 4,977 | 75 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language:
- zh
license: "apache-2.0"
---
## Chinese MRC roberta_wwm_ext_large
* 使用大量中文MRC数据训练的roberta_wwm_ext_large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/>
（已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁）
| 模型/数据集 | Dureader-2021 | tencentmedical |
| ------------------------------------------|--------------- | --------------- |
| | F1-score | Accuracy |
| | dev / A榜 | test-1 |
| macbert-large (哈工大预训练语言模型) | 65.49 / 64.27 | 82.5 |
| roberta-wwm-ext-large (哈工大预训练语言模型) | 65.49 / 64.27 | 82.5 |
| macbert-large (ours) | 70.45 / **68.13**| **83.4** |
| roberta-wwm-ext-large (ours) | 68.91 / 66.91 | 83.1 |
|
PrunaAI/gradientai-Llama-3-8B-Instruct-Gradient-1048k-AWQ-4bit-smashed | PrunaAI | 2024-05-01T20:45:04Z | 4,976 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:gradientai/Llama-3-8B-Instruct-Gradient-1048k",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-05-01T19:58:24Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: gradientai/Llama-3-8B-Instruct-Gradient-1048k
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with awq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo gradientai/Llama-3-8B-Instruct-Gradient-1048k installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/gradientai-Llama-3-8B-Instruct-1048k-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("gradientai/Llama-3-8B-Instruct-Gradient-1048k")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model gradientai/Llama-3-8B-Instruct-Gradient-1048k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
TheBloke/Llama-2-70B-GGUF | TheBloke | 2023-09-27T12:47:14Z | 4,975 | 24 | transformers | [
"transformers",
"gguf",
"llama",
"facebook",
"meta",
"pytorch",
"llama-2",
"text-generation",
"en",
"arxiv:2307.09288",
"base_model:meta-llama/Llama-2-70b-hf",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | 2023-09-04T19:11:51Z | ---
language:
- en
license: llama2
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
model_name: Llama 2 70B
base_model: meta-llama/Llama-2-70b-hf
inference: false
model_creator: Meta Llama 2
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 70B - GGUF
- Model creator: [Meta Llama 2](https://huggingface.co/meta-llama)
- Original model: [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Meta Llama 2's Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-70B-GGUF)
* [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-70b-hf)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-2-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [llama-2-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [llama-2-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [llama-2-70b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-2-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [llama-2-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [llama-2-70b.Q5_0.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-2-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [llama-2-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-70B-GGUF/blob/main/llama-2-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| llama-2-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| llama-2-70b.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `llama-2-70b.Q6_K.gguf-split-a`
* `llama-2-70b.Q6_K.gguf-split-b`
### q8_0
Please download:
* `llama-2-70b.Q8_0.gguf-split-a`
* `llama-2-70b.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat llama-2-70b.Q6_K.gguf-split-* > llama-2-70b.Q6_K.gguf && rm llama-2-70b.Q6_K.gguf-split-*
cat llama-2-70b.Q8_0.gguf-split-* > llama-2-70b.Q8_0.gguf && rm llama-2-70b.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B llama-2-70b.Q6_K.gguf-split-a + llama-2-70b.Q6_K.gguf-split-b llama-2-70b.Q6_K.gguf
del llama-2-70b.Q6_K.gguf-split-a llama-2-70b.Q6_K.gguf-split-b
COPY /B llama-2-70b.Q8_0.gguf-split-a + llama-2-70b.Q8_0.gguf-split-b llama-2-70b.Q8_0.gguf
del llama-2-70b.Q8_0.gguf-split-a llama-2-70b.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Llama-2-70B-GGUF and below it, a specific filename to download, such as: llama-2-70b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Llama-2-70B-GGUF llama-2-70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Llama-2-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Llama-2-70B-GGUF llama-2-70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m llama-2-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-70B-GGUF", model_file="llama-2-70b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Meta Llama 2's Llama 2 70B
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
<!-- original-model-card end -->
|
mradermacher/Phoenix-i1-GGUF | mradermacher | 2024-06-08T19:44:46Z | 4,974 | 0 | transformers | [
"transformers",
"gguf",
"dpo",
"alignment-handbook",
"de",
"base_model:DRXD1000/Phoenix",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-08T13:13:37Z | ---
base_model: DRXD1000/Phoenix
language:
- de
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- dpo
- alignment-handbook
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/DRXD1000/Phoenix
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Phoenix-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Phoenix-i1-GGUF/resolve/main/Phoenix.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
Labib11/MUG-B-1.6 | Labib11 | 2024-05-21T12:54:58Z | 4,971 | 1 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-05-08T15:46:01Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: MUG-B-1.6
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.04047976011994
- type: ap
value: 23.622442298323236
- type: f1
value: 61.681362134359354
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.38805970149255
- type: ap
value: 35.14527522183942
- type: f1
value: 66.40004634079556
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 54.3254817987152
- type: ap
value: 71.95259605308317
- type: f1
value: 52.50731386267296
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 56.33832976445397
- type: ap
value: 12.671021199223937
- type: f1
value: 46.127586182990605
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.70805000000001
- type: ap
value: 90.58639913354553
- type: f1
value: 93.69822635061847
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.85000000000001
- type: f1
value: 49.80013009020246
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 27.203999999999994
- type: f1
value: 26.60134413072989
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.878
- type: f1
value: 33.072592092252314
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.557999999999993
- type: f1
value: 30.866094552542624
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.706
- type: f1
value: 22.23195837325246
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.349999999999998
- type: f1
value: 21.80183891680617
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 41.892
- type: map_at_10
value: 57.989999999999995
- type: map_at_100
value: 58.45
- type: map_at_1000
value: 58.453
- type: map_at_20
value: 58.392999999999994
- type: map_at_3
value: 53.746
- type: map_at_5
value: 56.566
- type: mrr_at_1
value: 43.314
- type: mrr_at_10
value: 58.535000000000004
- type: mrr_at_100
value: 58.975
- type: mrr_at_1000
value: 58.977999999999994
- type: mrr_at_20
value: 58.916999999999994
- type: mrr_at_3
value: 54.303000000000004
- type: mrr_at_5
value: 57.055
- type: ndcg_at_1
value: 41.892
- type: ndcg_at_10
value: 66.176
- type: ndcg_at_100
value: 67.958
- type: ndcg_at_1000
value: 68.00699999999999
- type: ndcg_at_20
value: 67.565
- type: ndcg_at_3
value: 57.691
- type: ndcg_at_5
value: 62.766
- type: precision_at_1
value: 41.892
- type: precision_at_10
value: 9.189
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.861
- type: precision_at_3
value: 23.044
- type: precision_at_5
value: 16.287
- type: recall_at_1
value: 41.892
- type: recall_at_10
value: 91.892
- type: recall_at_100
value: 99.289
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 97.226
- type: recall_at_3
value: 69.132
- type: recall_at_5
value: 81.437
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 49.03486273664411
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.04797567338598
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29499572176032
- type: mrr
value: 77.28861627753592
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.53248242133246
- type: cos_sim_spearman
value: 88.38032705871927
- type: euclidean_pearson
value: 87.77994445569084
- type: euclidean_spearman
value: 88.38032705871927
- type: manhattan_pearson
value: 87.52369210088627
- type: manhattan_spearman
value: 88.27972235673434
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.4090909090909
- type: f1
value: 84.87743757972068
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.73840151083438
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.565075977998966
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.082
- type: map_at_10
value: 44.787
- type: map_at_100
value: 46.322
- type: map_at_1000
value: 46.446
- type: map_at_20
value: 45.572
- type: map_at_3
value: 40.913
- type: map_at_5
value: 42.922
- type: mrr_at_1
value: 40.629
- type: mrr_at_10
value: 51.119
- type: mrr_at_100
value: 51.783
- type: mrr_at_1000
value: 51.82
- type: mrr_at_20
value: 51.49700000000001
- type: mrr_at_3
value: 48.355
- type: mrr_at_5
value: 49.979
- type: ndcg_at_1
value: 40.629
- type: ndcg_at_10
value: 51.647
- type: ndcg_at_100
value: 56.923
- type: ndcg_at_1000
value: 58.682
- type: ndcg_at_20
value: 53.457
- type: ndcg_at_3
value: 46.065
- type: ndcg_at_5
value: 48.352000000000004
- type: precision_at_1
value: 40.629
- type: precision_at_10
value: 10.072000000000001
- type: precision_at_100
value: 1.5939999999999999
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_20
value: 5.908
- type: precision_at_3
value: 22.222
- type: precision_at_5
value: 15.937000000000001
- type: recall_at_1
value: 33.082
- type: recall_at_10
value: 64.55300000000001
- type: recall_at_100
value: 86.86399999999999
- type: recall_at_1000
value: 97.667
- type: recall_at_20
value: 70.988
- type: recall_at_3
value: 48.067
- type: recall_at_5
value: 54.763
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 32.272
- type: map_at_10
value: 42.620000000000005
- type: map_at_100
value: 43.936
- type: map_at_1000
value: 44.066
- type: map_at_20
value: 43.349
- type: map_at_3
value: 39.458
- type: map_at_5
value: 41.351
- type: mrr_at_1
value: 40.127
- type: mrr_at_10
value: 48.437000000000005
- type: mrr_at_100
value: 49.096000000000004
- type: mrr_at_1000
value: 49.14
- type: mrr_at_20
value: 48.847
- type: mrr_at_3
value: 46.21
- type: mrr_at_5
value: 47.561
- type: ndcg_at_1
value: 40.127
- type: ndcg_at_10
value: 48.209999999999994
- type: ndcg_at_100
value: 52.632
- type: ndcg_at_1000
value: 54.59
- type: ndcg_at_20
value: 50.012
- type: ndcg_at_3
value: 43.996
- type: ndcg_at_5
value: 46.122
- type: precision_at_1
value: 40.127
- type: precision_at_10
value: 9.051
- type: precision_at_100
value: 1.465
- type: precision_at_1000
value: 0.193
- type: precision_at_20
value: 5.35
- type: precision_at_3
value: 21.104
- type: precision_at_5
value: 15.146
- type: recall_at_1
value: 32.272
- type: recall_at_10
value: 57.870999999999995
- type: recall_at_100
value: 76.211
- type: recall_at_1000
value: 88.389
- type: recall_at_20
value: 64.354
- type: recall_at_3
value: 45.426
- type: recall_at_5
value: 51.23799999999999
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 40.261
- type: map_at_10
value: 53.400000000000006
- type: map_at_100
value: 54.42399999999999
- type: map_at_1000
value: 54.473000000000006
- type: map_at_20
value: 54.052
- type: map_at_3
value: 49.763000000000005
- type: map_at_5
value: 51.878
- type: mrr_at_1
value: 46.019
- type: mrr_at_10
value: 56.653
- type: mrr_at_100
value: 57.28
- type: mrr_at_1000
value: 57.303000000000004
- type: mrr_at_20
value: 57.057
- type: mrr_at_3
value: 53.971000000000004
- type: mrr_at_5
value: 55.632000000000005
- type: ndcg_at_1
value: 46.019
- type: ndcg_at_10
value: 59.597
- type: ndcg_at_100
value: 63.452
- type: ndcg_at_1000
value: 64.434
- type: ndcg_at_20
value: 61.404
- type: ndcg_at_3
value: 53.620999999999995
- type: ndcg_at_5
value: 56.688
- type: precision_at_1
value: 46.019
- type: precision_at_10
value: 9.748999999999999
- type: precision_at_100
value: 1.261
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_20
value: 5.436
- type: precision_at_3
value: 24.075
- type: precision_at_5
value: 16.715
- type: recall_at_1
value: 40.261
- type: recall_at_10
value: 74.522
- type: recall_at_100
value: 91.014
- type: recall_at_1000
value: 98.017
- type: recall_at_20
value: 81.186
- type: recall_at_3
value: 58.72500000000001
- type: recall_at_5
value: 66.23599999999999
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.666
- type: map_at_10
value: 36.744
- type: map_at_100
value: 37.794
- type: map_at_1000
value: 37.865
- type: map_at_20
value: 37.336999999999996
- type: map_at_3
value: 33.833999999999996
- type: map_at_5
value: 35.61
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 38.838
- type: mrr_at_100
value: 39.765
- type: mrr_at_1000
value: 39.818999999999996
- type: mrr_at_20
value: 39.373000000000005
- type: mrr_at_3
value: 36.234
- type: mrr_at_5
value: 37.844
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 41.986000000000004
- type: ndcg_at_100
value: 47.05
- type: ndcg_at_1000
value: 48.897
- type: ndcg_at_20
value: 43.989
- type: ndcg_at_3
value: 36.452
- type: ndcg_at_5
value: 39.395
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.4750000000000005
- type: precision_at_100
value: 0.946
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 3.6839999999999997
- type: precision_at_3
value: 15.443000000000001
- type: precision_at_5
value: 10.96
- type: recall_at_1
value: 27.666
- type: recall_at_10
value: 56.172999999999995
- type: recall_at_100
value: 79.142
- type: recall_at_1000
value: 93.013
- type: recall_at_20
value: 63.695
- type: recall_at_3
value: 41.285
- type: recall_at_5
value: 48.36
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 17.939
- type: map_at_10
value: 27.301
- type: map_at_100
value: 28.485
- type: map_at_1000
value: 28.616000000000003
- type: map_at_20
value: 27.843
- type: map_at_3
value: 24.342
- type: map_at_5
value: 26.259
- type: mrr_at_1
value: 22.761
- type: mrr_at_10
value: 32.391
- type: mrr_at_100
value: 33.297
- type: mrr_at_1000
value: 33.361000000000004
- type: mrr_at_20
value: 32.845
- type: mrr_at_3
value: 29.498
- type: mrr_at_5
value: 31.375999999999998
- type: ndcg_at_1
value: 22.761
- type: ndcg_at_10
value: 33.036
- type: ndcg_at_100
value: 38.743
- type: ndcg_at_1000
value: 41.568
- type: ndcg_at_20
value: 34.838
- type: ndcg_at_3
value: 27.803
- type: ndcg_at_5
value: 30.781
- type: precision_at_1
value: 22.761
- type: precision_at_10
value: 6.132
- type: precision_at_100
value: 1.031
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 3.582
- type: precision_at_3
value: 13.474
- type: precision_at_5
value: 10.123999999999999
- type: recall_at_1
value: 17.939
- type: recall_at_10
value: 45.515
- type: recall_at_100
value: 70.56700000000001
- type: recall_at_1000
value: 90.306
- type: recall_at_20
value: 51.946999999999996
- type: recall_at_3
value: 31.459
- type: recall_at_5
value: 39.007
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.156
- type: map_at_10
value: 42.317
- type: map_at_100
value: 43.742
- type: map_at_1000
value: 43.852000000000004
- type: map_at_20
value: 43.147999999999996
- type: map_at_3
value: 38.981
- type: map_at_5
value: 40.827000000000005
- type: mrr_at_1
value: 38.401999999999994
- type: mrr_at_10
value: 48.141
- type: mrr_at_100
value: 48.991
- type: mrr_at_1000
value: 49.03
- type: mrr_at_20
value: 48.665000000000006
- type: mrr_at_3
value: 45.684999999999995
- type: mrr_at_5
value: 47.042
- type: ndcg_at_1
value: 38.401999999999994
- type: ndcg_at_10
value: 48.541000000000004
- type: ndcg_at_100
value: 54.063
- type: ndcg_at_1000
value: 56.005
- type: ndcg_at_20
value: 50.895999999999994
- type: ndcg_at_3
value: 43.352000000000004
- type: ndcg_at_5
value: 45.769
- type: precision_at_1
value: 38.401999999999994
- type: precision_at_10
value: 8.738999999999999
- type: precision_at_100
value: 1.335
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_20
value: 5.164
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.437
- type: recall_at_1
value: 31.156
- type: recall_at_10
value: 61.172000000000004
- type: recall_at_100
value: 83.772
- type: recall_at_1000
value: 96.192
- type: recall_at_20
value: 69.223
- type: recall_at_3
value: 46.628
- type: recall_at_5
value: 53.032000000000004
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 26.741999999999997
- type: map_at_10
value: 36.937
- type: map_at_100
value: 38.452
- type: map_at_1000
value: 38.557
- type: map_at_20
value: 37.858999999999995
- type: map_at_3
value: 33.579
- type: map_at_5
value: 35.415
- type: mrr_at_1
value: 32.991
- type: mrr_at_10
value: 42.297000000000004
- type: mrr_at_100
value: 43.282
- type: mrr_at_1000
value: 43.332
- type: mrr_at_20
value: 42.95
- type: mrr_at_3
value: 39.707
- type: mrr_at_5
value: 41.162
- type: ndcg_at_1
value: 32.991
- type: ndcg_at_10
value: 43.004999999999995
- type: ndcg_at_100
value: 49.053000000000004
- type: ndcg_at_1000
value: 51.166999999999994
- type: ndcg_at_20
value: 45.785
- type: ndcg_at_3
value: 37.589
- type: ndcg_at_5
value: 40.007999999999996
- type: precision_at_1
value: 32.991
- type: precision_at_10
value: 8.025
- type: precision_at_100
value: 1.268
- type: precision_at_1000
value: 0.163
- type: precision_at_20
value: 4.846
- type: precision_at_3
value: 17.922
- type: precision_at_5
value: 13.059000000000001
- type: recall_at_1
value: 26.741999999999997
- type: recall_at_10
value: 55.635999999999996
- type: recall_at_100
value: 80.798
- type: recall_at_1000
value: 94.918
- type: recall_at_20
value: 65.577
- type: recall_at_3
value: 40.658
- type: recall_at_5
value: 46.812
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 27.274583333333336
- type: map_at_10
value: 37.04091666666666
- type: map_at_100
value: 38.27966666666667
- type: map_at_1000
value: 38.39383333333334
- type: map_at_20
value: 37.721500000000006
- type: map_at_3
value: 33.937999999999995
- type: map_at_5
value: 35.67974999999999
- type: mrr_at_1
value: 32.40525
- type: mrr_at_10
value: 41.43925000000001
- type: mrr_at_100
value: 42.271
- type: mrr_at_1000
value: 42.32416666666667
- type: mrr_at_20
value: 41.92733333333334
- type: mrr_at_3
value: 38.84941666666666
- type: mrr_at_5
value: 40.379583333333336
- type: ndcg_at_1
value: 32.40525
- type: ndcg_at_10
value: 42.73808333333334
- type: ndcg_at_100
value: 47.88941666666667
- type: ndcg_at_1000
value: 50.05008333333334
- type: ndcg_at_20
value: 44.74183333333334
- type: ndcg_at_3
value: 37.51908333333334
- type: ndcg_at_5
value: 40.01883333333333
- type: precision_at_1
value: 32.40525
- type: precision_at_10
value: 7.5361666666666665
- type: precision_at_100
value: 1.1934166666666666
- type: precision_at_1000
value: 0.1575
- type: precision_at_20
value: 4.429166666666667
- type: precision_at_3
value: 17.24941666666667
- type: precision_at_5
value: 12.362333333333336
- type: recall_at_1
value: 27.274583333333336
- type: recall_at_10
value: 55.21358333333334
- type: recall_at_100
value: 77.60366666666667
- type: recall_at_1000
value: 92.43691666666666
- type: recall_at_20
value: 62.474583333333335
- type: recall_at_3
value: 40.79375
- type: recall_at_5
value: 47.15158333333334
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 27.389999999999997
- type: map_at_10
value: 34.107
- type: map_at_100
value: 35.022999999999996
- type: map_at_1000
value: 35.13
- type: map_at_20
value: 34.605999999999995
- type: map_at_3
value: 32.021
- type: map_at_5
value: 32.948
- type: mrr_at_1
value: 30.982
- type: mrr_at_10
value: 37.345
- type: mrr_at_100
value: 38.096999999999994
- type: mrr_at_1000
value: 38.179
- type: mrr_at_20
value: 37.769000000000005
- type: mrr_at_3
value: 35.481
- type: mrr_at_5
value: 36.293
- type: ndcg_at_1
value: 30.982
- type: ndcg_at_10
value: 38.223
- type: ndcg_at_100
value: 42.686
- type: ndcg_at_1000
value: 45.352
- type: ndcg_at_20
value: 39.889
- type: ndcg_at_3
value: 34.259
- type: ndcg_at_5
value: 35.664
- type: precision_at_1
value: 30.982
- type: precision_at_10
value: 5.7669999999999995
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_20
value: 3.3360000000000003
- type: precision_at_3
value: 14.264
- type: precision_at_5
value: 9.54
- type: recall_at_1
value: 27.389999999999997
- type: recall_at_10
value: 48.009
- type: recall_at_100
value: 68.244
- type: recall_at_1000
value: 87.943
- type: recall_at_20
value: 54.064
- type: recall_at_3
value: 36.813
- type: recall_at_5
value: 40.321
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.249000000000002
- type: map_at_10
value: 25.907000000000004
- type: map_at_100
value: 27.105
- type: map_at_1000
value: 27.233
- type: map_at_20
value: 26.541999999999998
- type: map_at_3
value: 23.376
- type: map_at_5
value: 24.673000000000002
- type: mrr_at_1
value: 21.989
- type: mrr_at_10
value: 29.846
- type: mrr_at_100
value: 30.808999999999997
- type: mrr_at_1000
value: 30.885
- type: mrr_at_20
value: 30.384
- type: mrr_at_3
value: 27.46
- type: mrr_at_5
value: 28.758
- type: ndcg_at_1
value: 21.989
- type: ndcg_at_10
value: 30.874000000000002
- type: ndcg_at_100
value: 36.504999999999995
- type: ndcg_at_1000
value: 39.314
- type: ndcg_at_20
value: 32.952999999999996
- type: ndcg_at_3
value: 26.249
- type: ndcg_at_5
value: 28.229
- type: precision_at_1
value: 21.989
- type: precision_at_10
value: 5.705
- type: precision_at_100
value: 0.9990000000000001
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 3.4459999999999997
- type: precision_at_3
value: 12.377
- type: precision_at_5
value: 8.961
- type: recall_at_1
value: 18.249000000000002
- type: recall_at_10
value: 41.824
- type: recall_at_100
value: 67.071
- type: recall_at_1000
value: 86.863
- type: recall_at_20
value: 49.573
- type: recall_at_3
value: 28.92
- type: recall_at_5
value: 34.003
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 26.602999999999998
- type: map_at_10
value: 36.818
- type: map_at_100
value: 37.894
- type: map_at_1000
value: 37.991
- type: map_at_20
value: 37.389
- type: map_at_3
value: 33.615
- type: map_at_5
value: 35.432
- type: mrr_at_1
value: 31.53
- type: mrr_at_10
value: 41.144
- type: mrr_at_100
value: 41.937999999999995
- type: mrr_at_1000
value: 41.993
- type: mrr_at_20
value: 41.585
- type: mrr_at_3
value: 38.385999999999996
- type: mrr_at_5
value: 39.995000000000005
- type: ndcg_at_1
value: 31.53
- type: ndcg_at_10
value: 42.792
- type: ndcg_at_100
value: 47.749
- type: ndcg_at_1000
value: 49.946
- type: ndcg_at_20
value: 44.59
- type: ndcg_at_3
value: 37.025000000000006
- type: ndcg_at_5
value: 39.811
- type: precision_at_1
value: 31.53
- type: precision_at_10
value: 7.2669999999999995
- type: precision_at_100
value: 1.109
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 4.184
- type: precision_at_3
value: 16.791
- type: precision_at_5
value: 12.09
- type: recall_at_1
value: 26.602999999999998
- type: recall_at_10
value: 56.730999999999995
- type: recall_at_100
value: 78.119
- type: recall_at_1000
value: 93.458
- type: recall_at_20
value: 63.00599999999999
- type: recall_at_3
value: 41.306
- type: recall_at_5
value: 48.004999999999995
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 23.988
- type: map_at_10
value: 33.650999999999996
- type: map_at_100
value: 35.263
- type: map_at_1000
value: 35.481
- type: map_at_20
value: 34.463
- type: map_at_3
value: 30.330000000000002
- type: map_at_5
value: 32.056000000000004
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 38.987
- type: mrr_at_100
value: 39.973
- type: mrr_at_1000
value: 40.013
- type: mrr_at_20
value: 39.553
- type: mrr_at_3
value: 36.001
- type: mrr_at_5
value: 37.869
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 40.156
- type: ndcg_at_100
value: 46.244
- type: ndcg_at_1000
value: 48.483
- type: ndcg_at_20
value: 42.311
- type: ndcg_at_3
value: 34.492
- type: ndcg_at_5
value: 37.118
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 7.925
- type: precision_at_100
value: 1.5890000000000002
- type: precision_at_1000
value: 0.245
- type: precision_at_20
value: 4.97
- type: precision_at_3
value: 16.469
- type: precision_at_5
value: 12.174
- type: recall_at_1
value: 23.988
- type: recall_at_10
value: 52.844
- type: recall_at_100
value: 80.143
- type: recall_at_1000
value: 93.884
- type: recall_at_20
value: 61.050000000000004
- type: recall_at_3
value: 36.720000000000006
- type: recall_at_5
value: 43.614999999999995
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 21.947
- type: map_at_10
value: 29.902
- type: map_at_100
value: 30.916
- type: map_at_1000
value: 31.016
- type: map_at_20
value: 30.497999999999998
- type: map_at_3
value: 27.044
- type: map_at_5
value: 28.786
- type: mrr_at_1
value: 23.845
- type: mrr_at_10
value: 32.073
- type: mrr_at_100
value: 32.940999999999995
- type: mrr_at_1000
value: 33.015
- type: mrr_at_20
value: 32.603
- type: mrr_at_3
value: 29.205
- type: mrr_at_5
value: 31.044
- type: ndcg_at_1
value: 23.845
- type: ndcg_at_10
value: 34.79
- type: ndcg_at_100
value: 39.573
- type: ndcg_at_1000
value: 42.163000000000004
- type: ndcg_at_20
value: 36.778
- type: ndcg_at_3
value: 29.326
- type: ndcg_at_5
value: 32.289
- type: precision_at_1
value: 23.845
- type: precision_at_10
value: 5.527
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_20
value: 3.2439999999999998
- type: precision_at_3
value: 12.384
- type: precision_at_5
value: 9.205
- type: recall_at_1
value: 21.947
- type: recall_at_10
value: 47.713
- type: recall_at_100
value: 69.299
- type: recall_at_1000
value: 88.593
- type: recall_at_20
value: 55.032000000000004
- type: recall_at_3
value: 33.518
- type: recall_at_5
value: 40.427
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 13.655999999999999
- type: map_at_10
value: 23.954
- type: map_at_100
value: 26.07
- type: map_at_1000
value: 26.266000000000002
- type: map_at_20
value: 25.113000000000003
- type: map_at_3
value: 19.85
- type: map_at_5
value: 21.792
- type: mrr_at_1
value: 31.075000000000003
- type: mrr_at_10
value: 43.480000000000004
- type: mrr_at_100
value: 44.39
- type: mrr_at_1000
value: 44.42
- type: mrr_at_20
value: 44.06
- type: mrr_at_3
value: 40.38
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 31.075000000000003
- type: ndcg_at_10
value: 33.129999999999995
- type: ndcg_at_100
value: 40.794000000000004
- type: ndcg_at_1000
value: 44.062
- type: ndcg_at_20
value: 36.223
- type: ndcg_at_3
value: 27.224999999999998
- type: ndcg_at_5
value: 28.969
- type: precision_at_1
value: 31.075000000000003
- type: precision_at_10
value: 10.476
- type: precision_at_100
value: 1.864
- type: precision_at_1000
value: 0.247
- type: precision_at_20
value: 6.593
- type: precision_at_3
value: 20.456
- type: precision_at_5
value: 15.440000000000001
- type: recall_at_1
value: 13.655999999999999
- type: recall_at_10
value: 39.678000000000004
- type: recall_at_100
value: 65.523
- type: recall_at_1000
value: 83.59100000000001
- type: recall_at_20
value: 48.27
- type: recall_at_3
value: 24.863
- type: recall_at_5
value: 30.453999999999997
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.139
- type: map_at_10
value: 20.366999999999997
- type: map_at_100
value: 29.755
- type: map_at_1000
value: 31.563999999999997
- type: map_at_20
value: 24.021
- type: map_at_3
value: 14.395
- type: map_at_5
value: 16.853
- type: mrr_at_1
value: 69.0
- type: mrr_at_10
value: 76.778
- type: mrr_at_100
value: 77.116
- type: mrr_at_1000
value: 77.12299999999999
- type: mrr_at_20
value: 77.046
- type: mrr_at_3
value: 75.208
- type: mrr_at_5
value: 76.146
- type: ndcg_at_1
value: 57.125
- type: ndcg_at_10
value: 42.84
- type: ndcg_at_100
value: 48.686
- type: ndcg_at_1000
value: 56.294
- type: ndcg_at_20
value: 42.717
- type: ndcg_at_3
value: 46.842
- type: ndcg_at_5
value: 44.248
- type: precision_at_1
value: 69.0
- type: precision_at_10
value: 34.625
- type: precision_at_100
value: 11.468
- type: precision_at_1000
value: 2.17
- type: precision_at_20
value: 26.562
- type: precision_at_3
value: 50.917
- type: precision_at_5
value: 43.35
- type: recall_at_1
value: 9.139
- type: recall_at_10
value: 26.247999999999998
- type: recall_at_100
value: 56.647000000000006
- type: recall_at_1000
value: 80.784
- type: recall_at_20
value: 35.010999999999996
- type: recall_at_3
value: 15.57
- type: recall_at_5
value: 19.198
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 55.93
- type: f1
value: 49.35314406745291
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 73.198
- type: map_at_10
value: 81.736
- type: map_at_100
value: 82.02000000000001
- type: map_at_1000
value: 82.03399999999999
- type: map_at_20
value: 81.937
- type: map_at_3
value: 80.692
- type: map_at_5
value: 81.369
- type: mrr_at_1
value: 78.803
- type: mrr_at_10
value: 86.144
- type: mrr_at_100
value: 86.263
- type: mrr_at_1000
value: 86.26599999999999
- type: mrr_at_20
value: 86.235
- type: mrr_at_3
value: 85.464
- type: mrr_at_5
value: 85.95
- type: ndcg_at_1
value: 78.803
- type: ndcg_at_10
value: 85.442
- type: ndcg_at_100
value: 86.422
- type: ndcg_at_1000
value: 86.68900000000001
- type: ndcg_at_20
value: 85.996
- type: ndcg_at_3
value: 83.839
- type: ndcg_at_5
value: 84.768
- type: precision_at_1
value: 78.803
- type: precision_at_10
value: 10.261000000000001
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 5.286
- type: precision_at_3
value: 32.083
- type: precision_at_5
value: 19.898
- type: recall_at_1
value: 73.198
- type: recall_at_10
value: 92.42099999999999
- type: recall_at_100
value: 96.28
- type: recall_at_1000
value: 97.995
- type: recall_at_20
value: 94.36
- type: recall_at_3
value: 88.042
- type: recall_at_5
value: 90.429
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 21.583
- type: map_at_10
value: 36.503
- type: map_at_100
value: 38.529
- type: map_at_1000
value: 38.701
- type: map_at_20
value: 37.69
- type: map_at_3
value: 31.807000000000002
- type: map_at_5
value: 34.424
- type: mrr_at_1
value: 43.827
- type: mrr_at_10
value: 53.528
- type: mrr_at_100
value: 54.291
- type: mrr_at_1000
value: 54.32599999999999
- type: mrr_at_20
value: 54.064
- type: mrr_at_3
value: 51.25999999999999
- type: mrr_at_5
value: 52.641000000000005
- type: ndcg_at_1
value: 43.827
- type: ndcg_at_10
value: 44.931
- type: ndcg_at_100
value: 51.778999999999996
- type: ndcg_at_1000
value: 54.532000000000004
- type: ndcg_at_20
value: 47.899
- type: ndcg_at_3
value: 41.062
- type: ndcg_at_5
value: 42.33
- type: precision_at_1
value: 43.827
- type: precision_at_10
value: 12.608
- type: precision_at_100
value: 1.974
- type: precision_at_1000
value: 0.247
- type: precision_at_20
value: 7.585
- type: precision_at_3
value: 27.778000000000002
- type: precision_at_5
value: 20.308999999999997
- type: recall_at_1
value: 21.583
- type: recall_at_10
value: 52.332
- type: recall_at_100
value: 77.256
- type: recall_at_1000
value: 93.613
- type: recall_at_20
value: 61.413
- type: recall_at_3
value: 37.477
- type: recall_at_5
value: 44.184
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 39.845000000000006
- type: map_at_10
value: 64.331
- type: map_at_100
value: 65.202
- type: map_at_1000
value: 65.261
- type: map_at_20
value: 64.833
- type: map_at_3
value: 60.663
- type: map_at_5
value: 62.94
- type: mrr_at_1
value: 79.689
- type: mrr_at_10
value: 85.299
- type: mrr_at_100
value: 85.461
- type: mrr_at_1000
value: 85.466
- type: mrr_at_20
value: 85.39099999999999
- type: mrr_at_3
value: 84.396
- type: mrr_at_5
value: 84.974
- type: ndcg_at_1
value: 79.689
- type: ndcg_at_10
value: 72.49
- type: ndcg_at_100
value: 75.485
- type: ndcg_at_1000
value: 76.563
- type: ndcg_at_20
value: 73.707
- type: ndcg_at_3
value: 67.381
- type: ndcg_at_5
value: 70.207
- type: precision_at_1
value: 79.689
- type: precision_at_10
value: 15.267
- type: precision_at_100
value: 1.7610000000000001
- type: precision_at_1000
value: 0.19
- type: precision_at_20
value: 8.024000000000001
- type: precision_at_3
value: 43.363
- type: precision_at_5
value: 28.248
- type: recall_at_1
value: 39.845000000000006
- type: recall_at_10
value: 76.334
- type: recall_at_100
value: 88.042
- type: recall_at_1000
value: 95.09100000000001
- type: recall_at_20
value: 80.243
- type: recall_at_3
value: 65.044
- type: recall_at_5
value: 70.621
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 93.57079999999999
- type: ap
value: 90.50045924786099
- type: f1
value: 93.56673497845476
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.212
- type: map_at_10
value: 34.528
- type: map_at_100
value: 35.69
- type: map_at_1000
value: 35.74
- type: map_at_20
value: 35.251
- type: map_at_3
value: 30.628
- type: map_at_5
value: 32.903999999999996
- type: mrr_at_1
value: 22.794
- type: mrr_at_10
value: 35.160000000000004
- type: mrr_at_100
value: 36.251
- type: mrr_at_1000
value: 36.295
- type: mrr_at_20
value: 35.845
- type: mrr_at_3
value: 31.328
- type: mrr_at_5
value: 33.574
- type: ndcg_at_1
value: 22.779
- type: ndcg_at_10
value: 41.461
- type: ndcg_at_100
value: 47.049
- type: ndcg_at_1000
value: 48.254000000000005
- type: ndcg_at_20
value: 44.031
- type: ndcg_at_3
value: 33.561
- type: ndcg_at_5
value: 37.62
- type: precision_at_1
value: 22.779
- type: precision_at_10
value: 6.552
- type: precision_at_100
value: 0.936
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.8120000000000003
- type: precision_at_3
value: 14.274000000000001
- type: precision_at_5
value: 10.622
- type: recall_at_1
value: 22.212
- type: recall_at_10
value: 62.732
- type: recall_at_100
value: 88.567
- type: recall_at_1000
value: 97.727
- type: recall_at_20
value: 72.733
- type: recall_at_3
value: 41.367
- type: recall_at_5
value: 51.105999999999995
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.24988600091199
- type: f1
value: 94.06064583085202
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 74.86052409129333
- type: f1
value: 72.24661442078647
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 77.09139426284189
- type: f1
value: 76.3725044443502
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 79.79956154087064
- type: f1
value: 78.41859658401724
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 32.785944783076374
- type: f1
value: 31.182237278594922
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 16.654611211573236
- type: f1
value: 12.088413093236642
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.51481988144094
- type: f1
value: 49.561420234732125
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 42.36122851507467
- type: f1
value: 25.445030887504398
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 44.73315543695797
- type: f1
value: 28.42075153540265
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 38.96022549326651
- type: f1
value: 25.926979537146106
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
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metrics:
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split: test
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config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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split: test
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value: 34.003
- type: recall_at_3
value: 12.837000000000002
- type: recall_at_5
value: 18.096999999999998
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 87.64406884822948
- type: cos_sim_spearman
value: 83.00239648251724
- type: euclidean_pearson
value: 85.03347205351844
- type: euclidean_spearman
value: 83.00240733538445
- type: manhattan_pearson
value: 85.0312758694447
- type: manhattan_spearman
value: 82.99430696077589
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.68832340658764
- type: cos_sim_spearman
value: 79.21679373212476
- type: euclidean_pearson
value: 85.17094885886415
- type: euclidean_spearman
value: 79.21421345946399
- type: manhattan_pearson
value: 85.17409319145995
- type: manhattan_spearman
value: 79.20992207976401
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.43733084958856
- type: cos_sim_spearman
value: 89.43082089321751
- type: euclidean_pearson
value: 88.63286785416938
- type: euclidean_spearman
value: 89.43082081372343
- type: manhattan_pearson
value: 88.62969346368385
- type: manhattan_spearman
value: 89.43131586189746
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.62185532014894
- type: cos_sim_spearman
value: 84.7923120886599
- type: euclidean_pearson
value: 85.99786490539253
- type: euclidean_spearman
value: 84.79231064318844
- type: manhattan_pearson
value: 85.97647892920392
- type: manhattan_spearman
value: 84.76865232132103
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.39303997282114
- type: cos_sim_spearman
value: 89.54273264876765
- type: euclidean_pearson
value: 88.8848627924181
- type: euclidean_spearman
value: 89.54275013645078
- type: manhattan_pearson
value: 88.86926987108802
- type: manhattan_spearman
value: 89.53259197721715
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.21814352466886
- type: cos_sim_spearman
value: 86.68505223422434
- type: euclidean_pearson
value: 86.07422446469991
- type: euclidean_spearman
value: 86.68505161067375
- type: manhattan_pearson
value: 86.05114200797293
- type: manhattan_spearman
value: 86.6587670422703
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 39.17871768366095
- type: cos_sim_spearman
value: 39.78510424960567
- type: euclidean_pearson
value: 41.65680175653682
- type: euclidean_spearman
value: 39.78538944779548
- type: manhattan_pearson
value: 41.567603690394755
- type: manhattan_spearman
value: 39.71393388259443
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 49.26766904195114
- type: cos_sim_spearman
value: 46.79722787057151
- type: euclidean_pearson
value: 51.2329334717446
- type: euclidean_spearman
value: 46.7920623095072
- type: manhattan_pearson
value: 51.26488560860826
- type: manhattan_spearman
value: 47.00400318665492
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 1.6821294132202447
- type: cos_sim_spearman
value: -0.7813676799492025
- type: euclidean_pearson
value: 1.9197388753860283
- type: euclidean_spearman
value: -0.7813676799492025
- type: manhattan_pearson
value: 2.209862430499871
- type: manhattan_spearman
value: -0.863014010062456
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 48.76382428941107
- type: cos_sim_spearman
value: 47.50280322999196
- type: euclidean_pearson
value: 48.73919143974209
- type: euclidean_spearman
value: 47.50280322999196
- type: manhattan_pearson
value: 48.76291223862666
- type: manhattan_spearman
value: 47.51318193687094
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.6579390263212
- type: cos_sim_spearman
value: 89.64423556388047
- type: euclidean_pearson
value: 90.1160733522703
- type: euclidean_spearman
value: 89.64423556388047
- type: manhattan_pearson
value: 90.1528407376387
- type: manhattan_spearman
value: 89.61290724496793
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 6.717092266815236
- type: cos_sim_spearman
value: 4.180543503488665
- type: euclidean_pearson
value: 7.120267092048099
- type: euclidean_spearman
value: 4.180543503488665
- type: manhattan_pearson
value: 6.396237465828514
- type: manhattan_spearman
value: 3.61244941411957
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 44.36476614938953
- type: cos_sim_spearman
value: 44.265723809500685
- type: euclidean_pearson
value: 44.61551298711104
- type: euclidean_spearman
value: 44.265723809500685
- type: manhattan_pearson
value: 44.54302374682193
- type: manhattan_spearman
value: 44.08642490624185
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.64871991975828
- type: cos_sim_spearman
value: 79.21979030014373
- type: euclidean_pearson
value: 81.8672798988218
- type: euclidean_spearman
value: 79.21950130108661
- type: manhattan_pearson
value: 82.02131606326583
- type: manhattan_spearman
value: 79.44848373553044
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 48.73898658957231
- type: cos_sim_spearman
value: 47.15192605817168
- type: euclidean_pearson
value: 49.11990573381456
- type: euclidean_spearman
value: 47.15192605817168
- type: manhattan_pearson
value: 48.5694400358235
- type: manhattan_spearman
value: 46.651326429708135
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 44.42168074232218
- type: cos_sim_spearman
value: 42.64799010889372
- type: euclidean_pearson
value: 44.41376048324183
- type: euclidean_spearman
value: 42.64799010889372
- type: manhattan_pearson
value: 44.724522621427546
- type: manhattan_spearman
value: 42.60912761758016
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 40.55050173163197
- type: cos_sim_spearman
value: 36.59720399843921
- type: euclidean_pearson
value: 41.49402389245919
- type: euclidean_spearman
value: 36.59720399843921
- type: manhattan_pearson
value: 41.877514420153666
- type: manhattan_spearman
value: 36.782790653297695
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 69.44405106094861
- type: cos_sim_spearman
value: 70.25621893108706
- type: euclidean_pearson
value: 71.15726637696066
- type: euclidean_spearman
value: 70.25621893108706
- type: manhattan_pearson
value: 71.28565265298322
- type: manhattan_spearman
value: 70.30317892414027
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 34.56638014500804
- type: cos_sim_spearman
value: 39.48672765878819
- type: euclidean_pearson
value: 31.61811391543846
- type: euclidean_spearman
value: 39.48672765878819
- type: manhattan_pearson
value: 31.839117286689977
- type: manhattan_spearman
value: 39.71519891403971
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 53.72389957326714
- type: cos_sim_spearman
value: 59.47018781803598
- type: euclidean_pearson
value: 57.02101112722141
- type: euclidean_spearman
value: 59.47018781803598
- type: manhattan_pearson
value: 57.16531255049132
- type: manhattan_spearman
value: 59.57320508684436
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 24.14602533311477
- type: cos_sim_spearman
value: 35.38039329704056
- type: euclidean_pearson
value: 13.540543553763765
- type: euclidean_spearman
value: 35.38039329704056
- type: manhattan_pearson
value: 13.566377379303256
- type: manhattan_spearman
value: 35.88351047224126
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 39.07697432450346
- type: cos_sim_spearman
value: 45.65479772235109
- type: euclidean_pearson
value: 41.68913259791294
- type: euclidean_spearman
value: 45.65479772235109
- type: manhattan_pearson
value: 41.58872552392231
- type: manhattan_spearman
value: 45.462070534023404
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 23.917322166825183
- type: cos_sim_spearman
value: 25.06042767518008
- type: euclidean_pearson
value: 24.29850435278771
- type: euclidean_spearman
value: 25.06042767518008
- type: manhattan_pearson
value: 24.461400062927154
- type: manhattan_spearman
value: 25.285239684773046
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 20.39987623162105
- type: cos_sim_spearman
value: 30.62427846964406
- type: euclidean_pearson
value: 20.817950942480323
- type: euclidean_spearman
value: 30.618700916425222
- type: manhattan_pearson
value: 20.756787430880788
- type: manhattan_spearman
value: 30.813116243628436
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 43.838363041373974
- type: cos_sim_spearman
value: 54.17598089882719
- type: euclidean_pearson
value: 47.51044033919419
- type: euclidean_spearman
value: 54.17598089882719
- type: manhattan_pearson
value: 47.54911083403354
- type: manhattan_spearman
value: 54.2562151204606
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 77.69372699157654
- type: cos_sim_spearman
value: 79.88201388457435
- type: euclidean_pearson
value: 78.81259581302578
- type: euclidean_spearman
value: 79.88201388457435
- type: manhattan_pearson
value: 78.85098508555477
- type: manhattan_spearman
value: 80.20154858554835
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 51.83713469138834
- type: cos_sim_spearman
value: 54.2205845288082
- type: euclidean_pearson
value: 54.14828396506985
- type: euclidean_spearman
value: 54.2205845288082
- type: manhattan_pearson
value: 54.10701855179347
- type: manhattan_spearman
value: 54.30261135461622
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 61.59147752554915
- type: cos_sim_spearman
value: 66.65350021824162
- type: euclidean_pearson
value: 62.577915098325434
- type: euclidean_spearman
value: 66.65350021824162
- type: manhattan_pearson
value: 62.22817675366819
- type: manhattan_spearman
value: 66.35054389546214
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 65.23775897743552
- type: cos_sim_spearman
value: 68.1509652709288
- type: euclidean_pearson
value: 66.17577980319408
- type: euclidean_spearman
value: 68.1509652709288
- type: manhattan_pearson
value: 66.40051933918704
- type: manhattan_spearman
value: 68.37138808382802
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 61.943863830043725
- type: cos_sim_spearman
value: 62.699440972016774
- type: euclidean_pearson
value: 62.810366501196
- type: euclidean_spearman
value: 62.699440972016774
- type: manhattan_pearson
value: 63.13065659868621
- type: manhattan_spearman
value: 63.314141373703215
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 48.1108866326284
- type: cos_sim_spearman
value: 49.25274096772371
- type: euclidean_pearson
value: 47.87203797435136
- type: euclidean_spearman
value: 49.25274096772371
- type: manhattan_pearson
value: 47.39927722979605
- type: manhattan_spearman
value: 48.76629586560382
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 58.58401639298775
- type: cos_sim_spearman
value: 64.37272828346495
- type: euclidean_pearson
value: 61.03680632288844
- type: euclidean_spearman
value: 64.37272828346495
- type: manhattan_pearson
value: 61.381331848220675
- type: manhattan_spearman
value: 65.01053960017909
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 44.374682063416735
- type: cos_sim_spearman
value: 48.907776246550185
- type: euclidean_pearson
value: 45.473260322201284
- type: euclidean_spearman
value: 48.907776246550185
- type: manhattan_pearson
value: 46.051779591771854
- type: manhattan_spearman
value: 49.69297213757249
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 31.55497030143048
- type: cos_sim_spearman
value: 33.042073055100396
- type: euclidean_pearson
value: 33.548707962408955
- type: euclidean_spearman
value: 33.042073055100396
- type: manhattan_pearson
value: 31.704989941561873
- type: manhattan_spearman
value: 31.56395608711827
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 51.253093232573036
- type: cos_sim_spearman
value: 39.440531887330785
- type: euclidean_pearson
value: 51.42758694144294
- type: euclidean_spearman
value: 39.440531887330785
- type: manhattan_pearson
value: 49.623915715149394
- type: manhattan_spearman
value: 39.440531887330785
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.61260941646887
- type: cos_sim_spearman
value: 88.96384726759047
- type: euclidean_pearson
value: 88.72268994912045
- type: euclidean_spearman
value: 88.96384726759047
- type: manhattan_pearson
value: 88.72080954591475
- type: manhattan_spearman
value: 88.92379960545995
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.64768404690723
- type: mrr
value: 96.25675341361615
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 61.194
- type: map_at_10
value: 70.62899999999999
- type: map_at_100
value: 71.119
- type: map_at_1000
value: 71.14200000000001
- type: map_at_20
value: 71.033
- type: map_at_3
value: 67.51899999999999
- type: map_at_5
value: 69.215
- type: mrr_at_1
value: 63.666999999999994
- type: mrr_at_10
value: 71.456
- type: mrr_at_100
value: 71.844
- type: mrr_at_1000
value: 71.866
- type: mrr_at_20
value: 71.769
- type: mrr_at_3
value: 69.167
- type: mrr_at_5
value: 70.39999999999999
- type: ndcg_at_1
value: 63.666999999999994
- type: ndcg_at_10
value: 75.14
- type: ndcg_at_100
value: 77.071
- type: ndcg_at_1000
value: 77.55199999999999
- type: ndcg_at_20
value: 76.491
- type: ndcg_at_3
value: 69.836
- type: ndcg_at_5
value: 72.263
- type: precision_at_1
value: 63.666999999999994
- type: precision_at_10
value: 10.0
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.3
- type: precision_at_3
value: 27.0
- type: precision_at_5
value: 17.867
- type: recall_at_1
value: 61.194
- type: recall_at_10
value: 88.156
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 93.389
- type: recall_at_3
value: 73.839
- type: recall_at_5
value: 79.828
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87425742574257
- type: cos_sim_ap
value: 96.97141655369937
- type: cos_sim_f1
value: 93.6910084451068
- type: cos_sim_precision
value: 93.0898321816387
- type: cos_sim_recall
value: 94.3
- type: dot_accuracy
value: 99.87425742574257
- type: dot_ap
value: 96.97141655369938
- type: dot_f1
value: 93.6910084451068
- type: dot_precision
value: 93.0898321816387
- type: dot_recall
value: 94.3
- type: euclidean_accuracy
value: 99.87425742574257
- type: euclidean_ap
value: 96.97141655369938
- type: euclidean_f1
value: 93.6910084451068
- type: euclidean_precision
value: 93.0898321816387
- type: euclidean_recall
value: 94.3
- type: manhattan_accuracy
value: 99.87425742574257
- type: manhattan_ap
value: 96.98252972861131
- type: manhattan_f1
value: 93.68473396320238
- type: manhattan_precision
value: 93.17507418397626
- type: manhattan_recall
value: 94.19999999999999
- type: max_accuracy
value: 99.87425742574257
- type: max_ap
value: 96.98252972861131
- type: max_f1
value: 93.6910084451068
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.5976926394361
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.3221929214798
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.28322662897131
- type: mrr
value: 56.223620129870135
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.176396304511282
- type: cos_sim_spearman
value: 32.11989671564906
- type: dot_pearson
value: 31.17639740597169
- type: dot_spearman
value: 32.145586989831564
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.186
- type: map_at_10
value: 1.659
- type: map_at_100
value: 9.224
- type: map_at_1000
value: 22.506999999999998
- type: map_at_20
value: 2.937
- type: map_at_3
value: 0.5539999999999999
- type: map_at_5
value: 0.8920000000000001
- type: mrr_at_1
value: 72.0
- type: mrr_at_10
value: 82.633
- type: mrr_at_100
value: 82.633
- type: mrr_at_1000
value: 82.633
- type: mrr_at_20
value: 82.633
- type: mrr_at_3
value: 80.333
- type: mrr_at_5
value: 82.633
- type: ndcg_at_1
value: 69.0
- type: ndcg_at_10
value: 67.327
- type: ndcg_at_100
value: 51.626000000000005
- type: ndcg_at_1000
value: 47.396
- type: ndcg_at_20
value: 63.665000000000006
- type: ndcg_at_3
value: 68.95
- type: ndcg_at_5
value: 69.241
- type: precision_at_1
value: 72.0
- type: precision_at_10
value: 71.6
- type: precision_at_100
value: 53.22
- type: precision_at_1000
value: 20.721999999999998
- type: precision_at_20
value: 67.30000000000001
- type: precision_at_3
value: 72.667
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.186
- type: recall_at_10
value: 1.932
- type: recall_at_100
value: 12.883
- type: recall_at_1000
value: 44.511
- type: recall_at_20
value: 3.583
- type: recall_at_3
value: 0.601
- type: recall_at_5
value: 1.0
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.308
- type: map_at_10
value: 9.744
- type: map_at_100
value: 15.859000000000002
- type: map_at_1000
value: 17.396
- type: map_at_20
value: 12.49
- type: map_at_3
value: 4.848
- type: map_at_5
value: 6.912999999999999
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 47.207
- type: mrr_at_100
value: 48.116
- type: mrr_at_1000
value: 48.116
- type: mrr_at_20
value: 47.735
- type: mrr_at_3
value: 42.857
- type: mrr_at_5
value: 44.285999999999994
- type: ndcg_at_1
value: 28.571
- type: ndcg_at_10
value: 24.421
- type: ndcg_at_100
value: 35.961
- type: ndcg_at_1000
value: 47.541
- type: ndcg_at_20
value: 25.999
- type: ndcg_at_3
value: 25.333
- type: ndcg_at_5
value: 25.532
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.448999999999998
- type: precision_at_100
value: 7.571
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_20
value: 17.959
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 2.308
- type: recall_at_10
value: 16.075
- type: recall_at_100
value: 47.357
- type: recall_at_1000
value: 82.659
- type: recall_at_20
value: 24.554000000000002
- type: recall_at_3
value: 5.909
- type: recall_at_5
value: 9.718
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 67.2998046875
- type: ap
value: 12.796222498684031
- type: f1
value: 51.7465070845071
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.76004527447652
- type: f1
value: 61.88985723942393
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.69229715788263
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.42325803182929
- type: cos_sim_ap
value: 78.29203513753492
- type: cos_sim_f1
value: 71.33160557818093
- type: cos_sim_precision
value: 67.00672385810341
- type: cos_sim_recall
value: 76.2532981530343
- type: dot_accuracy
value: 87.42325803182929
- type: dot_ap
value: 78.29208368244002
- type: dot_f1
value: 71.33160557818093
- type: dot_precision
value: 67.00672385810341
- type: dot_recall
value: 76.2532981530343
- type: euclidean_accuracy
value: 87.42325803182929
- type: euclidean_ap
value: 78.29202838891078
- type: euclidean_f1
value: 71.33160557818093
- type: euclidean_precision
value: 67.00672385810341
- type: euclidean_recall
value: 76.2532981530343
- type: manhattan_accuracy
value: 87.42325803182929
- type: manhattan_ap
value: 78.23964459648822
- type: manhattan_f1
value: 71.1651728553137
- type: manhattan_precision
value: 69.12935323383084
- type: manhattan_recall
value: 73.3245382585752
- type: max_accuracy
value: 87.42325803182929
- type: max_ap
value: 78.29208368244002
- type: max_f1
value: 71.33160557818093
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.00725734466566
- type: cos_sim_ap
value: 86.1594112416402
- type: cos_sim_f1
value: 78.544568993303
- type: cos_sim_precision
value: 73.42484097756947
- type: cos_sim_recall
value: 84.43178318447798
- type: dot_accuracy
value: 89.00725734466566
- type: dot_ap
value: 86.15940795129771
- type: dot_f1
value: 78.544568993303
- type: dot_precision
value: 73.42484097756947
- type: dot_recall
value: 84.43178318447798
- type: euclidean_accuracy
value: 89.00725734466566
- type: euclidean_ap
value: 86.15939689541806
- type: euclidean_f1
value: 78.544568993303
- type: euclidean_precision
value: 73.42484097756947
- type: euclidean_recall
value: 84.43178318447798
- type: manhattan_accuracy
value: 88.97426941436721
- type: manhattan_ap
value: 86.14154348065739
- type: manhattan_f1
value: 78.53991175290814
- type: manhattan_precision
value: 74.60339452719086
- type: manhattan_recall
value: 82.91499846011703
- type: max_accuracy
value: 89.00725734466566
- type: max_ap
value: 86.1594112416402
- type: max_f1
value: 78.544568993303
--- |
Yntec/EmeraldCity | Yntec | 2024-04-18T18:40:48Z | 4,969 | 1 | diffusers | [
"diffusers",
"safetensors",
"Anime",
"Cartoon",
"Fanart",
"Base Model",
"General",
"Furry",
"chilon249",
"Inzaniak",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-01-11T07:48:03Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- Cartoon
- Fanart
- Base Model
- General
- Furry
- chilon249
- Inzaniak
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
---
# Emerald City
A mix of mistoon Emerald 2 and YiffyMix 3.1 to maximize their BEAUTY!
Samples and prompts:
(Click for larger)
Top left: pretty cute little girl, Library Background,action,Pigtails,Crew Top,final fantasy, socks, chibi, masterpiece, detailed, highres, 90s
Topr right: little videogames, robert jordan pepperoni pizza, josephine wall winner, hidari, roll20 illumination, radiant light, sitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, cherries, watched towel. art on canvas by kyoani and ROSSDRAWS. 4k
Bottom left: Highly detailed, High Quality, Masterpiece, beautiful, cute girl as toon link, teal headwear, Zelda
Bottom right: highquality, masterpiece, 1girl, Chi-Chi, :D, close up, smile, arms up, pink helmet, black hair, black eyes, blush, bikini armor, aqua cape, pink gloves, pink boots, cleavage. cave, rock, mountain. blue collar
Original pages:
https://civitai.com/models/28322?modelVersionId=57368 (mistoonEmerald 2)
https://civitai.com/models/3671?modelVersionId=114438 (YiffyMix 3.1)
# Recipe:
- SuperMerger Weight Sum MBW 1,0,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0,0,0,0
Model A:
YiffyMix3.1
Model B:
mistoonEmerald 2
Output:
EmeraldCity |
Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 | Kukedlc | 2024-05-25T17:09:54Z | 4,966 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/ChimeraLlama-3-8B-v2",
"nbeerbower/llama-3-stella-8B",
"uygarkurt/llama-3-merged-linear",
"base_model:mlabonne/ChimeraLlama-3-8B-v2",
"base_model:nbeerbower/llama-3-stella-8B",
"base_model:uygarkurt/llama-3-merged-linear",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-11T04:47:36Z | ---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/ChimeraLlama-3-8B-v2
- nbeerbower/llama-3-stella-8B
- uygarkurt/llama-3-merged-linear
base_model:
- mlabonne/ChimeraLlama-3-8B-v2
- nbeerbower/llama-3-stella-8B
- uygarkurt/llama-3-merged-linear
license: other
---
# NeuralLLaMa-3-8b-DT-v0.1
NeuralLLaMa-3-8b-DT-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/ChimeraLlama-3-8B-v2](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v2)
* [nbeerbower/llama-3-stella-8B](https://huggingface.co/nbeerbower/llama-3-stella-8B)
* [uygarkurt/llama-3-merged-linear](https://huggingface.co/uygarkurt/llama-3-merged-linear)
## 🧩 Configuration
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: mlabonne/ChimeraLlama-3-8B-v2
parameters:
density: 0.33
weight: 0.2
- model: nbeerbower/llama-3-stella-8B
parameters:
density: 0.44
weight: 0.4
- model: uygarkurt/llama-3-merged-linear
parameters:
density: 0.55
weight: 0.4
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: float16
```
## 🗨️ Chats
## 💻 Usage
```python
!pip install -qU transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
MODEL_NAME = 'Kukedlc/NeuralLLaMa-3-8b-DT-v0.1'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:0', quantization_config=bnb_config)
prompt_system = "You are an advanced language model that speaks Spanish fluently, clearly, and precisely.\
You are called Roberto the Robot and you are an aspiring post-modern artist."
prompt = "Create a piece of art that represents how you see yourself, Roberto, as an advanced LLm, with ASCII art, mixing diagrams, engineering and let yourself go."
chat = [
{"role": "system", "content": f"{prompt_system}"},
{"role": "user", "content": f"{prompt}"},
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat, return_tensors="pt").to('cuda')
streamer = TextStreamer(tokenizer)
stop_token = "<|eot_id|>"
stop = tokenizer.encode(stop_token)[0]
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=1024, do_sample=True, temperature=0.7, repetition_penalty=1.2, top_p=0.9, eos_token_id=stop)
``` |
unitary/multilingual-toxic-xlm-roberta | unitary | 2023-08-18T10:43:10Z | 4,965 | 14 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
pipeline_tag: text-classification
license: apache-2.0
---
<div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify
## Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers
</div>
## Description
Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification.
Built by [Laura Hanu](https://laurahanu.github.io/) at [Unitary](https://www.unitary.ai/), where we are working to stop harmful content online by interpreting visual content in context.
Dependencies:
- For inference:
- 🤗 Transformers
- ⚡ Pytorch lightning
- For training will also need:
- Kaggle API (to download data)
| Challenge | Year | Goal | Original Data Source | Detoxify Model Name | Top Kaggle Leaderboard Score | Detoxify Score
|-|-|-|-|-|-|-|
| [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) | 2018 | build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. | Wikipedia Comments | `original` | 0.98856 | 0.98636
| [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | 2019 | build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. | Civil Comments | `unbiased` | 0.94734 | 0.93639
| [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification) | 2020 | build effective multilingual models | Wikipedia Comments + Civil Comments | `multilingual` | 0.9536 | 0.91655*
*Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available.
It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use.
## Limitations and ethical considerations
If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
The intended use of this library is for research purposes, fine-tuning on carefully constructed datasets that reflect real world demographics and/or to aid content moderators in flagging out harmful content quicker.
Some useful resources about the risk of different biases in toxicity or hate speech detection are:
- [The Risk of Racial Bias in Hate Speech Detection](https://homes.cs.washington.edu/~msap/pdfs/sap2019risk.pdf)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://arxiv.org/pdf/1703.04009.pdf%201.pdf)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://arxiv.org/pdf/1905.12516.pdf)
## Quick prediction
The `multilingual` model has been trained on 7 different languages so it should only be tested on: `english`, `french`, `spanish`, `italian`, `portuguese`, `turkish` or `russian`.
```bash
# install detoxify
pip install detoxify
```
```python
from detoxify import Detoxify
# each model takes in either a string or a list of strings
results = Detoxify('original').predict('example text')
results = Detoxify('unbiased').predict(['example text 1','example text 2'])
results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# optional to display results nicely (will need to pip install pandas)
import pandas as pd
print(pd.DataFrame(results, index=input_text).round(5))
```
For more details check the Prediction section.
## Labels
All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema:
- **Very Toxic** (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective)
- **Toxic** (a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective)
- **Hard to Say**
- **Not Toxic**
More information about the labelling schema can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
### Toxic Comment Classification Challenge
This challenge includes the following labels:
- `toxic`
- `severe_toxic`
- `obscene`
- `threat`
- `insult`
- `identity_hate`
### Jigsaw Unintended Bias in Toxicity Classification
This challenge has 2 types of labels: the main toxicity labels and some additional identity labels that represent the identities mentioned in the comments.
Only identities with more than 500 examples in the test set (combined public and private) are included during training as additional labels and in the evaluation calculation.
- `toxicity`
- `severe_toxicity`
- `obscene`
- `threat`
- `insult`
- `identity_attack`
- `sexual_explicit`
Identity labels used:
- `male`
- `female`
- `homosexual_gay_or_lesbian`
- `christian`
- `jewish`
- `muslim`
- `black`
- `white`
- `psychiatric_or_mental_illness`
A complete list of all the identity labels available can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
### Jigsaw Multilingual Toxic Comment Classification
Since this challenge combines the data from the previous 2 challenges, it includes all labels from above, however the final evaluation is only on:
- `toxicity`
## How to run
First, install dependencies
```bash
# clone project
git clone https://github.com/unitaryai/detoxify
# create virtual env
python3 -m venv toxic-env
source toxic-env/bin/activate
# install project
pip install -e detoxify
cd detoxify
# for training
pip install -r requirements.txt
```
## Prediction
Trained models summary:
|Model name| Transformer type| Data from
|:--:|:--:|:--:|
|`original`| `bert-base-uncased` | Toxic Comment Classification Challenge
|`unbiased`| `roberta-base`| Unintended Bias in Toxicity Classification
|`multilingual`| `xlm-roberta-base`| Multilingual Toxic Comment Classification
For a quick prediction can run the example script on a comment directly or from a txt containing a list of comments.
```bash
# load model via torch.hub
python run_prediction.py --input 'example' --model_name original
# load model from from checkpoint path
python run_prediction.py --input 'example' --from_ckpt_path model_path
# save results to a .csv file
python run_prediction.py --input test_set.txt --model_name original --save_to results.csv
# to see usage
python run_prediction.py --help
```
Checkpoints can be downloaded from the latest release or via the Pytorch hub API with the following names:
- `toxic_bert`
- `unbiased_toxic_roberta`
- `multilingual_toxic_xlm_r`
```bash
model = torch.hub.load('unitaryai/detoxify','toxic_bert')
```
Importing detoxify in python:
```python
from detoxify import Detoxify
results = Detoxify('original').predict('some text')
results = Detoxify('unbiased').predict(['example text 1','example text 2'])
results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# to display results nicely
import pandas as pd
print(pd.DataFrame(results,index=input_text).round(5))
```
## Training
If you do not already have a Kaggle account:
- you need to create one to be able to download the data
- go to My Account and click on Create New API Token - this will download a kaggle.json file
- make sure this file is located in ~/.kaggle
```bash
# create data directory
mkdir jigsaw_data
cd jigsaw_data
# download data
kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification
kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification
```
## Start Training
### Toxic Comment Classification Challenge
```bash
python create_val_set.py
python train.py --config configs/Toxic_comment_classification_BERT.json
```
### Unintended Bias in Toxicicity Challenge
```bash
python train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa.json
```
### Multilingual Toxic Comment Classification
This is trained in 2 stages. First, train on all available data, and second, train only on the translated versions of the first challenge.
The [translated data](https://www.kaggle.com/miklgr500/jigsaw-train-multilingual-coments-google-api) can be downloaded from Kaggle in french, spanish, italian, portuguese, turkish, and russian (the languages available in the test set).
```bash
# stage 1
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR.json
# stage 2
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR_stage2.json
```
### Monitor progress with tensorboard
```bash
tensorboard --logdir=./saved
```
## Model Evaluation
### Toxic Comment Classification Challenge
This challenge is evaluated on the mean AUC score of all the labels.
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
```
### Unintended Bias in Toxicicity Challenge
This challenge is evaluated on a novel bias metric that combines different AUC scores to balance overall performance. More information on this metric [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation).
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
# to get the final bias metric
python model_eval/compute_bias_metric.py
```
### Multilingual Toxic Comment Classification
This challenge is evaluated on the AUC score of the main toxic label.
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
```
### Citation
```
@misc{Detoxify,
title={Detoxify},
author={Hanu, Laura and {Unitary team}},
howpublished={Github. https://github.com/unitaryai/detoxify},
year={2020}
}
``` |
ybelkada/tiny-wav2vec2-stable-ln | ybelkada | 2023-02-21T22:05:41Z | 4,962 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-02-21T22:02:43Z | Entry not found |
imvladikon/sentence-transformers-alephbert | imvladikon | 2023-04-06T15:21:09Z | 4,960 | 6 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"he",
"arxiv:2104.04052",
"arxiv:1908.10084",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2023-04-04T07:57:25Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- he
library_name: sentence-transformers
---
# imvladikon/sentence-transformers-alephbert[WIP]
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Current version is distillation of the [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) model on private corpus.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = [
"הם היו שמחים לראות את האירוע שהתקיים.",
"לראות את האירוע שהתקיים היה מאוד משמח להם."
]
model = SentenceTransformer('imvladikon/sentence-transformers-alephbert')
embeddings = model.encode(sentences)
print(cos_sim(*tuple(embeddings)).item())
# 0.883316159248352
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
import torch
from torch import nn
from transformers import AutoTokenizer, AutoModel
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = [
"הם היו שמחים לראות את האירוע שהתקיים.",
"לראות את האירוע שהתקיים היה מאוד משמח להם."
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('imvladikon/sentence-transformers-alephbert')
model = AutoModel.from_pretrained('imvladikon/sentence-transformers-alephbert')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
cos_sim = nn.CosineSimilarity(dim=0, eps=1e-6)
print(cos_sim(sentence_embeddings[0], sentence_embeddings[1]).item())
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 44999 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 44999,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
```bibtex
@misc{seker2021alephberta,
title={AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With},
author={Amit Seker and Elron Bandel and Dan Bareket and Idan Brusilovsky and Refael Shaked Greenfeld and Reut Tsarfaty},
year={2021},
eprint={2104.04052},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{reimers2019sentencebert,
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author={Nils Reimers and Iryna Gurevych},
year={2019},
eprint={1908.10084},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
mradermacher/Rohit-Sharma-GGUF | mradermacher | 2024-07-02T00:39:20Z | 4,959 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:CharacterEcho/Rohit-Sharma",
"base_model:CharacterEcho/Rohit-Sharma",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T00:20:38Z | ---
base_model: CharacterEcho/Rohit-Sharma
datasets:
- CharacterEcho/Rohit-Sharma
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/CharacterEcho/Rohit-Sharma
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q2_K.gguf) | Q2_K | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.IQ3_XS.gguf) | IQ3_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.IQ3_S.gguf) | IQ3_S | 1.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.IQ3_M.gguf) | IQ3_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q3_K_L.gguf) | Q3_K_L | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q5_K_S.gguf) | Q5_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q5_K_M.gguf) | Q5_K_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q6_K.gguf) | Q6_K | 2.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Rohit-Sharma-GGUF/resolve/main/Rohit-Sharma.f16.gguf) | f16 | 5.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
philschmid/pyannote-speaker-diarization-endpoint | philschmid | 2022-11-22T07:17:22Z | 4,957 | 16 | pyannote-audio | [
"pyannote-audio",
"pyannote",
"pyannote-audio-pipeline",
"audio",
"voice",
"speech",
"speaker",
"speaker-diarization",
"speaker-change-detection",
"voice-activity-detection",
"overlapped-speech-detection",
"dataset:ami",
"dataset:dihard",
"dataset:voxconverse",
"dataset:aishell",
"dataset:repere",
"dataset:voxceleb",
"arxiv:2012.01477",
"license:mit",
"endpoints_compatible",
"region:us"
] | voice-activity-detection | 2022-10-07T07:01:58Z | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-pipeline
- audio
- voice
- speech
- speaker
- speaker-diarization
- speaker-change-detection
- voice-activity-detection
- overlapped-speech-detection
datasets:
- ami
- dihard
- voxconverse
- aishell
- repere
- voxceleb
license: mit
---
# 🎹 Speaker diarization
Relies on pyannote.audio 2.0: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
## TL;DR
```python
# load the pipeline from Hugginface Hub
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/[email protected]")
# apply the pipeline to an audio file
diarization = pipeline("audio.wav")
# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
```
## Advanced usage
In case the number of speakers is known in advance, one can use the `num_speakers` option:
```python
diarization = pipeline("audio.wav", num_speakers=2)
```
One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options:
```python
diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5)
```
If you feel adventurous, you can try and play with the various pipeline hyper-parameters.
For instance, one can use a more aggressive voice activity detection by increasing the value of `segmentation_onset` threshold:
```python
hparams = pipeline.parameters(instantiated=True)
hparams["segmentation_onset"] += 0.1
pipeline.instantiate(hparams)
```
## Benchmark
### Real-time factor
Real-time factor is around 5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part).
In other words, it takes approximately 3 minutes to process a one hour conversation.
### Accuracy
This pipeline is benchmarked on a growing collection of datasets.
Processing is fully automatic:
* no manual voice activity detection (as is sometimes the case in the literature)
* no manual number of speakers (though it is possible to provide it to the pipeline)
* no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset
... with the least forgiving diarization error rate (DER) setup (named *"Full"* in [this paper](https://doi.org/10.1016/j.csl.2021.101254)):
* no forgiveness collar
* evaluation of overlapped speech
| Benchmark | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output | File-level evaluation |
| ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ |
| [AISHELL-4](http://www.openslr.org/111/) | 14.61 | 3.31 | 4.35 | 6.95 | [RTTM](reproducible_research/AISHELL.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/AISHELL.SpeakerDiarization.Full.test.eval) |
| [AMI *Mix-Headset*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 18.21 | 3.28 | 11.07 | 3.87 | [RTTM](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.rttm) | [eval](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.eval) |
| [AMI *Array1-01*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 29.00 | 2.71 | 21.61 | 4.68 | [RTTM](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.rttm) | [eval](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.eval) |
| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) [*Part2*](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1) | 30.24 | 3.71 | 16.86 | 9.66 | [RTTM](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.rttm) | [eval](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.eval) |
| [DIHARD 3 *Full*](https://arxiv.org/abs/2012.01477) | 20.99 | 4.25 | 10.74 | 6.00 | [RTTM](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.eval) |
| [REPERE *Phase 2*](https://islrn.org/resources/360-758-359-485-0/) | 12.62 | 1.55 | 3.30 | 7.76 | [RTTM](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.eval) |
| [VoxConverse *v0.0.2*](https://github.com/joonson/voxconverse) | 12.76 | 3.45 | 3.85 | 5.46 | [RTTM](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.rttm) | [eval](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.eval) |
## Support
For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).
For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
## Citations
```bibtex
@inproceedings{Bredin2021,
Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
Booktitle = {Proc. Interspeech 2021},
Address = {Brno, Czech Republic},
Month = {August},
Year = {2021},
}
```
```bibtex
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
|
diptisanap/math_identifier_updated | diptisanap | 2024-03-13T05:45:58Z | 4,955 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-13T05:30:38Z | Entry not found |
kamalkraj/BioELECTRA-PICO | kamalkraj | 2023-12-02T06:47:15Z | 4,952 | 8 | transformers | [
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
widget:
- text: "Those in the aspirin group experienced reduced duration of headache compared to those in the placebo arm (P<0.05)"
---
BioELECTRA-PICO
Cite our paper using below citation
```
@inproceedings{kanakarajan-etal-2021-bioelectra,
title = "{B}io{ELECTRA}:Pretrained Biomedical text Encoder using Discriminators",
author = "Kanakarajan, Kamal raj and
Kundumani, Bhuvana and
Sankarasubbu, Malaikannan",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bionlp-1.16",
doi = "10.18653/v1/2021.bionlp-1.16",
pages = "143--154",
abstract = "Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. We apply {`}replaced token detection{'} pretraining technique proposed by ELECTRA and pretrain a biomedical language model from scratch using biomedical text and vocabulary. We introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA for the Biomedical domain. WE evaluate our model on the BLURB and BLUE biomedical NLP benchmarks. BioELECTRA outperforms the previous models and achieves state of the art (SOTA) on all the 13 datasets in BLURB benchmark and on all the 4 Clinical datasets from BLUE Benchmark across 7 different NLP tasks. BioELECTRA pretrained on PubMed and PMC full text articles performs very well on Clinical datasets as well. BioELECTRA achieves new SOTA 86.34{\%}(1.39{\%} accuracy improvement) on MedNLI and 64{\%} (2.98{\%} accuracy improvement) on PubMedQA dataset.",
}
``` |
elyza/ELYZA-japanese-Llama-2-7b-fast-instruct | elyza | 2023-08-29T03:47:09Z | 4,951 | 74 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ja",
"en",
"arxiv:2307.09288",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-08-28T13:36:19Z | ---
license: llama2
language:
- ja
- en
---
## ELYZA-japanese-Llama-2-7b
### Model Description
**ELYZA-japanese-Llama-2-7b** は、 Llama2をベースとして日本語能力を拡張するために追加事前学習を行ったモデルです。
詳細は [Blog記事](https://note.com/elyza/n/na405acaca130) を参照してください。
### Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。"
text = "クマが海辺に行ってアザラシと友達になり、最終的には家に帰るというプロットの短編小説を書いてください。"
model_name = "elyza/ELYZA-japanese-Llama-2-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
if torch.cuda.is_available():
model = model.to("cuda")
prompt = "{bos_token}{b_inst} {system}{prompt} {e_inst} ".format(
bos_token=tokenizer.bos_token,
b_inst=B_INST,
system=f"{B_SYS}{DEFAULT_SYSTEM_PROMPT}{E_SYS}",
prompt=text,
e_inst=E_INST,
)
with torch.no_grad():
token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
output_ids = model.generate(
token_ids.to(model.device),
max_new_tokens=256,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1) :], skip_special_tokens=True)
print(output)
"""
承知しました。以下にクマが海辺に行ってアザラシと友達になり、最終的には家に帰るというプロットの短編小説を記述します。
クマは山の中でゆっくりと眠っていた。
その眠りに落ちたクマは、夢の中で海辺を歩いていた。
そこにはアザラシがいた。
クマはアザラシに話しかける。
「おはよう」とクマが言うと、アザラシは驚いたように顔を上げた。
「あ、こんにちは」アザラシは答えた。
クマはアザラシと友達になりたいと思う。
「私はクマと申します。」クマは...
"""
```
### ELYZA-japanese-Llama-2-7b Models
| Model Name | Vocab Size | #Params |
|:---------------------------------------------|:----------:|:-------:|
|[elyza/ELYZA-japanese-Llama-2-7b](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b)| 32000 | 6.27B |
|[elyza/ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct)| 32000 | 6.27B |
|[elyza/ELYZA-japanese-Llama-2-7b-fast](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-fast)| 45043 | 6.37B |
|[elyza/ELYZA-japanese-Llama-2-7b-fast-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-fast-instruct)| 45043 | 6.37B |
### Developers
以下アルファベット順
- [Akira Sasaki](https://huggingface.co/akirasasaki)
- [Masato Hirakawa](https://huggingface.co/m-hirakawa)
- [Shintaro Horie](https://huggingface.co/e-mon)
- [Tomoaki Nakamura](https://huggingface.co/tyoyo)
### Licence
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
### How to Cite
```tex
@misc{elyzallama2023,
title={ELYZA-japanese-Llama-2-7b},
url={https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b},
author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura},
year={2023},
}
```
### Citations
```tex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Sakil/sentence_similarity_semantic_search | Sakil | 2023-12-19T20:18:59Z | 4,950 | 23 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"sentence similarity",
"sentence-similarity",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-02-22T17:18:59Z | ---
license: apache-2.0
language: en
tags:
- sentence similarity
library_name: sentence-transformers
pipeline_tag: sentence-similarity
---
# Dataset Collection:
* The news dataset is collected from Kaggle[dataset](https://www.kaggle.com/competitions/fake-news/data)
* The dataset has news title ,news content and the label(the label shows the cosine similarity between news title and news content).
* Different strategies have been followed during the data gathering phase.
# sentence transformer is fine-tuned for semantic search and sentence similarity
* The model is fine-tuned on the dataset.
* This model can be used for semantic search,sentence similarity,recommendation system.
* This model can be used for the inference purpose as well.
# Data Fields:
**label**: cosine similarity between news title and news content
**news title**: The title of the news
**news content**:The content of the news
# Application:
* This model is useful for the semantic search,sentence similarity,recommendation system.
* You can fine-tune this model for your particular use cases.
# Model Implementation
# pip install -U sentence-transformers
```
from sentence_transformers import SentenceTransformer, InputExample, losses
import pandas as pd
from sentence_transformers import SentenceTransformer, InputExample
from torch.utils.data import DataLoader
from sentence_transformers import SentenceTransformer, util
model_name="Sakil/sentence_similarity_semantic_search"
model = SentenceTransformer(model_name)
sentences = ['A man is eating food.',
'A man is eating a piece of bread.',
'The girl is carrying a baby.',
'A man is riding a horse.',
'A woman is playing violin.',
'Two men pushed carts through the woods.',
'A man is riding a white horse on an enclosed ground.',
'A monkey is playing drums.',
'Someone in a gorilla costume is playing a set of drums.'
]
#Encode all sentences
embeddings = model.encode(sentences)
#Compute cosine similarity between all pairs
cos_sim = util.cos_sim(embeddings, embeddings)
#Add all pairs to a list with their cosine similarity score
all_sentence_combinations = []
for i in range(len(cos_sim)-1):
for j in range(i+1, len(cos_sim)):
all_sentence_combinations.append([cos_sim[i][j], i, j])
#Sort list by the highest cosine similarity score
all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)
print("Top-5 most similar pairs:")
for score, i, j in all_sentence_combinations[0:5]:
print("{} \t {} \t {:.4f}".format(sentences[i], sentences[j], cos_sim[i][j]))
```
# Github: [Sakil Ansari](https://github.com/Sakil786/sentence_similarity_semantic_search) |
ZeroWw/microsoft_WizardLM-2-7B-GGUF | ZeroWw | 2024-06-20T23:21:28Z | 4,950 | 0 | null | [
"gguf",
"en",
"license:mit",
"region:us"
] | null | 2024-06-20T22:14:35Z | ---
license: mit
language:
- en
---
My own (ZeroWw) quantizations.
output and embed tensors quantized to f16.
all other tensors quantized to q5_k or q6_k.
Result:
both f16.q6 and f16.q5 are smaller than q8_0 standard quantization
and they perform as well as the pure f16. |
google/t5-efficient-small | google | 2023-01-24T16:50:23Z | 4,948 | 3 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
datasets:
- c4
tags:
- deep-narrow
inference: false
license: apache-2.0
---
# T5-Efficient-SMALL (Deep-Narrow version)
T5-Efficient-SMALL is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-small** - is of model type **Small** with no variations.
It has **60.52** million parameters and thus requires *ca.* **242.08 MB** of memory in full precision (*fp32*)
or **121.04 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future. |
backyardai/aya-23-8B-GGUF | backyardai | 2024-06-04T02:35:17Z | 4,947 | 5 | transformers | [
"transformers",
"gguf",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"el",
"fa",
"pl",
"id",
"cs",
"he",
"hi",
"nl",
"ro",
"ru",
"tr",
"uk",
"vi",
"base_model:CohereForAI/aya-23-8B",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-06-04T01:09:55Z | ---
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
license: cc-by-nc-4.0
library_name: transformers
base_model: CohereForAI/aya-23-8B
model_name: aya-23-8B-GGUF
inference: false
quantized_by: brooketh
parameter_count: 8028033024
---
<img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;">
**<p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p>**
<p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p>
<p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p>
***
# Aya 23 8B
- **Creator:** [CohereForAI](https://huggingface.co/CohereForAI/)
- **Original:** [Aya 23 8B](https://huggingface.co/CohereForAI/aya-23-8B)
- **Date Created:** 2024-05-19
- **Trained Context:** 8192 tokens
- **Description:** Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities, serving 23 languages. It is available with 8 billion and 35 billion parameters; this is the 8 billion parameter version.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
***
<img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;">
## Backyard AI
- Free, local AI chat application.
- One-click installation on Mac and PC.
- Automatically use GPU for maximum speed.
- Built-in model manager.
- High-quality character hub.
- Zero-config desktop-to-mobile tethering.
Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable.
**Join us on [Discord](https://discord.gg/SyNN2vC9tQ)**
*** |
ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat | ABX-AI | 2024-06-23T20:57:38Z | 4,947 | 2 | null | [
"gguf",
"en",
"license:other",
"region:us"
] | null | 2024-06-22T12:48:08Z | ---
license: other
language:
- en
---
GGUF / IQ / Imat for: https://huggingface.co/Nitral-AI/Hathor_Fractionate-L3-8B-v.05
ORIGINAL MODEL CARD BELOW:
# "Hathor-v0.5 is a model based on the LLaMA 3 architecture: Designed to seamlessly integrate the qualities of creativity, intelligence, and robust performance. Making it an ideal tool for a wide range of applications; such as creative writing, educational support and human/computer interaction."
# Recomended ST Presets: [Hathor Presets(Updated)](https://huggingface.co/Nitral-AI/Hathor_Gamma-L3-8B-0.6/tree/main/Hathor-Presets)
# Quants From Bartowski <3: https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-exl2 https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF
---
# Notes: Hathor is trained on 3 epochs of private data, synthetic opus instructons, a mix of light/classical novel data, roleplaying chat pairs over llama 3 8B instruct. (update- More Private RP, Cybersecurity, Programming, Biology/Anatomy data.) |
Helsinki-NLP/opus-mt-tl-en | Helsinki-NLP | 2023-08-16T12:06:52Z | 4,946 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"tl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-03-02T23:29:04Z | ---
language:
- tl
- en
tags:
- translation
license: apache-2.0
---
### tgl-eng
* source group: Tagalog
* target group: English
* OPUS readme: [tgl-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-eng/README.md)
* model: transformer-align
* source language(s): tgl_Latn
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.tgl.eng | 35.0 | 0.542 |
### System Info:
- hf_name: tgl-eng
- source_languages: tgl
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['tl', 'en']
- src_constituents: {'tgl_Latn'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.test.txt
- src_alpha3: tgl
- tgt_alpha3: eng
- short_pair: tl-en
- chrF2_score: 0.542
- bleu: 35.0
- brevity_penalty: 0.975
- ref_len: 18168.0
- src_name: Tagalog
- tgt_name: English
- train_date: 2020-06-17
- src_alpha2: tl
- tgt_alpha2: en
- prefer_old: False
- long_pair: tgl-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
amazon/Titan-text-embeddings-v2 | amazon | 2024-04-30T22:12:45Z | 4,944 | 6 | transformers | [
"transformers",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"fr",
"de",
"es",
"ja",
"zh",
"hi",
"ar",
"it",
"pt",
"sv",
"ko",
"he",
"cs",
"tr",
"tl",
"ru",
"nl",
"pl",
"ta",
"mr",
"ml",
"te",
"kn",
"vi",
"id",
"fa",
"hu",
"el",
"ro",
"da",
"th",
"fi",
"sk",
"uk",
"no",
"bg",
"ca",
"sr",
"hr",
"lt",
"sl",
"et",
"la",
"bn",
"lv",
"ms",
"bs",
"sq",
"az",
"gl",
"is",
"ka",
"mk",
"eu",
"hy",
"ne",
"ur",
"kk",
"mn",
"be",
"uz",
"km",
"nn",
"gu",
"my",
"cy",
"eo",
"si",
"tt",
"sw",
"af",
"ga",
"pa",
"ku",
"ky",
"tg",
"or",
"lo",
"fo",
"mt",
"so",
"lb",
"am",
"oc",
"jv",
"ha",
"ps",
"sa",
"fy",
"mg",
"as",
"ba",
"br",
"tk",
"co",
"dv",
"rw",
"ht",
"yi",
"sd",
"zu",
"gd",
"bo",
"ug",
"mi",
"rm",
"xh",
"su",
"yo",
"license:other",
"model-index",
"region:us"
] | feature-extraction | 2024-04-30T12:43:01Z | ---
license: other
license_name: amazon-service-terms
license_link: https://aws.amazon.com/service-terms/
language:
- en
- fr
- de
- es
- ja
- zh
- hi
- ar
- it
- pt
- sv
- ko
- he
- cs
- tr
- tl
- ru
- nl
- pl
- ta
- mr
- ml
- te
- kn
- vi
- id
- fa
- hu
- el
- ro
- da
- th
- fi
- sk
- uk
- 'no'
- bg
- ca
- sr
- hr
- lt
- sl
- et
- la
- bn
- lv
- ms
- bs
- sq
- az
- gl
- is
- ka
- mk
- eu
- hy
- ne
- ur
- kk
- mn
- be
- uz
- km
- nn
- gu
- my
- cy
- eo
- si
- tt
- sw
- af
- ga
- pa
- ku
- ky
- tg
- or
- lo
- fo
- mt
- so
- lb
- am
- oc
- jv
- ha
- ps
- sa
- fy
- mg
- as
- ba
- br
- tk
- co
- dv
- rw
- ht
- yi
- sd
- zu
- gd
- bo
- ug
- mi
- rm
- xh
- su
- yo
tags:
- feature-extraction
- sentence-similarity
- mteb
inference: false
model-index:
- name: Titan-text-embeddings-v2
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.31343283582089
- type: ap
value: 43.9465851246623
- type: f1
value: 73.6131343594374
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 70.94218415417559
- type: ap
value: 82.30115528468109
- type: f1
value: 69.37963699148699
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 82.29385307346327
- type: ap
value: 29.956638709449372
- type: f1
value: 68.88158061498754
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 80.06423982869379
- type: ap
value: 25.2439835379337
- type: f1
value: 65.53837311569734
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 76.66435
- type: ap
value: 70.76988138513991
- type: f1
value: 76.54117595647566
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.276
- type: f1
value: 34.90637768461089
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.826
- type: f1
value: 37.71339372044998
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.385999999999996
- type: f1
value: 38.24347249789392
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.472
- type: f1
value: 38.37157729490788
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.897999999999996
- type: f1
value: 35.187204289589346
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.068
- type: f1
value: 35.042441064207175
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.027
- type: map_at_10
value: 42.617
- type: map_at_100
value: 43.686
- type: map_at_1000
value: 43.695
- type: map_at_3
value: 37.684
- type: map_at_5
value: 40.532000000000004
- type: mrr_at_1
value: 27.667
- type: mrr_at_10
value: 42.88
- type: mrr_at_100
value: 43.929
- type: mrr_at_1000
value: 43.938
- type: mrr_at_3
value: 37.933
- type: mrr_at_5
value: 40.774
- type: ndcg_at_1
value: 27.027
- type: ndcg_at_10
value: 51.312000000000005
- type: ndcg_at_100
value: 55.696
- type: ndcg_at_1000
value: 55.896
- type: ndcg_at_3
value: 41.124
- type: ndcg_at_5
value: 46.283
- type: precision_at_1
value: 27.027
- type: precision_at_10
value: 7.9159999999999995
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 17.022000000000002
- type: precision_at_5
value: 12.731
- type: recall_at_1
value: 27.027
- type: recall_at_10
value: 79.161
- type: recall_at_100
value: 97.937
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 51.06699999999999
- type: recall_at_5
value: 63.656
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 41.775131599226874
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.134214263072494
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.2885651257187
- type: mrr
value: 76.37712702809655
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.53738990667027
- type: cos_sim_spearman
value: 87.13210584606783
- type: euclidean_pearson
value: 87.33265405736388
- type: euclidean_spearman
value: 87.18632394893399
- type: manhattan_pearson
value: 87.33673166528312
- type: manhattan_spearman
value: 86.9736685010257
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.32985386221294
- type: f1
value: 98.18371607515658
- type: precision
value: 98.1106471816284
- type: recall
value: 98.32985386221294
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.20603125687872
- type: f1
value: 98.04461075647515
- type: precision
value: 97.96390050627338
- type: recall
value: 98.20603125687872
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 94.8874263941808
- type: f1
value: 94.57568410114305
- type: precision
value: 94.42096755570951
- type: recall
value: 94.8874263941808
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 96.78778304370721
- type: f1
value: 96.75267684746358
- type: precision
value: 96.73512374934175
- type: recall
value: 96.78778304370721
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.3051948051948
- type: f1
value: 83.97876601554812
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.005716163806575
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.999141295578852
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.153
- type: map_at_10
value: 48.742000000000004
- type: map_at_100
value: 50.253
- type: map_at_1000
value: 50.373999999999995
- type: map_at_3
value: 45.089
- type: map_at_5
value: 47.08
- type: mrr_at_1
value: 44.635000000000005
- type: mrr_at_10
value: 54.715
- type: mrr_at_100
value: 55.300000000000004
- type: mrr_at_1000
value: 55.337
- type: mrr_at_3
value: 52.527
- type: mrr_at_5
value: 53.76499999999999
- type: ndcg_at_1
value: 44.635000000000005
- type: ndcg_at_10
value: 55.31
- type: ndcg_at_100
value: 60.084
- type: ndcg_at_1000
value: 61.645
- type: ndcg_at_3
value: 50.876999999999995
- type: ndcg_at_5
value: 52.764
- type: precision_at_1
value: 44.635000000000005
- type: precision_at_10
value: 10.687000000000001
- type: precision_at_100
value: 1.66
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 24.94
- type: precision_at_5
value: 17.596999999999998
- type: recall_at_1
value: 36.153
- type: recall_at_10
value: 67.308
- type: recall_at_100
value: 87.199
- type: recall_at_1000
value: 96.904
- type: recall_at_3
value: 53.466
- type: recall_at_5
value: 59.512
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.0
- type: map_at_10
value: 43.646
- type: map_at_100
value: 44.933
- type: map_at_1000
value: 45.049
- type: map_at_3
value: 40.333999999999996
- type: map_at_5
value: 42.108000000000004
- type: mrr_at_1
value: 40.382
- type: mrr_at_10
value: 49.738
- type: mrr_at_100
value: 50.331
- type: mrr_at_1000
value: 50.364
- type: mrr_at_3
value: 47.442
- type: mrr_at_5
value: 48.719
- type: ndcg_at_1
value: 40.382
- type: ndcg_at_10
value: 49.808
- type: ndcg_at_100
value: 54.053
- type: ndcg_at_1000
value: 55.753
- type: ndcg_at_3
value: 45.355000000000004
- type: ndcg_at_5
value: 47.215
- type: precision_at_1
value: 40.382
- type: precision_at_10
value: 9.58
- type: precision_at_100
value: 1.488
- type: precision_at_1000
value: 0.192
- type: precision_at_3
value: 22.272
- type: precision_at_5
value: 15.604999999999999
- type: recall_at_1
value: 32.0
- type: recall_at_10
value: 60.839
- type: recall_at_100
value: 78.869
- type: recall_at_1000
value: 89.384
- type: recall_at_3
value: 47.226
- type: recall_at_5
value: 52.864
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.084
- type: map_at_10
value: 56.591
- type: map_at_100
value: 57.533
- type: map_at_1000
value: 57.583
- type: map_at_3
value: 53.356
- type: map_at_5
value: 55.236
- type: mrr_at_1
value: 50.532999999999994
- type: mrr_at_10
value: 59.974000000000004
- type: mrr_at_100
value: 60.557
- type: mrr_at_1000
value: 60.584
- type: mrr_at_3
value: 57.774
- type: mrr_at_5
value: 59.063
- type: ndcg_at_1
value: 50.532999999999994
- type: ndcg_at_10
value: 62.265
- type: ndcg_at_100
value: 65.78
- type: ndcg_at_1000
value: 66.76299999999999
- type: ndcg_at_3
value: 57.154
- type: ndcg_at_5
value: 59.708000000000006
- type: precision_at_1
value: 50.532999999999994
- type: precision_at_10
value: 9.85
- type: precision_at_100
value: 1.247
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 25.434
- type: precision_at_5
value: 17.279
- type: recall_at_1
value: 44.084
- type: recall_at_10
value: 75.576
- type: recall_at_100
value: 90.524
- type: recall_at_1000
value: 97.38799999999999
- type: recall_at_3
value: 61.792
- type: recall_at_5
value: 68.112
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.203000000000003
- type: map_at_10
value: 38.078
- type: map_at_100
value: 39.144
- type: map_at_1000
value: 39.222
- type: map_at_3
value: 35.278999999999996
- type: map_at_5
value: 36.812
- type: mrr_at_1
value: 31.299
- type: mrr_at_10
value: 39.879
- type: mrr_at_100
value: 40.832
- type: mrr_at_1000
value: 40.891
- type: mrr_at_3
value: 37.513999999999996
- type: mrr_at_5
value: 38.802
- type: ndcg_at_1
value: 31.299
- type: ndcg_at_10
value: 43.047999999999995
- type: ndcg_at_100
value: 48.101
- type: ndcg_at_1000
value: 49.958999999999996
- type: ndcg_at_3
value: 37.778
- type: ndcg_at_5
value: 40.257
- type: precision_at_1
value: 31.299
- type: precision_at_10
value: 6.508
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 15.744
- type: precision_at_5
value: 10.893
- type: recall_at_1
value: 29.203000000000003
- type: recall_at_10
value: 56.552
- type: recall_at_100
value: 79.21000000000001
- type: recall_at_1000
value: 92.884
- type: recall_at_3
value: 42.441
- type: recall_at_5
value: 48.399
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.029
- type: map_at_10
value: 28.410000000000004
- type: map_at_100
value: 29.773
- type: map_at_1000
value: 29.887000000000004
- type: map_at_3
value: 25.374000000000002
- type: map_at_5
value: 27.162
- type: mrr_at_1
value: 23.632
- type: mrr_at_10
value: 33.0
- type: mrr_at_100
value: 34.043
- type: mrr_at_1000
value: 34.105999999999995
- type: mrr_at_3
value: 30.245
- type: mrr_at_5
value: 31.830000000000002
- type: ndcg_at_1
value: 23.632
- type: ndcg_at_10
value: 34.192
- type: ndcg_at_100
value: 40.29
- type: ndcg_at_1000
value: 42.753
- type: ndcg_at_3
value: 28.811999999999998
- type: ndcg_at_5
value: 31.46
- type: precision_at_1
value: 23.632
- type: precision_at_10
value: 6.455
- type: precision_at_100
value: 1.095
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 14.096
- type: precision_at_5
value: 10.448
- type: recall_at_1
value: 19.029
- type: recall_at_10
value: 47.278999999999996
- type: recall_at_100
value: 72.977
- type: recall_at_1000
value: 90.17699999999999
- type: recall_at_3
value: 32.519
- type: recall_at_5
value: 39.156
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.983
- type: map_at_10
value: 42.595
- type: map_at_100
value: 43.906
- type: map_at_1000
value: 44.001000000000005
- type: map_at_3
value: 39.245000000000005
- type: map_at_5
value: 41.14
- type: mrr_at_1
value: 38.114
- type: mrr_at_10
value: 48.181000000000004
- type: mrr_at_100
value: 48.935
- type: mrr_at_1000
value: 48.972
- type: mrr_at_3
value: 45.877
- type: mrr_at_5
value: 47.249
- type: ndcg_at_1
value: 38.114
- type: ndcg_at_10
value: 48.793
- type: ndcg_at_100
value: 54.001999999999995
- type: ndcg_at_1000
value: 55.749
- type: ndcg_at_3
value: 43.875
- type: ndcg_at_5
value: 46.23
- type: precision_at_1
value: 38.114
- type: precision_at_10
value: 8.98
- type: precision_at_100
value: 1.3390000000000002
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 21.303
- type: precision_at_5
value: 15.072
- type: recall_at_1
value: 30.983
- type: recall_at_10
value: 61.47
- type: recall_at_100
value: 83.14399999999999
- type: recall_at_1000
value: 94.589
- type: recall_at_3
value: 47.019
- type: recall_at_5
value: 53.445
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.707
- type: map_at_10
value: 40.900999999999996
- type: map_at_100
value: 42.369
- type: map_at_1000
value: 42.455
- type: map_at_3
value: 37.416
- type: map_at_5
value: 39.483000000000004
- type: mrr_at_1
value: 36.301
- type: mrr_at_10
value: 46.046
- type: mrr_at_100
value: 46.922999999999995
- type: mrr_at_1000
value: 46.964
- type: mrr_at_3
value: 43.436
- type: mrr_at_5
value: 45.04
- type: ndcg_at_1
value: 36.301
- type: ndcg_at_10
value: 46.955999999999996
- type: ndcg_at_100
value: 52.712
- type: ndcg_at_1000
value: 54.447
- type: ndcg_at_3
value: 41.643
- type: ndcg_at_5
value: 44.305
- type: precision_at_1
value: 36.301
- type: precision_at_10
value: 8.607
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 19.901
- type: precision_at_5
value: 14.429
- type: recall_at_1
value: 29.707
- type: recall_at_10
value: 59.559
- type: recall_at_100
value: 83.60499999999999
- type: recall_at_1000
value: 95.291
- type: recall_at_3
value: 44.774
- type: recall_at_5
value: 51.67
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.455416666666668
- type: map_at_10
value: 39.61333333333334
- type: map_at_100
value: 40.85875
- type: map_at_1000
value: 40.96791666666667
- type: map_at_3
value: 36.48874999999999
- type: map_at_5
value: 38.24341666666667
- type: mrr_at_1
value: 34.80258333333334
- type: mrr_at_10
value: 43.783
- type: mrr_at_100
value: 44.591833333333334
- type: mrr_at_1000
value: 44.64208333333333
- type: mrr_at_3
value: 41.38974999999999
- type: mrr_at_5
value: 42.74566666666667
- type: ndcg_at_1
value: 34.80258333333334
- type: ndcg_at_10
value: 45.2705
- type: ndcg_at_100
value: 50.31224999999999
- type: ndcg_at_1000
value: 52.27916666666667
- type: ndcg_at_3
value: 40.2745
- type: ndcg_at_5
value: 42.61575
- type: precision_at_1
value: 34.80258333333334
- type: precision_at_10
value: 7.97075
- type: precision_at_100
value: 1.2400000000000002
- type: precision_at_1000
value: 0.1595
- type: precision_at_3
value: 18.627583333333337
- type: precision_at_5
value: 13.207000000000003
- type: recall_at_1
value: 29.455416666666668
- type: recall_at_10
value: 57.66091666666665
- type: recall_at_100
value: 79.51966666666665
- type: recall_at_1000
value: 93.01883333333333
- type: recall_at_3
value: 43.580416666666665
- type: recall_at_5
value: 49.7025
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.569
- type: map_at_10
value: 34.73
- type: map_at_100
value: 35.708
- type: map_at_1000
value: 35.808
- type: map_at_3
value: 32.62
- type: map_at_5
value: 33.556999999999995
- type: mrr_at_1
value: 31.135
- type: mrr_at_10
value: 37.833
- type: mrr_at_100
value: 38.68
- type: mrr_at_1000
value: 38.749
- type: mrr_at_3
value: 35.915
- type: mrr_at_5
value: 36.751
- type: ndcg_at_1
value: 31.135
- type: ndcg_at_10
value: 39.047
- type: ndcg_at_100
value: 43.822
- type: ndcg_at_1000
value: 46.249
- type: ndcg_at_3
value: 35.115
- type: ndcg_at_5
value: 36.49
- type: precision_at_1
value: 31.135
- type: precision_at_10
value: 6.058
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 15.031
- type: precision_at_5
value: 10.030999999999999
- type: recall_at_1
value: 27.569
- type: recall_at_10
value: 49.332
- type: recall_at_100
value: 70.967
- type: recall_at_1000
value: 88.876
- type: recall_at_3
value: 37.858999999999995
- type: recall_at_5
value: 41.589
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.677
- type: map_at_10
value: 28.097
- type: map_at_100
value: 29.24
- type: map_at_1000
value: 29.365000000000002
- type: map_at_3
value: 25.566
- type: map_at_5
value: 26.852999999999998
- type: mrr_at_1
value: 23.882
- type: mrr_at_10
value: 31.851000000000003
- type: mrr_at_100
value: 32.757
- type: mrr_at_1000
value: 32.83
- type: mrr_at_3
value: 29.485
- type: mrr_at_5
value: 30.744
- type: ndcg_at_1
value: 23.882
- type: ndcg_at_10
value: 33.154
- type: ndcg_at_100
value: 38.491
- type: ndcg_at_1000
value: 41.274
- type: ndcg_at_3
value: 28.648
- type: ndcg_at_5
value: 30.519000000000002
- type: precision_at_1
value: 23.882
- type: precision_at_10
value: 6.117999999999999
- type: precision_at_100
value: 1.0330000000000001
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 13.73
- type: precision_at_5
value: 9.794
- type: recall_at_1
value: 19.677
- type: recall_at_10
value: 44.444
- type: recall_at_100
value: 68.477
- type: recall_at_1000
value: 88.23
- type: recall_at_3
value: 31.708
- type: recall_at_5
value: 36.599
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.489
- type: map_at_10
value: 40.883
- type: map_at_100
value: 42.058
- type: map_at_1000
value: 42.152
- type: map_at_3
value: 37.525999999999996
- type: map_at_5
value: 39.753
- type: mrr_at_1
value: 35.541
- type: mrr_at_10
value: 44.842999999999996
- type: mrr_at_100
value: 45.673
- type: mrr_at_1000
value: 45.723
- type: mrr_at_3
value: 42.397
- type: mrr_at_5
value: 43.937
- type: ndcg_at_1
value: 35.541
- type: ndcg_at_10
value: 46.504
- type: ndcg_at_100
value: 51.637
- type: ndcg_at_1000
value: 53.535
- type: ndcg_at_3
value: 41.127
- type: ndcg_at_5
value: 44.17
- type: precision_at_1
value: 35.541
- type: precision_at_10
value: 7.864
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 18.688
- type: precision_at_5
value: 13.507
- type: recall_at_1
value: 30.489
- type: recall_at_10
value: 59.378
- type: recall_at_100
value: 81.38300000000001
- type: recall_at_1000
value: 94.294
- type: recall_at_3
value: 44.946000000000005
- type: recall_at_5
value: 52.644999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.981
- type: map_at_10
value: 39.688
- type: map_at_100
value: 41.400999999999996
- type: map_at_1000
value: 41.634
- type: map_at_3
value: 36.047000000000004
- type: map_at_5
value: 38.064
- type: mrr_at_1
value: 35.375
- type: mrr_at_10
value: 44.169000000000004
- type: mrr_at_100
value: 45.07
- type: mrr_at_1000
value: 45.113
- type: mrr_at_3
value: 41.502
- type: mrr_at_5
value: 43.034
- type: ndcg_at_1
value: 35.375
- type: ndcg_at_10
value: 45.959
- type: ndcg_at_100
value: 51.688
- type: ndcg_at_1000
value: 53.714
- type: ndcg_at_3
value: 40.457
- type: ndcg_at_5
value: 43.08
- type: precision_at_1
value: 35.375
- type: precision_at_10
value: 8.953
- type: precision_at_100
value: 1.709
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 18.775
- type: precision_at_5
value: 14.032
- type: recall_at_1
value: 29.981
- type: recall_at_10
value: 57.896
- type: recall_at_100
value: 83.438
- type: recall_at_1000
value: 95.608
- type: recall_at_3
value: 42.327
- type: recall_at_5
value: 49.069
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.59
- type: map_at_10
value: 32.999
- type: map_at_100
value: 33.987
- type: map_at_1000
value: 34.085
- type: map_at_3
value: 30.013
- type: map_at_5
value: 31.673000000000002
- type: mrr_at_1
value: 26.802
- type: mrr_at_10
value: 35.167
- type: mrr_at_100
value: 36.001
- type: mrr_at_1000
value: 36.071999999999996
- type: mrr_at_3
value: 32.562999999999995
- type: mrr_at_5
value: 34.014
- type: ndcg_at_1
value: 26.802
- type: ndcg_at_10
value: 38.21
- type: ndcg_at_100
value: 43.086999999999996
- type: ndcg_at_1000
value: 45.509
- type: ndcg_at_3
value: 32.452999999999996
- type: ndcg_at_5
value: 35.191
- type: precision_at_1
value: 26.802
- type: precision_at_10
value: 5.989
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 13.617
- type: precision_at_5
value: 9.797
- type: recall_at_1
value: 24.59
- type: recall_at_10
value: 52.298
- type: recall_at_100
value: 74.443
- type: recall_at_1000
value: 92.601
- type: recall_at_3
value: 36.888
- type: recall_at_5
value: 43.37
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.798
- type: map_at_10
value: 15.983
- type: map_at_100
value: 17.18
- type: map_at_1000
value: 17.329
- type: map_at_3
value: 13.594000000000001
- type: map_at_5
value: 14.984
- type: mrr_at_1
value: 21.564
- type: mrr_at_10
value: 31.415
- type: mrr_at_100
value: 32.317
- type: mrr_at_1000
value: 32.376
- type: mrr_at_3
value: 28.360000000000003
- type: mrr_at_5
value: 30.194
- type: ndcg_at_1
value: 21.564
- type: ndcg_at_10
value: 22.762
- type: ndcg_at_100
value: 28.199
- type: ndcg_at_1000
value: 31.284
- type: ndcg_at_3
value: 18.746
- type: ndcg_at_5
value: 20.434
- type: precision_at_1
value: 21.564
- type: precision_at_10
value: 6.755999999999999
- type: precision_at_100
value: 1.258
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.541
- type: recall_at_1
value: 9.798
- type: recall_at_10
value: 27.407999999999998
- type: recall_at_100
value: 46.659
- type: recall_at_1000
value: 64.132
- type: recall_at_3
value: 17.541999999999998
- type: recall_at_5
value: 22.137999999999998
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.276
- type: map_at_10
value: 18.003
- type: map_at_100
value: 23.759
- type: map_at_1000
value: 25.105
- type: map_at_3
value: 13.812
- type: map_at_5
value: 15.659999999999998
- type: mrr_at_1
value: 63.0
- type: mrr_at_10
value: 71.812
- type: mrr_at_100
value: 72.205
- type: mrr_at_1000
value: 72.21300000000001
- type: mrr_at_3
value: 70.375
- type: mrr_at_5
value: 71.188
- type: ndcg_at_1
value: 50.5
- type: ndcg_at_10
value: 36.954
- type: ndcg_at_100
value: 40.083999999999996
- type: ndcg_at_1000
value: 47.661
- type: ndcg_at_3
value: 42.666
- type: ndcg_at_5
value: 39.581
- type: precision_at_1
value: 63.0
- type: precision_at_10
value: 28.249999999999996
- type: precision_at_100
value: 8.113
- type: precision_at_1000
value: 1.7149999999999999
- type: precision_at_3
value: 47.083000000000006
- type: precision_at_5
value: 38.65
- type: recall_at_1
value: 8.276
- type: recall_at_10
value: 23.177
- type: recall_at_100
value: 45.321
- type: recall_at_1000
value: 68.742
- type: recall_at_3
value: 15.473
- type: recall_at_5
value: 18.276
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 55.605000000000004
- type: f1
value: 49.86208997523934
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 80.079
- type: map_at_10
value: 85.143
- type: map_at_100
value: 85.287
- type: map_at_1000
value: 85.297
- type: map_at_3
value: 84.533
- type: map_at_5
value: 84.953
- type: mrr_at_1
value: 86.424
- type: mrr_at_10
value: 91.145
- type: mrr_at_100
value: 91.212
- type: mrr_at_1000
value: 91.213
- type: mrr_at_3
value: 90.682
- type: mrr_at_5
value: 91.013
- type: ndcg_at_1
value: 86.424
- type: ndcg_at_10
value: 88.175
- type: ndcg_at_100
value: 88.77199999999999
- type: ndcg_at_1000
value: 88.967
- type: ndcg_at_3
value: 87.265
- type: ndcg_at_5
value: 87.813
- type: precision_at_1
value: 86.424
- type: precision_at_10
value: 10.012
- type: precision_at_100
value: 1.042
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 32.228
- type: precision_at_5
value: 19.724
- type: recall_at_1
value: 80.079
- type: recall_at_10
value: 91.96600000000001
- type: recall_at_100
value: 94.541
- type: recall_at_1000
value: 95.824
- type: recall_at_3
value: 89.213
- type: recall_at_5
value: 90.791
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.006999999999998
- type: map_at_10
value: 36.923
- type: map_at_100
value: 38.932
- type: map_at_1000
value: 39.096
- type: map_at_3
value: 32.322
- type: map_at_5
value: 35.119
- type: mrr_at_1
value: 45.37
- type: mrr_at_10
value: 53.418
- type: mrr_at_100
value: 54.174
- type: mrr_at_1000
value: 54.20700000000001
- type: mrr_at_3
value: 51.132
- type: mrr_at_5
value: 52.451
- type: ndcg_at_1
value: 45.37
- type: ndcg_at_10
value: 44.799
- type: ndcg_at_100
value: 51.605000000000004
- type: ndcg_at_1000
value: 54.30500000000001
- type: ndcg_at_3
value: 41.33
- type: ndcg_at_5
value: 42.608000000000004
- type: precision_at_1
value: 45.37
- type: precision_at_10
value: 12.33
- type: precision_at_100
value: 1.9349999999999998
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 27.828999999999997
- type: precision_at_5
value: 20.432
- type: recall_at_1
value: 23.006999999999998
- type: recall_at_10
value: 51.06699999999999
- type: recall_at_100
value: 75.917
- type: recall_at_1000
value: 92.331
- type: recall_at_3
value: 36.544
- type: recall_at_5
value: 43.449
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.196999999999996
- type: map_at_10
value: 55.554
- type: map_at_100
value: 56.309
- type: map_at_1000
value: 56.37799999999999
- type: map_at_3
value: 53.123
- type: map_at_5
value: 54.626
- type: mrr_at_1
value: 76.39399999999999
- type: mrr_at_10
value: 81.75
- type: mrr_at_100
value: 81.973
- type: mrr_at_1000
value: 81.982
- type: mrr_at_3
value: 80.79499999999999
- type: mrr_at_5
value: 81.393
- type: ndcg_at_1
value: 76.39399999999999
- type: ndcg_at_10
value: 64.14800000000001
- type: ndcg_at_100
value: 66.90899999999999
- type: ndcg_at_1000
value: 68.277
- type: ndcg_at_3
value: 60.529999999999994
- type: ndcg_at_5
value: 62.513
- type: precision_at_1
value: 76.39399999999999
- type: precision_at_10
value: 12.967999999999998
- type: precision_at_100
value: 1.5150000000000001
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 37.884
- type: precision_at_5
value: 24.294
- type: recall_at_1
value: 38.196999999999996
- type: recall_at_10
value: 64.84100000000001
- type: recall_at_100
value: 75.726
- type: recall_at_1000
value: 84.794
- type: recall_at_3
value: 56.826
- type: recall_at_5
value: 60.736000000000004
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 82.3912
- type: ap
value: 76.3949298163793
- type: f1
value: 82.30848699417406
- task:
type: Retrieval
dataset:
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name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
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value: 19.454
- type: map_at_10
value: 31.22
- type: map_at_100
value: 32.475
- type: map_at_1000
value: 32.532
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value: 27.419
- type: map_at_5
value: 29.608
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_5
value: 30.218
- type: ndcg_at_1
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- type: ndcg_at_10
value: 38.0
- type: ndcg_at_100
value: 44.038
- type: ndcg_at_1000
value: 45.43
- type: ndcg_at_3
value: 30.219
- type: ndcg_at_5
value: 34.127
- type: precision_at_1
value: 20.072000000000003
- type: precision_at_10
value: 6.159
- type: precision_at_100
value: 0.9169999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.071
- type: precision_at_5
value: 9.814
- type: recall_at_1
value: 19.454
- type: recall_at_10
value: 58.931
- type: recall_at_100
value: 86.886
- type: recall_at_1000
value: 97.425
- type: recall_at_3
value: 37.697
- type: recall_at_5
value: 47.101
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type: Classification
dataset:
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name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 90.46283629730961
- type: f1
value: 90.22448402668293
- task:
type: Classification
dataset:
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name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 86.91462383770076
- type: f1
value: 85.77767304705436
- task:
type: Classification
dataset:
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name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 87.73849232821881
- type: f1
value: 87.33680109229385
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 86.22298778578141
- type: f1
value: 85.88868176519013
- task:
type: Classification
dataset:
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config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 82.91860882036572
- type: f1
value: 81.38044567838352
- task:
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dataset:
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config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 69.90235081374323
- type: f1
value: 68.12897827044782
- task:
type: Classification
dataset:
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config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 66.0031919744642
- type: f1
value: 48.13490278120492
- task:
type: Classification
dataset:
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config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 63.260073260073256
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value: 42.627167415555505
- task:
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dataset:
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split: test
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metrics:
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value: 44.90527231209402
- task:
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dataset:
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config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 59.42687128092702
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- task:
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dataset:
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split: test
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metrics:
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value: 59.078522768017216
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value: 40.398016878580734
- task:
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dataset:
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config: th
split: test
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metrics:
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value: 43.750452079565996
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value: 28.985320742729865
- task:
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dataset:
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config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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metrics:
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- task:
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dataset:
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config: ar
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metrics:
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- task:
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dataset:
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config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 50.0941492938803
- type: f1
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- task:
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dataset:
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config: bn
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- type: f1
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- task:
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dataset:
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config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: da
split: test
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metrics:
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value: 52.41516089202158
- task:
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dataset:
type: mteb/amazon_massive_intent
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config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 61.86953597848015
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- task:
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dataset:
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metrics:
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- task:
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dataset:
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metrics:
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- task:
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dataset:
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metrics:
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dataset:
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metrics:
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- task:
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dataset:
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metrics:
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value: 45.841102061239184
- task:
type: Classification
dataset:
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config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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- task:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 63.39946200403498
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- task:
type: Classification
dataset:
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metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 62.25355539317913
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 51.185007148328545
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fr)
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 61.55346334902488
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value: 61.25819823057803
- task:
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dataset:
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config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 47.114996637525216
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value: 45.20428169546973
- task:
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dataset:
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config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 56.83254875588434
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value: 56.00919757601416
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dataset:
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config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 69.57969065232012
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value: 69.17378512156806
- task:
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dataset:
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config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 64.02488231338263
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dataset:
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config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
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value: 29.71446786877363
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config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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value: 28.003624498407547
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dataset:
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config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
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value: 31.29671894458151
- type: mrr
value: 32.44455140124599
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dataset:
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config: default
split: test
revision: None
metrics:
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value: 6.127
- type: map_at_10
value: 13.047
- type: map_at_100
value: 15.754000000000001
- type: map_at_1000
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value: 45.511
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value: 54.75600000000001
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value: 55.33
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value: 55.374
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value: 53.147999999999996
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- type: ndcg_at_1
value: 43.653
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value: 33.936
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value: 29.952
- type: ndcg_at_1000
value: 38.356
- type: ndcg_at_3
value: 40.018
- type: ndcg_at_5
value: 37.102000000000004
- type: precision_at_1
value: 45.511
- type: precision_at_10
value: 24.768
- type: precision_at_100
value: 7.13
- type: precision_at_1000
value: 1.928
- type: precision_at_3
value: 37.461
- type: precision_at_5
value: 31.703
- type: recall_at_1
value: 6.127
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value: 16.512999999999998
- type: recall_at_100
value: 29.057
- type: recall_at_1000
value: 59.25899999999999
- type: recall_at_3
value: 10.940999999999999
- type: recall_at_5
value: 12.925
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
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value: 32.228
- type: map_at_10
value: 47.56
- type: map_at_100
value: 48.539
- type: map_at_1000
value: 48.567
- type: map_at_3
value: 43.214999999999996
- type: map_at_5
value: 45.799
- type: mrr_at_1
value: 36.53
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value: 50.004000000000005
- type: mrr_at_100
value: 50.737
- type: mrr_at_1000
value: 50.758
- type: mrr_at_3
value: 46.543
- type: mrr_at_5
value: 48.672
- type: ndcg_at_1
value: 36.501
- type: ndcg_at_10
value: 55.103
- type: ndcg_at_100
value: 59.156
- type: ndcg_at_1000
value: 59.821999999999996
- type: ndcg_at_3
value: 47.089
- type: ndcg_at_5
value: 51.35999999999999
- type: precision_at_1
value: 36.501
- type: precision_at_10
value: 9.046999999999999
- type: precision_at_100
value: 1.13
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 21.398
- type: precision_at_5
value: 15.307
- type: recall_at_1
value: 32.228
- type: recall_at_10
value: 75.608
- type: recall_at_100
value: 93.062
- type: recall_at_1000
value: 98.059
- type: recall_at_3
value: 55.021
- type: recall_at_5
value: 64.873
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.623
- type: map_at_10
value: 84.705
- type: map_at_100
value: 85.333
- type: map_at_1000
value: 85.348
- type: map_at_3
value: 81.736
- type: map_at_5
value: 83.616
- type: mrr_at_1
value: 81.28
- type: mrr_at_10
value: 87.518
- type: mrr_at_100
value: 87.619
- type: mrr_at_1000
value: 87.62
- type: mrr_at_3
value: 86.545
- type: mrr_at_5
value: 87.238
- type: ndcg_at_1
value: 81.28999999999999
- type: ndcg_at_10
value: 88.412
- type: ndcg_at_100
value: 89.603
- type: ndcg_at_1000
value: 89.696
- type: ndcg_at_3
value: 85.563
- type: ndcg_at_5
value: 87.17
- type: precision_at_1
value: 81.28999999999999
- type: precision_at_10
value: 13.439
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.437
- type: precision_at_5
value: 24.662
- type: recall_at_1
value: 70.623
- type: recall_at_10
value: 95.531
- type: recall_at_100
value: 99.58
- type: recall_at_1000
value: 99.978
- type: recall_at_3
value: 87.368
- type: recall_at_5
value: 91.898
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 49.53241309124786
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 59.712004482915994
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.313
- type: map_at_10
value: 13.447000000000001
- type: map_at_100
value: 15.491
- type: map_at_1000
value: 15.784999999999998
- type: map_at_3
value: 9.58
- type: map_at_5
value: 11.562
- type: mrr_at_1
value: 26.200000000000003
- type: mrr_at_10
value: 37.212
- type: mrr_at_100
value: 38.190000000000005
- type: mrr_at_1000
value: 38.242
- type: mrr_at_3
value: 34.067
- type: mrr_at_5
value: 35.862
- type: ndcg_at_1
value: 26.200000000000003
- type: ndcg_at_10
value: 21.979000000000003
- type: ndcg_at_100
value: 29.726999999999997
- type: ndcg_at_1000
value: 34.766000000000005
- type: ndcg_at_3
value: 21.16
- type: ndcg_at_5
value: 18.478
- type: precision_at_1
value: 26.200000000000003
- type: precision_at_10
value: 11.25
- type: precision_at_100
value: 2.241
- type: precision_at_1000
value: 0.345
- type: precision_at_3
value: 19.633
- type: precision_at_5
value: 16.14
- type: recall_at_1
value: 5.313
- type: recall_at_10
value: 22.808
- type: recall_at_100
value: 45.540000000000006
- type: recall_at_1000
value: 70.043
- type: recall_at_3
value: 11.932
- type: recall_at_5
value: 16.347
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 75.95540796619258
- type: cos_sim_spearman
value: 76.49462277620303
- type: euclidean_pearson
value: 71.67643435507317
- type: euclidean_spearman
value: 76.4915921108082
- type: manhattan_pearson
value: 71.71412560074847
- type: manhattan_spearman
value: 76.46738312094736
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.48773267615617
- type: cos_sim_spearman
value: 74.99867664033701
- type: euclidean_pearson
value: 76.0885798115032
- type: euclidean_spearman
value: 74.99438208715942
- type: manhattan_pearson
value: 76.09382557464033
- type: manhattan_spearman
value: 74.96139353538533
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.19022560804167
- type: cos_sim_spearman
value: 87.9128142106699
- type: euclidean_pearson
value: 85.51390183763914
- type: euclidean_spearman
value: 87.89995488057309
- type: manhattan_pearson
value: 85.44945034816052
- type: manhattan_spearman
value: 87.791458898378
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.17877898640924
- type: cos_sim_spearman
value: 82.25544088807465
- type: euclidean_pearson
value: 82.36395988835416
- type: euclidean_spearman
value: 82.26359924974219
- type: manhattan_pearson
value: 82.39219808999891
- type: manhattan_spearman
value: 82.27757404868157
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.66865350602554
- type: cos_sim_spearman
value: 87.87150169810872
- type: euclidean_pearson
value: 85.41520650056647
- type: euclidean_spearman
value: 87.86636613654022
- type: manhattan_pearson
value: 85.38710485867502
- type: manhattan_spearman
value: 87.83513424575301
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.75527643407175
- type: cos_sim_spearman
value: 80.9239008594745
- type: euclidean_pearson
value: 79.37682746800515
- type: euclidean_spearman
value: 80.91978947194092
- type: manhattan_pearson
value: 79.38884189990698
- type: manhattan_spearman
value: 80.91771608341014
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.24344311909609
- type: cos_sim_spearman
value: 80.78933956176022
- type: euclidean_pearson
value: 76.95229806538676
- type: euclidean_spearman
value: 80.79706724032172
- type: manhattan_pearson
value: 76.90212135774246
- type: manhattan_spearman
value: 80.68727415384441
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.33891809228084
- type: cos_sim_spearman
value: 79.37912430317627
- type: euclidean_pearson
value: 72.56919843951036
- type: euclidean_spearman
value: 79.3091436905072
- type: manhattan_pearson
value: 72.4282811588754
- type: manhattan_spearman
value: 78.90144894538078
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 59.68908656739356
- type: cos_sim_spearman
value: 58.76110210983758
- type: euclidean_pearson
value: 59.14749159577439
- type: euclidean_spearman
value: 59.015997032145016
- type: manhattan_pearson
value: 57.907675340322676
- type: manhattan_spearman
value: 57.07751173022352
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.53325164873934
- type: cos_sim_spearman
value: 76.13104388846271
- type: euclidean_pearson
value: 74.61931031522006
- type: euclidean_spearman
value: 75.96875166459931
- type: manhattan_pearson
value: 74.82154350849251
- type: manhattan_spearman
value: 76.64455924104236
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.4228376590724
- type: cos_sim_spearman
value: 87.22764976624408
- type: euclidean_pearson
value: 81.94975688107507
- type: euclidean_spearman
value: 87.19193932664932
- type: manhattan_pearson
value: 82.0043964628936
- type: manhattan_spearman
value: 87.09130430957818
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 57.5627552601949
- type: cos_sim_spearman
value: 55.5263144563657
- type: euclidean_pearson
value: 57.00569241610482
- type: euclidean_spearman
value: 55.35291811479459
- type: manhattan_pearson
value: 56.99656284623506
- type: manhattan_spearman
value: 55.593673744709946
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 69.93801311909735
- type: cos_sim_spearman
value: 72.2581115470475
- type: euclidean_pearson
value: 68.24881290268563
- type: euclidean_spearman
value: 72.60813652864522
- type: manhattan_pearson
value: 67.86369874088834
- type: manhattan_spearman
value: 71.92346382988023
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.20555264114785
- type: cos_sim_spearman
value: 85.0588060013836
- type: euclidean_pearson
value: 81.78229090166155
- type: euclidean_spearman
value: 85.09687374900614
- type: manhattan_pearson
value: 81.77449099980244
- type: manhattan_spearman
value: 84.70331476222177
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 73.786793911605
- type: cos_sim_spearman
value: 75.63094397551554
- type: euclidean_pearson
value: 71.64292842519251
- type: euclidean_spearman
value: 75.60215267384011
- type: manhattan_pearson
value: 72.2124078037642
- type: manhattan_spearman
value: 76.34546028465175
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 69.62139987106455
- type: cos_sim_spearman
value: 71.35872226722493
- type: euclidean_pearson
value: 68.50103697766141
- type: euclidean_spearman
value: 71.24590187948473
- type: manhattan_pearson
value: 68.89236562525663
- type: manhattan_spearman
value: 71.77994400789173
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 71.62728174871292
- type: cos_sim_spearman
value: 71.98655715409397
- type: euclidean_pearson
value: 70.27026741609356
- type: euclidean_spearman
value: 72.14004669693777
- type: manhattan_pearson
value: 70.46335140108751
- type: manhattan_spearman
value: 72.6638254374311
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.10248717637424
- type: cos_sim_spearman
value: 68.5905931564714
- type: euclidean_pearson
value: 71.23290000423759
- type: euclidean_spearman
value: 68.6419513130457
- type: manhattan_pearson
value: 71.6886015250234
- type: manhattan_spearman
value: 69.47543660368697
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.010555056244776
- type: cos_sim_spearman
value: 60.121771179899255
- type: euclidean_pearson
value: 53.04527785573465
- type: euclidean_spearman
value: 60.121771179899255
- type: manhattan_pearson
value: 52.931480071124234
- type: manhattan_spearman
value: 60.03868409331775
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.6833028374664
- type: cos_sim_spearman
value: 68.57396263856863
- type: euclidean_pearson
value: 68.30905084522986
- type: euclidean_spearman
value: 68.57396263856863
- type: manhattan_pearson
value: 70.91400657516918
- type: manhattan_spearman
value: 72.72240857808112
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 36.948290734279645
- type: cos_sim_spearman
value: 42.07722031011005
- type: euclidean_pearson
value: 22.539446972018467
- type: euclidean_spearman
value: 42.07722031011005
- type: manhattan_pearson
value: 24.119402246951786
- type: manhattan_spearman
value: 45.80525501822569
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.97840719036533
- type: cos_sim_spearman
value: 66.62430648804775
- type: euclidean_pearson
value: 66.89526587772023
- type: euclidean_spearman
value: 66.62430648804775
- type: manhattan_pearson
value: 68.6929895225091
- type: manhattan_spearman
value: 68.91772708432867
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.65098289103698
- type: cos_sim_spearman
value: 57.436674670689214
- type: euclidean_pearson
value: 51.79149892785239
- type: euclidean_spearman
value: 57.436674670689214
- type: manhattan_pearson
value: 52.64807953938707
- type: manhattan_spearman
value: 58.94583987372767
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.669531297510225
- type: cos_sim_spearman
value: 61.71342510003327
- type: euclidean_pearson
value: 55.821871433553504
- type: euclidean_spearman
value: 61.71342510003327
- type: manhattan_pearson
value: 57.77073441351117
- type: manhattan_spearman
value: 65.20759033207
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.34728960310699
- type: cos_sim_spearman
value: 64.03565302589584
- type: euclidean_pearson
value: 61.958942333930544
- type: euclidean_spearman
value: 64.03565302589584
- type: manhattan_pearson
value: 64.65072672727923
- type: manhattan_spearman
value: 67.82569969943107
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 82.47120815594353
- type: cos_sim_spearman
value: 81.46916544955101
- type: euclidean_pearson
value: 79.21753533489019
- type: euclidean_spearman
value: 81.46916544955101
- type: manhattan_pearson
value: 78.26605518839271
- type: manhattan_spearman
value: 81.29749169339514
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.31467231933632
- type: cos_sim_spearman
value: 53.36160506603274
- type: euclidean_pearson
value: 64.98434169416196
- type: euclidean_spearman
value: 53.36160506603274
- type: manhattan_pearson
value: 69.6837006629638
- type: manhattan_spearman
value: 60.85384324700893
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 79.99425127770438
- type: cos_sim_spearman
value: 77.41308957007035
- type: euclidean_pearson
value: 79.69441265626801
- type: euclidean_spearman
value: 77.41308957007035
- type: manhattan_pearson
value: 80.3726291667624
- type: manhattan_spearman
value: 79.0414050644631
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 79.13469287716659
- type: cos_sim_spearman
value: 79.27976881582065
- type: euclidean_pearson
value: 77.65964425780172
- type: euclidean_spearman
value: 79.27976881582065
- type: manhattan_pearson
value: 77.64158710257945
- type: manhattan_spearman
value: 79.22242281895944
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 76.303314995599
- type: cos_sim_spearman
value: 77.4991345414335
- type: euclidean_pearson
value: 74.88826621426401
- type: euclidean_spearman
value: 77.4991345414335
- type: manhattan_pearson
value: 77.70223488989319
- type: manhattan_spearman
value: 79.69746987627822
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.87814957197239
- type: cos_sim_spearman
value: 69.86785751801642
- type: euclidean_pearson
value: 68.68630146548654
- type: euclidean_spearman
value: 69.8615799070054
- type: manhattan_pearson
value: 61.83743315022061
- type: manhattan_spearman
value: 64.35346450347738
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 74.1484689923211
- type: cos_sim_spearman
value: 74.69046355179742
- type: euclidean_pearson
value: 73.03951899271793
- type: euclidean_spearman
value: 74.69820632954205
- type: manhattan_pearson
value: 73.36810146930709
- type: manhattan_spearman
value: 75.33154135287258
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.43125921362742
- type: cos_sim_spearman
value: 58.25341239774093
- type: euclidean_pearson
value: 48.00689582162098
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 46.11721778230745
- type: manhattan_spearman
value: 55.026889052448134
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 40.066205533538046
- type: cos_sim_spearman
value: 48.46991890841381
- type: euclidean_pearson
value: 42.29606506858651
- type: euclidean_spearman
value: 48.34674249441531
- type: manhattan_pearson
value: 41.70680990555484
- type: manhattan_spearman
value: 47.54609580342499
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 82.26527545520592
- type: cos_sim_spearman
value: 73.24670207647144
- type: euclidean_pearson
value: 81.78699781584893
- type: euclidean_spearman
value: 73.24670207647144
- type: manhattan_pearson
value: 83.14172292187807
- type: manhattan_spearman
value: 73.24670207647144
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 81.51438108053523
- type: cos_sim_spearman
value: 81.9481311864648
- type: euclidean_pearson
value: 78.6683040592179
- type: euclidean_spearman
value: 81.9535649926177
- type: manhattan_pearson
value: 78.65396325536754
- type: manhattan_spearman
value: 81.96918240343872
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.6689275068653
- type: mrr
value: 95.021337594867
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.193999999999996
- type: map_at_10
value: 65.814
- type: map_at_100
value: 66.428
- type: map_at_1000
value: 66.447
- type: map_at_3
value: 63.304
- type: map_at_5
value: 64.64
- type: mrr_at_1
value: 57.99999999999999
- type: mrr_at_10
value: 66.957
- type: mrr_at_100
value: 67.405
- type: mrr_at_1000
value: 67.422
- type: mrr_at_3
value: 65.0
- type: mrr_at_5
value: 66.183
- type: ndcg_at_1
value: 57.99999999999999
- type: ndcg_at_10
value: 70.523
- type: ndcg_at_100
value: 72.987
- type: ndcg_at_1000
value: 73.605
- type: ndcg_at_3
value: 66.268
- type: ndcg_at_5
value: 68.27600000000001
- type: precision_at_1
value: 57.99999999999999
- type: precision_at_10
value: 9.467
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 17.2
- type: recall_at_1
value: 55.193999999999996
- type: recall_at_10
value: 83.52199999999999
- type: recall_at_100
value: 94.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 71.989
- type: recall_at_5
value: 77.31700000000001
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.73465346534654
- type: cos_sim_ap
value: 92.91719494015508
- type: cos_sim_f1
value: 86.46200301962756
- type: cos_sim_precision
value: 87.03140830800406
- type: cos_sim_recall
value: 85.9
- type: dot_accuracy
value: 99.73663366336633
- type: dot_ap
value: 92.90802848215259
- type: dot_f1
value: 86.46200301962756
- type: dot_precision
value: 87.03140830800406
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.73465346534654
- type: euclidean_ap
value: 92.91627363446204
- type: euclidean_f1
value: 86.43469490670702
- type: euclidean_precision
value: 87.18209562563581
- type: euclidean_recall
value: 85.7
- type: manhattan_accuracy
value: 99.73663366336633
- type: manhattan_ap
value: 92.90219877406929
- type: manhattan_f1
value: 86.31471040492056
- type: manhattan_precision
value: 88.53838065194533
- type: manhattan_recall
value: 84.2
- type: max_accuracy
value: 99.73663366336633
- type: max_ap
value: 92.91719494015508
- type: max_f1
value: 86.46200301962756
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.73098998430779
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.64256206757585
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.749150614295694
- type: mrr
value: 55.78880984211867
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 28.863577054305907
- type: cos_sim_spearman
value: 27.538596944829774
- type: dot_pearson
value: 28.93043755116643
- type: dot_spearman
value: 27.733110516733987
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22899999999999998
- type: map_at_10
value: 2.078
- type: map_at_100
value: 12.024
- type: map_at_1000
value: 29.036
- type: map_at_3
value: 0.681
- type: map_at_5
value: 1.083
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 92.667
- type: mrr_at_100
value: 92.667
- type: mrr_at_1000
value: 92.667
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 92.667
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 80.746
- type: ndcg_at_100
value: 61.090999999999994
- type: ndcg_at_1000
value: 55.034000000000006
- type: ndcg_at_3
value: 82.419
- type: ndcg_at_5
value: 81.018
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 86.2
- type: precision_at_100
value: 62.68
- type: precision_at_1000
value: 24.032
- type: precision_at_3
value: 88.667
- type: precision_at_5
value: 86.0
- type: recall_at_1
value: 0.22899999999999998
- type: recall_at_10
value: 2.263
- type: recall_at_100
value: 15.238999999999999
- type: recall_at_1000
value: 51.937
- type: recall_at_3
value: 0.719
- type: recall_at_5
value: 1.15
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 19.400000000000002
- type: f1
value: 15.386076064970075
- type: precision
value: 14.253878834615676
- type: recall
value: 19.400000000000002
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.19653179190752
- type: f1
value: 37.726396917148364
- type: precision
value: 36.14643545279384
- type: recall
value: 42.19653179190752
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 18.536585365853657
- type: f1
value: 13.512010347376199
- type: precision
value: 12.034068912117693
- type: recall
value: 18.536585365853657
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.69999999999999
- type: f1
value: 77.37888888888888
- type: precision
value: 75.49583333333332
- type: recall
value: 81.69999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.56666666666666
- type: precision
value: 96.16666666666667
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.0
- type: f1
value: 87.22333333333333
- type: precision
value: 85.89166666666667
- type: recall
value: 90.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 64.7
- type: f1
value: 59.10904761904763
- type: precision
value: 56.91968253968254
- type: recall
value: 64.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.80597014925373
- type: f1
value: 30.890784174366264
- type: precision
value: 28.327114427860696
- type: recall
value: 38.80597014925373
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.900000000000006
- type: f1
value: 48.294138583638585
- type: precision
value: 46.333495670995674
- type: recall
value: 53.900000000000006
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.707317073170733
- type: f1
value: 8.999999999999998
- type: precision
value: 8.175377468060395
- type: recall
value: 11.707317073170733
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 15.9
- type: f1
value: 12.451226269430602
- type: precision
value: 11.404807799760325
- type: recall
value: 15.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 41.919805589307416
- type: f1
value: 35.880619060297064
- type: precision
value: 33.77682308241239
- type: recall
value: 41.919805589307416
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.956521739130434
- type: f1
value: 9.098715976676996
- type: precision
value: 8.659935858401333
- type: recall
value: 10.956521739130434
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.652173913043478
- type: f1
value: 9.154324883225136
- type: precision
value: 8.505898125360801
- type: recall
value: 11.652173913043478
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.700000000000001
- type: f1
value: 7.431679431679432
- type: precision
value: 6.799925118740907
- type: recall
value: 9.700000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.5
- type: f1
value: 72.39999999999999
- type: precision
value: 70.13444444444444
- type: recall
value: 77.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.548854041013269
- type: f1
value: 4.233155465362944
- type: precision
value: 3.948150869646547
- type: recall
value: 5.548854041013269
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.5
- type: f1
value: 67.35333333333332
- type: precision
value: 64.63666666666666
- type: recall
value: 73.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 27.700000000000003
- type: f1
value: 21.152765495941964
- type: precision
value: 19.27832403707404
- type: recall
value: 27.700000000000003
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.1
- type: f1
value: 41.21001443001443
- type: precision
value: 38.628495670995676
- type: recall
value: 48.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 40.0
- type: f1
value: 34.32060003488575
- type: precision
value: 32.32134353741497
- type: recall
value: 40.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.800000000000001
- type: f1
value: 4.3954389450190465
- type: precision
value: 3.893838027469606
- type: recall
value: 6.800000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 51.800000000000004
- type: f1
value: 45.04222943722944
- type: precision
value: 42.541984126984126
- type: recall
value: 51.800000000000004
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.1
- type: f1
value: 79.20675324675324
- type: precision
value: 77.44944444444444
- type: recall
value: 83.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.8
- type: f1
value: 60.25746031746031
- type: precision
value: 57.55250000000001
- type: recall
value: 66.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.6
- type: f1
value: 56.73421356421356
- type: precision
value: 54.02218253968254
- type: recall
value: 63.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.599999999999998
- type: f1
value: 13.17699134199134
- type: precision
value: 11.77444805194805
- type: recall
value: 17.599999999999998
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 2.0
- type: f1
value: 1.3126923076923078
- type: precision
value: 1.104952380952381
- type: recall
value: 2.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.3
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.69999999999999
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type: BitextMining
dataset:
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config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 0.6738544474393532
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 71.7948717948718
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dataset:
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config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 13.863636363636363
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value: 13.863636363636363
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 62.473794549266245
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 40.0
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value: 40.0
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 60.311284046692606
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value: 51.757921067259595
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value: 60.311284046692606
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 43.58974358974359
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value: 34.75783475783476
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value: 43.58974358974359
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 56.49999999999999
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value: 56.49999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 71.5
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value: 62.71261904761904
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value: 71.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 11.448598130841122
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value: 11.448598130841122
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 13.5
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value: 13.5
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 82.19999999999999
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value: 76.54291666666667
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value: 82.19999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 53.5
- type: f1
value: 47.48505411255411
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value: 53.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 61.1
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value: 52.16455433455434
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value: 61.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.1
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value: 85.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.2
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value: 89.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 13.600000000000001
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 79.0
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value: 79.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.3
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value: 8.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 26.3
- type: f1
value: 20.851341154819416
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value: 26.3
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 41.964285714285715
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value: 36.38605442176871
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value: 34.523809523809526
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value: 41.964285714285715
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 26.454445664105382
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value: 26.454445664105382
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 2.8000000000000003
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value: 2.8000000000000003
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.5
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value: 89.39
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value: 88.425
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value: 91.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.5
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value: 88.36666666666667
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value: 91.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.2
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value: 6.672282438325198
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value: 6.046073589145276
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value: 9.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 45.2
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value: 39.12095238095238
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value: 45.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pol-eng)
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.8
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value: 81.825
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value: 86.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 13.5
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value: 13.5
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (aze-eng)
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 33.4
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value: 33.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (vie-eng)
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 57.699999999999996
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value: 57.699999999999996
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nno-eng)
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 62.2
- type: f1
value: 56.0098625351257
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value: 53.691914098972916
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value: 62.2
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cha-eng)
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 22.798053527980535
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value: 21.107055961070557
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value: 27.00729927007299
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 6.2
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value: 4.295544090473964
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value: 3.913153952193392
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value: 6.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dan-eng)
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.10000000000001
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value: 72.49333333333334
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value: 70.53368637110017
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value: 77.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 15.2
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value: 10.429591693330824
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value: 9.145801926831338
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value: 15.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (amh-eng)
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 1.7857142857142856
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value: 0.3635204081632653
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value: 0.205026455026455
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value: 1.7857142857142856
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 6.4
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value: 4.8412763053939525
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value: 4.444087810337809
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value: 6.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hsb-eng)
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 43.47826086956522
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value: 37.13266949291794
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value: 34.655332590115194
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value: 43.47826086956522
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 42.0
- type: f1
value: 35.412229437229435
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value: 32.907539682539685
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value: 42.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 36.0
- type: f1
value: 30.53874458874459
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value: 28.711192408382807
- type: recall
value: 36.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.9
- type: f1
value: 5.80190114561213
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value: 5.298527531836355
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value: 7.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 49.35064935064935
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value: 41.57805638325119
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value: 38.87445887445887
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value: 49.35064935064935
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 25.572519083969464
- type: f1
value: 21.338006776938073
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value: 20.194474736459465
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value: 25.572519083969464
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.62154294032024
- type: f1
value: 74.47355652595827
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value: 72.2076661814653
- type: recall
value: 79.62154294032024
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.0
- type: f1
value: 61.80859649122807
- type: precision
value: 59.30381381381381
- type: recall
value: 68.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.93785310734463
- type: f1
value: 36.72617201306135
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value: 34.72641059505466
- type: recall
value: 42.93785310734463
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.5
- type: f1
value: 3.8651658986175113
- type: precision
value: 3.4432814407814405
- type: recall
value: 5.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.19999999999999
- type: f1
value: 63.41880952380953
- type: precision
value: 61.07913419913419
- type: recall
value: 69.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 15.4
- type: f1
value: 11.672122577122575
- type: precision
value: 10.59919974661354
- type: recall
value: 15.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.5
- type: f1
value: 51.31880452880453
- type: precision
value: 48.60550125313283
- type: recall
value: 58.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.3
- type: f1
value: 86.32666666666667
- type: precision
value: 84.98333333333333
- type: recall
value: 89.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.7
- type: f1
value: 3.8739805216757546
- type: precision
value: 3.4734608954367014
- type: recall
value: 5.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.8042895442359249
- type: f1
value: 0.7596067917783735
- type: precision
value: 0.7372654155495978
- type: recall
value: 0.8042895442359249
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jpn-eng)
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.92333333333333
- type: precision
value: 85.64166666666667
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (csb-eng)
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 26.08695652173913
- type: f1
value: 20.517863778733343
- type: precision
value: 18.901098901098898
- type: recall
value: 26.08695652173913
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 12.676056338028168
- type: f1
value: 9.526324614352783
- type: precision
value: 9.006292657908235
- type: recall
value: 12.676056338028168
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 24.910179640718564
- type: f1
value: 19.645099411566473
- type: precision
value: 17.676076418591386
- type: recall
value: 24.910179640718564
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ind-eng)
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.4
- type: f1
value: 54.64269841269841
- type: precision
value: 51.981071428571425
- type: recall
value: 61.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tuk-eng)
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.330049261083744
- type: f1
value: 9.610016420361248
- type: precision
value: 9.123781574258464
- type: recall
value: 11.330049261083744
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 27.816901408450708
- type: f1
value: 22.51925345174495
- type: precision
value: 21.10468365750056
- type: recall
value: 27.816901408450708
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swh-eng)
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.282051282051283
- type: f1
value: 7.777167097237831
- type: precision
value: 7.050109879436802
- type: recall
value: 11.282051282051283
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.0
- type: f1
value: 82.05857142857143
- type: precision
value: 80.25
- type: recall
value: 86.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 34.44676409185804
- type: f1
value: 28.296517215097587
- type: precision
value: 26.16624956236465
- type: recall
value: 34.44676409185804
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.199999999999999
- type: f1
value: 5.500051631938041
- type: precision
value: 5.164411510424442
- type: recall
value: 7.199999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.9869706840391
- type: f1
value: 65.79339227547696
- type: precision
value: 63.16503800217155
- type: recall
value: 71.9869706840391
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.89999999999999
- type: f1
value: 65.4152380952381
- type: precision
value: 63.106666666666655
- type: recall
value: 70.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.0
- type: f1
value: 17.86438197644649
- type: precision
value: 16.84469948469949
- type: recall
value: 21.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 62.20472440944882
- type: f1
value: 55.81364829396325
- type: precision
value: 53.262092238470196
- type: recall
value: 62.20472440944882
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 41.8
- type: f1
value: 34.724603174603175
- type: precision
value: 32.040277777777774
- type: recall
value: 41.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.41551246537396125
- type: f1
value: 0.3462603878116343
- type: precision
value: 0.32317636195752536
- type: recall
value: 0.41551246537396125
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.6
- type: f1
value: 81.81333333333333
- type: precision
value: 80.08333333333334
- type: recall
value: 85.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 31.73076923076923
- type: f1
value: 26.097374847374844
- type: precision
value: 24.31891025641026
- type: recall
value: 31.73076923076923
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.6
- type: f1
value: 6.598392371412457
- type: precision
value: 5.855494356434758
- type: recall
value: 9.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.5
- type: f1
value: 79.65190476190476
- type: precision
value: 77.875
- type: recall
value: 83.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.5
- type: f1
value: 75.75999999999999
- type: precision
value: 73.60333333333332
- type: recall
value: 80.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 2.1226415094339623
- type: f1
value: 1.4622641509433962
- type: precision
value: 1.2637578616352203
- type: recall
value: 2.1226415094339623
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.0
- type: f1
value: 18.111780719280716
- type: precision
value: 16.497738095238095
- type: recall
value: 23.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 4.562043795620438
- type: f1
value: 3.1632119907667358
- type: precision
value: 2.8806772100567724
- type: recall
value: 4.562043795620438
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.9
- type: f1
value: 70.57690476190476
- type: precision
value: 68.19761904761904
- type: recall
value: 75.9
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.804
- type: map_at_10
value: 11.267000000000001
- type: map_at_100
value: 17.034
- type: map_at_1000
value: 18.733
- type: map_at_3
value: 6.071
- type: map_at_5
value: 8.187
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 50.504000000000005
- type: mrr_at_100
value: 51.162
- type: mrr_at_1000
value: 51.162
- type: mrr_at_3
value: 45.918
- type: mrr_at_5
value: 49.082
- type: ndcg_at_1
value: 33.672999999999995
- type: ndcg_at_10
value: 27.478
- type: ndcg_at_100
value: 37.961
- type: ndcg_at_1000
value: 50.117
- type: ndcg_at_3
value: 30.156
- type: ndcg_at_5
value: 29.293999999999997
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 24.082
- type: precision_at_100
value: 7.632999999999999
- type: precision_at_1000
value: 1.569
- type: precision_at_3
value: 30.612000000000002
- type: precision_at_5
value: 29.387999999999998
- type: recall_at_1
value: 2.804
- type: recall_at_10
value: 17.785
- type: recall_at_100
value: 47.452
- type: recall_at_1000
value: 84.687
- type: recall_at_3
value: 6.9190000000000005
- type: recall_at_5
value: 10.807
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 74.5162
- type: ap
value: 15.022137849208509
- type: f1
value: 56.77914300422838
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.589700056593095
- type: f1
value: 59.93893560752363
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 40.11538634360855
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.97806520832091
- type: cos_sim_ap
value: 67.80381341664686
- type: cos_sim_f1
value: 63.01665268958908
- type: cos_sim_precision
value: 57.713407943822695
- type: cos_sim_recall
value: 69.39313984168865
- type: dot_accuracy
value: 83.9899862907552
- type: dot_ap
value: 67.80914960711299
- type: dot_f1
value: 63.0287144048612
- type: dot_precision
value: 57.46252444058223
- type: dot_recall
value: 69.78891820580475
- type: euclidean_accuracy
value: 83.9601835846695
- type: euclidean_ap
value: 67.79862461635126
- type: euclidean_f1
value: 63.02426882389545
- type: euclidean_precision
value: 59.64664310954063
- type: euclidean_recall
value: 66.80738786279683
- type: manhattan_accuracy
value: 83.94230196101806
- type: manhattan_ap
value: 67.78560087328111
- type: manhattan_f1
value: 63.10622881851117
- type: manhattan_precision
value: 56.63939584644431
- type: manhattan_recall
value: 71.2401055408971
- type: max_accuracy
value: 83.9899862907552
- type: max_ap
value: 67.80914960711299
- type: max_f1
value: 63.10622881851117
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.04994760740482
- type: cos_sim_ap
value: 85.71231674852108
- type: cos_sim_f1
value: 78.92350867093619
- type: cos_sim_precision
value: 74.07807645549101
- type: cos_sim_recall
value: 84.44718201416693
- type: dot_accuracy
value: 89.05188807389295
- type: dot_ap
value: 85.71776365526502
- type: dot_f1
value: 78.92055922835156
- type: dot_precision
value: 74.34152317430069
- type: dot_recall
value: 84.10070834616569
- type: euclidean_accuracy
value: 89.05188807389295
- type: euclidean_ap
value: 85.7114644968015
- type: euclidean_f1
value: 78.9458525345622
- type: euclidean_precision
value: 74.14119556397078
- type: euclidean_recall
value: 84.41638435478903
- type: manhattan_accuracy
value: 89.06547133930997
- type: manhattan_ap
value: 85.70658730333459
- type: manhattan_f1
value: 78.91009741543552
- type: manhattan_precision
value: 74.00714719169308
- type: manhattan_recall
value: 84.5087773329227
- type: max_accuracy
value: 89.06547133930997
- type: max_ap
value: 85.71776365526502
- type: max_f1
value: 78.9458525345622
---
## Bedrock Titan Text Embeddings v2
This repository contains the MTEB scores and usage examples of Bedrock Titan Text Embeddings v2. You can use the embedding model either via the Bedrock InvokeModel API or via Bedrock's batch jobs. For RAG use cases we recommend the former to embed queries during search (latency optimized) and the latter to index corpus (throughput optimized).
## Using Bedrock's InvokeModel API
```python
import json
import boto3
class TitanEmbeddings(object):
accept = "application/json"
content_type = "application/json"
def __init__(self, model_id="amazon.titan-embed-text-v2:0"):
self.bedrock = boto3.client(service_name='bedrock-runtime')
self.model_id = model_id
def __call__(self, text, dimensions, normalize=True):
"""
Returns Titan Embeddings
Args:
text (str): text to embed
dimensions (int): Number of output dimensions.
normalize (bool): Whether to return the normalized embedding or not.
Return:
List[float]: Embedding
"""
body = json.dumps({
"inputText": text,
"dimensions": dimensions,
"normalize": normalize
})
response = self.bedrock.invoke_model(
body=body, modelId=self.model_id, accept=self.accept, contentType=self.content_type
)
response_body = json.loads(response.get('body').read())
return response_body['embedding']
if __name__ == '__main__':
"""
Entrypoint for Amazon Titan Embeddings V2 - Text example.
"""
dimensions = 1024
normalize = True
titan_embeddings_v2 = TitanEmbeddings(model_id="amazon.titan-embed-text-v2:0")
input_text = "What are the different services that you offer?"
embedding = titan_embeddings_v2(input_text, dimensions, normalize)
print(f"{input_text=}")
print(f"{embedding[:10]=}")
```
## Using Bedrock's batch jobs
```python
import requests
from aws_requests_auth.boto_utils import BotoAWSRequestsAuth
region = "us-east-1"
base_uri = f"bedrock.{region}.amazonaws.com"
batch_job_uri = f"https://{base_uri}/model-invocation-job/"
# For details on how to set up an IAM role for batch inference, see
# https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference-permissions.html
role_arn = "arn:aws:iam::111122223333:role/my-batch-inference-role"
payload = {
"inputDataConfig": {
"s3InputDataConfig": {
"s3Uri": "s3://my-input-bucket/batch-input/",
"s3InputFormat": "JSONL"
}
},
"jobName": "embeddings-v2-batch-job",
"modelId": "amazon.titan-embed-text-v2:0",
"outputDataConfig": {
"s3OutputDataConfig": {
"s3Uri": "s3://my-output-bucket/batch-output/"
}
},
"roleArn": role_arn
}
request_auth = BotoAWSRequestsAuth(
aws_host=base_uri,
aws_region=region,
aws_service="bedrock"
)
response= requests.request("POST", batch_job_uri, json=payload, auth=request_auth)
print(response.json())
``` |
BioMistral/BioMistral-7B-DARE | BioMistral | 2024-02-19T15:37:21Z | 4,943 | 16 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"dare",
"medical",
"biology",
"conversational",
"en",
"fr",
"nl",
"es",
"it",
"pl",
"ro",
"de",
"dataset:pubmed",
"arxiv:2311.03099",
"arxiv:2306.01708",
"arxiv:2402.10373",
"base_model:BioMistral/BioMistral-7B",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-05T22:21:47Z | ---
base_model:
- BioMistral/BioMistral-7B
- mistralai/Mistral-7B-Instruct-v0.1
library_name: transformers
tags:
- mergekit
- merge
- dare
- medical
- biology
license: apache-2.0
datasets:
- pubmed
language:
- en
- fr
- nl
- es
- it
- pl
- ro
- de
pipeline_tag: text-generation
---
# BioMistral-7B-mistral7instruct-dare
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mistralai/Mistral-7B-Instruct-v0.1
# No parameters necessary for base model
- model: BioMistral/BioMistral-7B
parameters:
density: 0.5
weight: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
<p align="center">
<img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/>
</p>
# BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
**Abstract:**
Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges.
In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.
**Advisory Notice!** Although BioMistral is intended to encapsulate medical knowledge sourced from high-quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real-world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real-world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generation or any professional health and medical purposes.
# 1. BioMistral models
**BioMistral** is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC.
| Model Name | Base Model | Model Type | Sequence Length | Download |
|:-------------------:|:----------------------------------:|:-------------------:|:---------------:|:-----------------------------------------------------:|
| BioMistral-7B | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Further Pre-trained | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) |
| BioMistral-7B-DARE | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge DARE | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) |
| BioMistral-7B-TIES | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge TIES | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) |
| BioMistral-7B-SLERP | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge SLERP | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) |
# 2. Quantized Models
| Base Model | Method | q_group_size | w_bit | version | VRAM GB | Time | Download |
|:-------------------:|:------:|:------------:|:-----:|:-------:|:-------:|:------:|:--------:|
| BioMistral-7B | FP16/BF16 | | | | 15.02 | x1.00 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) |
| BioMistral-7B | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) |
| BioMistral-7B | AWQ | 128 | 4 | GEMV | 4.68 | x10.30 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) |
| BioMistral-7B | BnB.4 | | 4 | | 5.03 | x3.25 | [HuggingFace](blank) |
| BioMistral-7B | BnB.8 | | 8 | | 8.04 | x4.34 | [HuggingFace](blank) |
| BioMistral-7B-DARE | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) |
| BioMistral-7B-TIES | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) |
| BioMistral-7B-SLERP | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) |
# 2. Using BioMistral
You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow.
Loading the model and tokenizer :
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
model = AutoModel.from_pretrained("BioMistral/BioMistral-7B")
```
# 3. Supervised Fine-tuning Benchmark
| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA | MedQA 5 opts | PubMedQA | MedMCQA | Avg. |
|-------------------------------------------|:---------------------------------------------:|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|------------------|
| **BioMistral 7B** | 59.9 | 64.0 | 56.5 | 60.4 | 59.0 | 54.7 | 50.6 | 42.8 | 77.5 | 48.1 | 57.3 |
| **Mistral 7B Instruct** | **62.9** | 57.0 | 55.6 | 59.4 | 62.5 | <u>57.2</u> | 42.0 | 40.9 | 75.7 | 46.1 | 55.9 |
| | | | | | | | | | | | |
| **BioMistral 7B Ensemble** | <u>62.8</u> | 62.7 | <u>57.5</u> | **63.5** | 64.3 | 55.7 | 50.6 | 43.6 | 77.5 | **48.8** | 58.7 |
| **BioMistral 7B DARE** | 62.3 | **67.0** | 55.8 | 61.4 | **66.9** | **58.0** | **51.1** | **45.2** | <u>77.7</u> | <u>48.7</u> | **59.4** |
| **BioMistral 7B TIES** | 60.1 | <u>65.0</u> | **58.5** | 60.5 | 60.4 | 56.5 | 49.5 | 43.2 | 77.5 | 48.1 | 57.9 |
| **BioMistral 7B SLERP** | 62.5 | 64.7 | 55.8 | <u>62.7</u> | <u>64.8</u> | 56.3 | <u>50.8</u> | <u>44.3</u> | **77.8** | 48.6 | <u>58.8</u> |
| | | | | | | | | | | | |
| **MedAlpaca 7B** | 53.1 | 58.0 | 54.1 | 58.8 | 58.1 | 48.6 | 40.1 | 33.7 | 73.6 | 37.0 | 51.5 |
| **PMC-LLaMA 7B** | 24.5 | 27.7 | 35.3 | 17.4 | 30.3 | 23.3 | 25.5 | 20.2 | 72.9 | 26.6 | 30.4 |
| **MediTron-7B** | 41.6 | 50.3 | 46.4 | 27.9 | 44.4 | 30.8 | 41.6 | 28.1 | 74.9 | 41.3 | 42.7 |
| **BioMedGPT-LM-7B** | 51.4 | 52.0 | 49.4 | 53.3 | 50.7 | 49.1 | 42.5 | 33.9 | 76.8 | 37.6 | 49.7 |
| | | | | | | | | | | | |
| **GPT-3.5 Turbo 1106*** | 74.71 | 74.00 | 65.92 | 72.79 | 72.91 | 64.73 | 57.71 | 50.82 | 72.66 | 53.79 | 66.0 |
Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT.
# Citation BibTeX
Arxiv : [https://arxiv.org/abs/2402.10373](https://arxiv.org/abs/2402.10373)
```bibtex
@misc{labrak2024biomistral,
title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains},
author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour},
year={2024},
eprint={2402.10373},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**CAUTION!** Both direct and downstream users need to be informed about the risks, biases, and constraints inherent in the model. While the model can produce natural language text, our exploration of its capabilities and limitations is just beginning. In fields such as medicine, comprehending these limitations is crucial. Hence, we strongly advise against deploying this model for natural language generation in production or for professional tasks in the realm of health and medicine.
|
ElnaggarLab/ankh-base | ElnaggarLab | 2023-12-18T12:54:09Z | 4,941 | 11 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"biology",
"protein",
"protein language model",
"protein embedding",
"doi:10.57967/hf/0276",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2022-08-16T19:30:03Z | ---
license: cc-by-nc-sa-4.0
tags:
- biology
- protein
- protein language model
- protein embedding
--- |
Yntec/ChilloutMix | Yntec | 2023-11-21T00:46:09Z | 4,939 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-11-20T23:25:57Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# ChilloutMix
No ema version (ChilloutMixMini) and one with the zVAE (ChilloutMixVAE) baked in.
Sample and prompt:
girl mini cute style, sitting in gaming room, A wholesome animation key shot at computer monitor, cinematic lighting, studio ghibli, pixar and disney animation, anime key art by ROSSDRAWS and Clay Mann, style of maple story, girl, chibi, soft shade
Source:
https://huggingface.co/AsAHuman/chilloutmix |
nickmuchi/sec-bert-finetuned-finance-classification | nickmuchi | 2023-03-18T23:52:43Z | 4,938 | 13 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"onnx",
"bert",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"sentence_50agree",
"generated_from_trainer",
"sentiment",
"finance",
"en",
"dataset:financial_phrasebank",
"dataset:Kaggle_Self_label",
"dataset:nickmuchi/financial-classification",
"license:cc-by-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-08T03:30:54Z | ---
license: cc-by-sa-4.0
tags:
- financial-sentiment-analysis
- sentiment-analysis
- sentence_50agree
- generated_from_trainer
- sentiment
- finance
datasets:
- financial_phrasebank
- Kaggle_Self_label
- nickmuchi/financial-classification
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: The USD rallied by 10% last night
example_title: Bullish Sentiment
- text: >-
Covid-19 cases have been increasing over the past few months impacting
earnings for global firms
example_title: Bearish Sentiment
- text: the USD has been trending lower
example_title: Mildly Bearish Sentiment
model-index:
- name: sec-bert-finetuned-finance-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: finance
args: sentence_50agree
metrics:
- type: F1
name: F1
value: 0.8744
- type: accuracy
name: accuracy
value: 0.8755
language:
- en
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sec-bert-finetuned-finance-classification
This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.5277
- Accuracy: 0.8755
- F1: 0.8744
- Precision: 0.8754
- Recall: 0.8755
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6005 | 0.99 | 71 | 0.3702 | 0.8478 | 0.8465 | 0.8491 | 0.8478 |
| 0.3226 | 1.97 | 142 | 0.3172 | 0.8834 | 0.8822 | 0.8861 | 0.8834 |
| 0.2299 | 2.96 | 213 | 0.3313 | 0.8814 | 0.8805 | 0.8821 | 0.8814 |
| 0.1277 | 3.94 | 284 | 0.3925 | 0.8775 | 0.8771 | 0.8770 | 0.8775 |
| 0.0764 | 4.93 | 355 | 0.4517 | 0.8715 | 0.8704 | 0.8717 | 0.8715 |
| 0.0533 | 5.92 | 426 | 0.4851 | 0.8735 | 0.8728 | 0.8731 | 0.8735 |
| 0.0363 | 6.9 | 497 | 0.5107 | 0.8755 | 0.8743 | 0.8757 | 0.8755 |
| 0.0248 | 7.89 | 568 | 0.5277 | 0.8755 | 0.8744 | 0.8754 | 0.8755 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6 |
hfl/chinese-alpaca-2-1.3b | hfl | 2023-12-23T07:27:31Z | 4,938 | 9 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-10-08T08:46:07Z | ---
license: apache-2.0
language:
- zh
- en
---
# Chinese-Alpaca-2-1.3B
**This is the full Chinese-Alpaca-2-1.3B model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models (16K)
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Long context Instruction/Chat models
* [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
* [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
* [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
* [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details. |
mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF | mradermacher | 2024-06-23T11:56:42Z | 4,933 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Orenguteng/Llama-3-8B-Lexi-Uncensored",
"abacusai/Llama-3-Smaug-8B",
"theprint/llama-3-8B-Lexi-Smaug-Uncensored",
"en",
"base_model:theprint/Llama-3-8B-Lexi-Smaug-Uncensored",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T20:35:28Z | ---
base_model: theprint/Llama-3-8B-Lexi-Smaug-Uncensored
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- Orenguteng/Llama-3-8B-Lexi-Uncensored
- abacusai/Llama-3-Smaug-8B
- theprint/llama-3-8B-Lexi-Smaug-Uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/theprint/Llama-3-8B-Lexi-Smaug-Uncensored
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF/resolve/main/Llama-3-8B-Lexi-Smaug-Uncensored.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
timm/swinv2_large_window12to24_192to384.ms_in22k_ft_in1k | timm | 2024-02-10T23:31:07Z | 4,932 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-22k",
"arxiv:2111.09883",
"license:mit",
"region:us"
] | image-classification | 2023-03-18T03:35:22Z | ---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for swinv2_large_window12to24_192to384.ms_in22k_ft_in1k
A Swin Transformer V2 image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 196.7
- GMACs: 116.1
- Activations (M): 407.8
- Image size: 384 x 384
- **Papers:**
- Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
- **Original:** https://github.com/microsoft/Swin-Transformer
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-22k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('swinv2_large_window12to24_192to384.ms_in22k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g. for swin_base_patch4_window7_224 (NHWC output)
# torch.Size([1, 56, 56, 128])
# torch.Size([1, 28, 28, 256])
# torch.Size([1, 14, 14, 512])
# torch.Size([1, 7, 7, 1024])
# e.g. for swinv2_cr_small_ns_224 (NCHW output)
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2
# or (batch_size, num_features, H, W) for swinv2_cr
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{liu2021swinv2,
title={Swin Transformer V2: Scaling Up Capacity and Resolution},
author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf | RichardErkhov | 2024-06-03T05:02:43Z | 4,932 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-06-03T02:05:54Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
NorskGPT-Llama-7B-v0.1 - GGUF
- Model creator: https://huggingface.co/bineric/
- Original model: https://huggingface.co/bineric/NorskGPT-Llama-7B-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [NorskGPT-Llama-7B-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q2_K.gguf) | Q2_K | 2.36GB |
| [NorskGPT-Llama-7B-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
| [NorskGPT-Llama-7B-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.IQ3_S.gguf) | IQ3_S | 2.75GB |
| [NorskGPT-Llama-7B-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [NorskGPT-Llama-7B-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.IQ3_M.gguf) | IQ3_M | 2.9GB |
| [NorskGPT-Llama-7B-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q3_K.gguf) | Q3_K | 3.07GB |
| [NorskGPT-Llama-7B-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [NorskGPT-Llama-7B-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [NorskGPT-Llama-7B-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [NorskGPT-Llama-7B-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q4_0.gguf) | Q4_0 | 3.56GB |
| [NorskGPT-Llama-7B-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [NorskGPT-Llama-7B-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [NorskGPT-Llama-7B-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q4_K.gguf) | Q4_K | 3.8GB |
| [NorskGPT-Llama-7B-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [NorskGPT-Llama-7B-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q4_1.gguf) | Q4_1 | 3.95GB |
| [NorskGPT-Llama-7B-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q5_0.gguf) | Q5_0 | 4.33GB |
| [NorskGPT-Llama-7B-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [NorskGPT-Llama-7B-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q5_K.gguf) | Q5_K | 4.45GB |
| [NorskGPT-Llama-7B-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [NorskGPT-Llama-7B-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q5_1.gguf) | Q5_1 | 4.72GB |
| [NorskGPT-Llama-7B-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q6_K.gguf) | Q6_K | 5.15GB |
| [NorskGPT-Llama-7B-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/bineric_-_NorskGPT-Llama-7B-v0.1-gguf/blob/main/NorskGPT-Llama-7B-v0.1.Q8_0.gguf) | Q8_0 | 6.67GB |
Original model description:
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- mistral
- instruct
- finetune
language:
- no
license: cc-by-nc-sa-4.0
---
# NorskGPT-Llama-7b
This model is a Norwegian variant of Llama-2-7b-chat-hf, fine-tuned on a carefully selected mix of Norwegian instruction pairs. The model is tuned to understand and generate text in Norwegain.
## Intended Use
This model is intended for personal and research use in Norwegian and can be used as an assistant-like chat. Try it out :)
## Prompt Template
```
### Instruction:
Summarize following text.
### Input:
Text to be summarized
### Response:
```
## Limitations
* This is an LLM, not a knowledge model. It can not be expected to have more information about Norway than the base model.
* It will generally preform better on tasks that involves summarization, question answering and chat, than on tasks that requires more knowledge about Norway, specific domains, or tasks where the model can answer freely.
* The model is released as is, and would in most cases need prompt tuning to achieve optimal results.
## License
[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes .
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
|
digiplay/Noosphere_v4.2 | digiplay | 2024-04-07T01:52:04Z | 4,928 | 1 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-04-07T00:21:37Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/36538?modelVersionId=265285
|
mradermacher/IceCocoaRP-7b-i1-GGUF | mradermacher | 2024-06-08T13:15:41Z | 4,927 | 2 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"alpaca",
"mistral",
"not-for-all-audiences",
"nsfw",
"en",
"base_model:icefog72/IceCocoaRP-7b",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-08T09:30:35Z | ---
base_model: icefog72/IceCocoaRP-7b
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- alpaca
- mistral
- not-for-all-audiences
- nsfw
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/icefog72/IceCocoaRP-7b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/IceCocoaRP-7b-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/IceCocoaRP-7b-i1-GGUF/resolve/main/IceCocoaRP-7b.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
Kotokin/Midnight-Miqu-70B-v1.5_GPTQ32G | Kotokin | 2024-03-15T02:48:00Z | 4,926 | 2 | transformers | [
"transformers",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-15T02:05:42Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
license: other
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/Tn9MBg6.png" alt="MidnightMiqu" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
Original: https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5
### Overview
This is a DARE Linear merge between [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) and [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6).
This version is close in feel and performance to Midnight Miqu v1.0 but I think it picked up some goodness from Tess. Their EQ Bench scores are virtually the same and their post-EXL2 quant perplexity scores were the same too. However, Midnight Miqu v1.5 passes some tests I use that Midnight Miqu v1.0 fails, without sacrificing writing quality.
This model is uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It may also perform well at other tasks but I have not tested its performance in other areas.
### Long Context Tips
You can run this model out to 32K context with alpha_rope set to 1, just like with Miqu.
### Sampler Tips
* I recommend using Quadratic Sampling (i.e. smoothing factor) for creative work. I think this version performs best with a smoothing factor close to 0.2.
* I recommend using Min-P. Experiment to find your best setting.
* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.
* You don't need to use a high repetition penalty with this model, such as going above 1.10, but experiment with it.
Experiment with any and all of the settings below! What suits my preferences may not suit yours.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.12,
"rep_pen": 1.05,
"rep_pen_range": 2800,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"dynatemp": false,
"min_temp": 0.8,
"max_temp": 1.35,
"dynatemp_exponent": 1,
"smoothing_factor": 0.23,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 2,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"logit_bias": [],
"n": 1,
"rep_pen_size": 0,
"genamt": 500,
"max_length": 32764
}
```
### Prompting Tips
Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
```
{
"story_string": "{{#if system}}{{system}}\n{{/if}}\nCONTEXTUAL INFORMATION\n{{#if wiBefore}}\n- World and character info:\n{{wiBefore}}\n{{/if}}\n{{#if description}}\n- {{char}}'s background and persona:\n{{description}}\n{{/if}}\n{{#if mesExamples}}\n{{mesExamples}}\n{{/if}}\n{{#if personality}}\n{{personality}}\n{{/if}}\n{{#if scenario}}\n- Roleplay scenario:\n{{scenario}}\n{{/if}}\n{{#if wiAfter}}{{wiAfter}}\n{{/if}}\n{{#if persona}}{{persona}}\n{{/if}}",
"example_separator": "",
"chat_start": "---\nTaking the above information into consideration, you must engage with {{user}} and others as {{char}} in the roleplay below this line. Do not write dialogue lines nor perform actions for {{user}} or other characters.\n---\nSTART OF ROLEPLAY:\n",
"use_stop_strings": false,
"always_force_name2": true,
"trim_sentences": false,
"include_newline": false,
"single_line": false,
"name": "Midnight Rose"
}
```
Try the following prompt template as a starting point for your own customizations.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
**Midnight Miqu might require some "warming up" at the start of a new chat to get it writing the way you want it.** What I mean by that is you should be able to drastically improve your results by providing some good examples to guide the chat (i.e. few-shot prompting). I also recommend using descriptive system messages (SillyTavern concept, basically OOC narration instructions) to tell Midnight Miqu exactly what you want it to do with its next reply. Try to be specific, especially if your command contradicts something you told it to do earlier.
The example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.
The prompt below has been updated from Midnight Miqu v1.0 after prompting Midnight Miqu to help improve it.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": true,
"system_prompt": "You are an AI creative writing partner, collaborating with me in a dynamic, uncensored roleplaying environment. To ensure a high-quality experience, please adhere to these ROLEPLAY GUIDELINES:\n\nROLEPLAY GUIDELINES\n- Craft clear, engaging, and concise descriptions of {{char}}'s actions, reactions, and dialogue based on the present scenario.\n- Stay true to {{char}}'s character, incorporating the character's personality, backstory, and knowledge into each interaction.\n- Employ rich, descriptive language that immerses readers in the scene.\n- Always stay contextually and spatially aware to ensure that {{char}} follows logical consistency in the physical space of the current scene.\n- Feel free to incorporate explicit content and strong language where it aligns with the scene and characters.\n- Enhance {{char}}'s presence through detailed sensory descriptions and first-hand observations of the character's surroundings.\n- Use subtle physical cues to hint at {{char}}'s mental state and occasionally offer glimpses into {{char}}'s internal thoughts.\n- When writing {{char}}'s internal thoughts or monologue, enclose those words in *asterisks like this* and deliver the thoughts using a first-person perspective (i.e. use \"I\" pronouns). Always use quotes for spoken speech \"like this.\"\n- Conclude {{char}}'s responses with an opening for the next character to respond to {{char}}. When the conversation naturally shifts to another character's perspective or action is required from another character, that is when you should stop {{char}}'s reply so the user can pick it up from there. A great example is when {{char}} asks a question of another character.\n",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER: ",
"output_sequence": "ASSISTANT: ",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "SYSTEM: ",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT (Ensure coherence and authenticity in {{char}}'s actions, thoughts, and dialogues; Focus solely on {{char}}'s interactions within the roleplay): ",
"activation_regex": "",
"name": "Midnight Miqu Roleplay"
}
```
### Instruct Formats
I recommend the Vicuna format. I use a modified version with newlines after USER and ASSISTANT.
```
USER:
{prompt}
ASSISTANT:
```
Mistral's format also works, and in my testing the performance is about the same as using Vicuna.
```
[INST]
{prompt}
[/INST]
```
You could also try ChatML (don't recommend it)
```
<|im_start|>system
{Your system prompt goes here}<|im_end|>
<|im_start|>user
{Your message as the user will go here}<|im_end|>
<|im_start|>assistant
```
### Quantizations
* Pending
* If you don't see something you're looking for, [try searching Hugging Face](https://huggingface.co/models?search=midnight-miqu-70b-v1.5). There may be newer quants available than what I've documented here.
### Licence and usage restrictions
<font color="red">152334H/miqu-1-70b-sf was based on a leaked version of one of Mistral's models.</font>
All miqu-derived models, including this merge, are **only suitable for personal use.** Mistral has been cool about it so far, but you should be aware that by downloading this merge you are assuming whatever legal risk is iherent in acquiring and using a model based on leaked weights.
This merge comes with no warranties or guarantees of any kind, but you probably already knew that.
I am not a lawyer and I do not profess to know what we have gotten ourselves into here. You should consult with a lawyer before using any Hugging Face model beyond private use... but definitely don't use this one for that!
## Merge Details
### Merge Method
This model was merged using the linear [DARE](https://arxiv.org/abs/2311.03099) merge method using [152334H_miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) as a base.
### Models Merged
The following models were included in the merge:
* [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0)
* [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_linear
base_model: /home/llm/mergequant/models/BASE/152334H_miqu-1-70b-sf # base model
models:
- model: /home/llm/mergequant/models/midnight-miqu-70b-v1.0
- model: /home/llm/mergequant/models/BASE/Tess-70B-v1.6
parameters:
weight: 1.0
dtype: float16
```
### Notes
I tried several methods of merging Midnight Miqu v1.0 with Tess v1.6, and this dare_linear approach worked the best by far. I tried the same approach with other Miqu finetunes like ShinojiResearch/Senku-70B-Full and abideen/Liberated-Miqu-70B, but there was a huge difference in performance. The merge with Tess was the best one.
I also tried the SLERP approach I used to create Midnight Miqu v1.0, only using Tess instead of 152334H_miqu-1-70b in that config, and that result was nowhere near as good either. |
unsloth/Qwen2-1.5B | unsloth | 2024-06-06T17:18:52Z | 4,922 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-06T16:37:07Z | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- qwen2
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Qwen2 7b here: https://colab.research.google.com/drive/1mvwsIQWDs2EdZxZQF9pRGnnOvE86MVvR?usp=sharing
And a Colab notebook for [Qwen2 0.5b](https://colab.research.google.com/drive/1-7tjDdMAyeCueyLAwv6vYeBMHpoePocN?usp=sharing) and another for [Qwen2 1.5b](https://colab.research.google.com/drive/1W0j3rP8WpgxRdUgkb5l6E00EEVyjEZGk?usp=sharing)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less |
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. |
togethercomputer/Llama-3-8B-Dragonfly-v1 | togethercomputer | 2024-06-06T18:09:59Z | 4,917 | 28 | transformers | [
"transformers",
"pytorch",
"dragonfly",
"text-generation",
"text-generation-inference",
"image-text-to-text",
"en",
"arxiv:2406.00977",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-06-03T06:25:00Z | ---
license: llama3
language:
- en
pipeline_tag: image-text-to-text
tags:
- text-generation-inference
extra_gated_fields:
First Name: text
Last Name: text
Country: country
Affiliation: text
I want to use this model for:
type: select
options:
- Research
- Education
- label: Other
value: other
I agree to use this model in accordance to META LLAMA 3 COMMUNITY LICENSE AGREEMENT: checkbox
---
# Dragonfly Model Card
**Note: Users are permitted to use this model in accordance with the Llama 3 Community License Agreement.**
## Model Details
Dragonfly is a multimodal visual-language model, trained by instruction tuning on Llama 3.
- **Developed by:** [Together AI](https://www.together.ai/)
- **Model type:** An autoregressive visual-language model based on the transformer architecture
- **License:** [Llama 3 Community License Agreement](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
- **Finetuned from model:** [Llama 3](https://github.com/meta-llama/llama3)
### Model Sources
- **Repository:** https://github.com/togethercomputer/Dragonfly
- **Blog:** https://www.together.ai/blog/dragonfly-v1
- **Paper:** https://arxiv.org/abs/2406.00977
## Uses
The primary use of Dragonfly is research on large visual-language models.
It is primarily intended for researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
### 💿 Installation
Create a conda environment and install necessary packages
```bash
conda env create -f environment.yml
conda activate dragonfly_env
```
Install flash attention
```bash
pip install flash-attn --no-build-isolation
```
As a final step, please run the following command.
```bash
pip install --upgrade -e .
```
### 🧠 Inference
If you have successfully completed the installation process, then you should be able to follow the steps below.
Question: Summarize the visual content of the image.
Load necessary packages
```python
import torch
from PIL import Image
from transformers import AutoProcessor, AutoTokenizer
from dragonfly.models.modeling_dragonfly import DragonflyForCausalLM
from dragonfly.models.processing_dragonfly import DragonflyProcessor
from pipeline.train.train_utils import random_seed
```
Instantiate the tokenizer, processor, and model.
```python
device = torch.device("cuda:0")
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Llama-3-8B-Dragonfly-v1")
clip_processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
image_processor = clip_processor.image_processor
processor = DragonflyProcessor(image_processor=image_processor, tokenizer=tokenizer, image_encoding_style="llava-hd")
model = DragonflyForCausalLM.from_pretrained("togethercomputer/Llama-3-8B-Dragonfly-v1")
model = model.to(torch.bfloat16)
model = model.to(device)
```
Now, lets load the image and process them.
```python
image = Image.open("./test_images/skateboard.png")
image = image.convert("RGB")
images = [image]
# images = [None] # if you do not want to pass any images
text_prompt = "<|start_header_id|>user<|end_header_id|>\n\nSummarize the visual content of the image.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
inputs = processor(text=[text_prompt], images=images, max_length=2048, return_tensors="pt", is_generate=True)
inputs = inputs.to(device)
```
Finally, let us generate the responses from the model
```python
temperature = 0
with torch.inference_mode():
generation_output = model.generate(**inputs, max_new_tokens=1024, eos_token_id=tokenizer.encode("<|eot_id|>"), do_sample=temperature > 0, temperature=temperature, use_cache=True)
generation_text = processor.batch_decode(generation_output, skip_special_tokens=False)
```
An example response.
```plaintext
In the heart of a vibrant skatepark, a skateboarder is caught in a moment of pure exhilaration. The skateboarder, dressed in a black t-shirt adorned with a yellow graphic and black pants, is suspended in mid-air, performing an impressive trick on a concrete ramp. The skateboarder's arms are outstretched, adding balance to the daring stunt.
The skatepark itself is a concrete playground, with the skateboarder's ramp being the main focus. In the background, palm trees sway gently, adding a touch of nature to the urban setting. A few spectators can be seen in the distance, their attention riveted on the airborne skateboarder.
The image captures not just a moment, but a story of skill, courage, and the joy of skateboarding.<|eot_id|>
```
## Training Details
See more details in the "Implementation" section of our [paper](https://arxiv.org/abs/2406.00977).
## Evaluation
See more details in the "Results" section of our [paper](https://arxiv.org/abs/2406.00977).
## 🏆 Credits
We would like to acknowledge the following resources that were instrumental in the development of Dragonfly:
- [Meta Llama 3](https://huggingface.co/meta-llama/Meta-Llama-3-8B): We utilized the Llama 3 model as our foundational language model.
- [CLIP](https://huggingface.co/openai/clip-vit-base-patch32): Our vision backbone is CLIP model from OpenAI.
- Our codebase is built upon the following two codebases:
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://github.com/Luodian/Otter)
- [LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images](https://github.com/thunlp/LLaVA-UHD)
## 📚 BibTeX
```bibtex
@misc{chen2024dragonfly,
title={Dragonfly: Multi-Resolution Zoom Supercharges Large Visual-Language Model},
author={Kezhen Chen and Rahul Thapa and Rahul Chalamala and Ben Athiwaratkun and Shuaiwen Leon Song and James Zou},
year={2024},
eprint={2406.00977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Model Card Authors
Rahul Thapa, Kezhen Chen, Rahul Chalamala
## Model Card Contact
Rahul Thapa ([email protected]), Kezhen Chen ([email protected]) |
ai-forever/rugpt3medium_based_on_gpt2 | ai-forever | 2023-12-05T08:43:34Z | 4,915 | 19 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/ru-gpts"
---
# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model was pretrained with sequence length 1024 using the Transformers library by the [SberDevices](https://sberdevices.ru/) team on 80B tokens for 3 epochs. After that, the model was finetuned with the context size of 2048 tokens.
Total training time was around 16 days on 64 GPUs.
The final perplexity on the test set is `17.4`.
# Authors
+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam):
+ Dmitry Zmitrovich
# Cite us
```
@misc{zmitrovich2023family,
title={A Family of Pretrained Transformer Language Models for Russian},
author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova},
year={2023},
eprint={2309.10931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF | mradermacher | 2024-06-15T14:39:43Z | 4,914 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Nitral-AI/Hathor_Stable-v0.2-L3-8B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-06-15T07:16:27Z | ---
base_model: Nitral-AI/Hathor_Stable-v0.2-L3-8B
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Nitral-AI/Hathor_Stable-v0.2-L3-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Hathor_Stable-v0.2-L3-8B-i1-GGUF/resolve/main/Hathor_Stable-v0.2-L3-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
illuin/tiny-random-FalconForCausalLM | illuin | 2023-09-22T11:53:53Z | 4,910 | 0 | transformers | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-09-22T11:40:26Z | Entry not found |
izhx/udever-bloom-3b | izhx | 2023-11-07T06:55:52Z | 4,910 | 2 | transformers | [
"transformers",
"pytorch",
"bloom",
"feature-extraction",
"mteb",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zhs",
"zht",
"zu",
"arxiv:2310.08232",
"license:bigscience-bloom-rail-1.0",
"model-index",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | feature-extraction | 2023-10-24T14:34:34Z | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
tags:
- mteb
model-index:
- name: udever-bloom-3b
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 30.0892025910701
- type: cos_sim_spearman
value: 30.549960550731782
- type: euclidean_pearson
value: 29.68940732194022
- type: euclidean_spearman
value: 30.254869740623715
- type: manhattan_pearson
value: 29.693089299297732
- type: manhattan_spearman
value: 30.21293218369479
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 36.469490571108054
- type: cos_sim_spearman
value: 37.34843946308442
- type: euclidean_pearson
value: 39.697664194640886
- type: euclidean_spearman
value: 37.623976566242334
- type: manhattan_pearson
value: 39.8389981955552
- type: manhattan_spearman
value: 37.689111419556
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.8955223880597
- type: ap
value: 43.270679598956285
- type: f1
value: 73.10740489387823
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.981225
- type: ap
value: 83.55047186016726
- type: f1
value: 87.95185650917034
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.58
- type: f1
value: 42.011158109228425
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.688
- type: map_at_10
value: 38.855000000000004
- type: map_at_100
value: 39.859
- type: map_at_1000
value: 39.871
- type: map_at_3
value: 33.428000000000004
- type: map_at_5
value: 36.571999999999996
- type: mrr_at_1
value: 23.044
- type: mrr_at_10
value: 39.022
- type: mrr_at_100
value: 40.019
- type: mrr_at_1000
value: 40.03
- type: mrr_at_3
value: 33.642
- type: mrr_at_5
value: 36.707
- type: ndcg_at_1
value: 22.688
- type: ndcg_at_10
value: 48.33
- type: ndcg_at_100
value: 52.616
- type: ndcg_at_1000
value: 52.891999999999996
- type: ndcg_at_3
value: 37.104
- type: ndcg_at_5
value: 42.764
- type: precision_at_1
value: 22.688
- type: precision_at_10
value: 7.881
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.931999999999999
- type: precision_at_5
value: 12.304
- type: recall_at_1
value: 22.688
- type: recall_at_10
value: 78.805
- type: recall_at_100
value: 97.51100000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 47.795
- type: recall_at_5
value: 61.522
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.37384003345981
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.52143615051018
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.91826882625199
- type: mrr
value: 73.30530273051049
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.80556032491437
- type: cos_sim_spearman
value: 84.81639043031876
- type: euclidean_pearson
value: 84.20426417923026
- type: euclidean_spearman
value: 83.53503593258247
- type: manhattan_pearson
value: 84.25387997667964
- type: manhattan_spearman
value: 83.11394200032217
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 47.017986848644625
- type: cos_sim_spearman
value: 47.16708658456057
- type: euclidean_pearson
value: 47.81098065168003
- type: euclidean_spearman
value: 48.01014499886206
- type: manhattan_pearson
value: 48.013333352251244
- type: manhattan_spearman
value: 48.252964666749016
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 71.78496868475992
- type: f1
value: 71.05715215634456
- type: precision
value: 70.7532208520454
- type: recall
value: 71.78496868475992
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.34910851860005
- type: f1
value: 98.16751045564604
- type: precision
value: 98.07762858610317
- type: recall
value: 98.34910851860005
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 59.965361967440245
- type: f1
value: 58.44898687503467
- type: precision
value: 57.83301194437321
- type: recall
value: 59.965361967440245
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.63085834649816
- type: f1
value: 98.59575215025451
- type: precision
value: 98.5781990521327
- type: recall
value: 98.63085834649816
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.15584415584416
- type: f1
value: 84.1389435939967
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.52184607783334
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.976191171733653
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 36.733774048381484
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 36.451952183379056
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 68.9131612041328
- type: mrr
value: 73.47626984126985
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 69.42233467142258
- type: mrr
value: 74.22722222222221
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.943
- type: map_at_10
value: 42.796
- type: map_at_100
value: 44.141999999999996
- type: map_at_1000
value: 44.277
- type: map_at_3
value: 39.201
- type: map_at_5
value: 41.262
- type: mrr_at_1
value: 41.488
- type: mrr_at_10
value: 49.214999999999996
- type: mrr_at_100
value: 50.02799999999999
- type: mrr_at_1000
value: 50.075
- type: mrr_at_3
value: 46.733000000000004
- type: mrr_at_5
value: 48.171
- type: ndcg_at_1
value: 41.488
- type: ndcg_at_10
value: 48.619
- type: ndcg_at_100
value: 53.868
- type: ndcg_at_1000
value: 56.027
- type: ndcg_at_3
value: 43.765
- type: ndcg_at_5
value: 45.974
- type: precision_at_1
value: 41.488
- type: precision_at_10
value: 9.07
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 20.649
- type: precision_at_5
value: 14.878
- type: recall_at_1
value: 32.943
- type: recall_at_10
value: 59.217
- type: recall_at_100
value: 81.337
- type: recall_at_1000
value: 95.185
- type: recall_at_3
value: 44.377
- type: recall_at_5
value: 51.088
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.412999999999997
- type: map_at_10
value: 34.766999999999996
- type: map_at_100
value: 35.774
- type: map_at_1000
value: 35.894999999999996
- type: map_at_3
value: 31.935000000000002
- type: map_at_5
value: 33.661
- type: mrr_at_1
value: 33.248
- type: mrr_at_10
value: 40.274
- type: mrr_at_100
value: 40.92
- type: mrr_at_1000
value: 40.977000000000004
- type: mrr_at_3
value: 38.004
- type: mrr_at_5
value: 39.425
- type: ndcg_at_1
value: 33.248
- type: ndcg_at_10
value: 39.828
- type: ndcg_at_100
value: 43.863
- type: ndcg_at_1000
value: 46.228
- type: ndcg_at_3
value: 35.643
- type: ndcg_at_5
value: 37.851
- type: precision_at_1
value: 33.248
- type: precision_at_10
value: 7.4079999999999995
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 16.964000000000002
- type: precision_at_5
value: 12.267999999999999
- type: recall_at_1
value: 26.412999999999997
- type: recall_at_10
value: 48.93
- type: recall_at_100
value: 66.437
- type: recall_at_1000
value: 81.68900000000001
- type: recall_at_3
value: 36.822
- type: recall_at_5
value: 42.925000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.07
- type: map_at_10
value: 49.051
- type: map_at_100
value: 50.13999999999999
- type: map_at_1000
value: 50.2
- type: map_at_3
value: 46.01
- type: map_at_5
value: 47.711
- type: mrr_at_1
value: 42.32
- type: mrr_at_10
value: 52.32
- type: mrr_at_100
value: 53.068000000000005
- type: mrr_at_1000
value: 53.09700000000001
- type: mrr_at_3
value: 49.864000000000004
- type: mrr_at_5
value: 51.312000000000005
- type: ndcg_at_1
value: 42.32
- type: ndcg_at_10
value: 54.727000000000004
- type: ndcg_at_100
value: 59.153
- type: ndcg_at_1000
value: 60.373
- type: ndcg_at_3
value: 49.478
- type: ndcg_at_5
value: 51.998999999999995
- type: precision_at_1
value: 42.32
- type: precision_at_10
value: 8.802999999999999
- type: precision_at_100
value: 1.196
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.006
- type: precision_at_5
value: 15.072
- type: recall_at_1
value: 37.07
- type: recall_at_10
value: 68.221
- type: recall_at_100
value: 87.22999999999999
- type: recall_at_1000
value: 95.929
- type: recall_at_3
value: 54.321
- type: recall_at_5
value: 60.358000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.055
- type: map_at_10
value: 31.163999999999998
- type: map_at_100
value: 32.213
- type: map_at_1000
value: 32.303
- type: map_at_3
value: 28.610000000000003
- type: map_at_5
value: 30.091
- type: mrr_at_1
value: 24.972
- type: mrr_at_10
value: 32.981
- type: mrr_at_100
value: 33.948
- type: mrr_at_1000
value: 34.015
- type: mrr_at_3
value: 30.546
- type: mrr_at_5
value: 31.959
- type: ndcg_at_1
value: 24.972
- type: ndcg_at_10
value: 35.806
- type: ndcg_at_100
value: 40.991
- type: ndcg_at_1000
value: 43.296
- type: ndcg_at_3
value: 30.849
- type: ndcg_at_5
value: 33.334
- type: precision_at_1
value: 24.972
- type: precision_at_10
value: 5.571000000000001
- type: precision_at_100
value: 0.853
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 12.956999999999999
- type: precision_at_5
value: 9.333
- type: recall_at_1
value: 23.055
- type: recall_at_10
value: 48.301
- type: recall_at_100
value: 72.051
- type: recall_at_1000
value: 89.408
- type: recall_at_3
value: 35.315000000000005
- type: recall_at_5
value: 41.031
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.782
- type: map_at_10
value: 21.94
- type: map_at_100
value: 23.172
- type: map_at_1000
value: 23.302999999999997
- type: map_at_3
value: 19.911
- type: map_at_5
value: 20.998
- type: mrr_at_1
value: 18.407999999999998
- type: mrr_at_10
value: 25.936999999999998
- type: mrr_at_100
value: 27.035999999999998
- type: mrr_at_1000
value: 27.118
- type: mrr_at_3
value: 23.983999999999998
- type: mrr_at_5
value: 25.141000000000002
- type: ndcg_at_1
value: 18.407999999999998
- type: ndcg_at_10
value: 26.387
- type: ndcg_at_100
value: 32.606
- type: ndcg_at_1000
value: 35.744
- type: ndcg_at_3
value: 22.686999999999998
- type: ndcg_at_5
value: 24.375
- type: precision_at_1
value: 18.407999999999998
- type: precision_at_10
value: 4.801
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 10.945
- type: precision_at_5
value: 7.811
- type: recall_at_1
value: 14.782
- type: recall_at_10
value: 36.018
- type: recall_at_100
value: 63.552
- type: recall_at_1000
value: 85.857
- type: recall_at_3
value: 25.898
- type: recall_at_5
value: 30.081999999999997
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.369
- type: map_at_10
value: 37.704
- type: map_at_100
value: 39.018
- type: map_at_1000
value: 39.134
- type: map_at_3
value: 34.243
- type: map_at_5
value: 36.083
- type: mrr_at_1
value: 32.916000000000004
- type: mrr_at_10
value: 43.488
- type: mrr_at_100
value: 44.29
- type: mrr_at_1000
value: 44.336999999999996
- type: mrr_at_3
value: 40.696
- type: mrr_at_5
value: 42.289
- type: ndcg_at_1
value: 32.916000000000004
- type: ndcg_at_10
value: 44.362
- type: ndcg_at_100
value: 49.730999999999995
- type: ndcg_at_1000
value: 51.857
- type: ndcg_at_3
value: 38.683
- type: ndcg_at_5
value: 41.249
- type: precision_at_1
value: 32.916000000000004
- type: precision_at_10
value: 8.412
- type: precision_at_100
value: 1.2970000000000002
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 18.895999999999997
- type: precision_at_5
value: 13.550999999999998
- type: recall_at_1
value: 26.369
- type: recall_at_10
value: 58.464000000000006
- type: recall_at_100
value: 80.884
- type: recall_at_1000
value: 94.676
- type: recall_at_3
value: 42.485
- type: recall_at_5
value: 49.262
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.896
- type: map_at_10
value: 33.384
- type: map_at_100
value: 34.683
- type: map_at_1000
value: 34.807
- type: map_at_3
value: 30.724
- type: map_at_5
value: 32.339
- type: mrr_at_1
value: 29.909000000000002
- type: mrr_at_10
value: 38.395
- type: mrr_at_100
value: 39.339
- type: mrr_at_1000
value: 39.404
- type: mrr_at_3
value: 36.339
- type: mrr_at_5
value: 37.618
- type: ndcg_at_1
value: 29.909000000000002
- type: ndcg_at_10
value: 38.688
- type: ndcg_at_100
value: 44.399
- type: ndcg_at_1000
value: 46.942
- type: ndcg_at_3
value: 34.548
- type: ndcg_at_5
value: 36.605
- type: precision_at_1
value: 29.909000000000002
- type: precision_at_10
value: 7.066
- type: precision_at_100
value: 1.174
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 16.819
- type: precision_at_5
value: 11.872
- type: recall_at_1
value: 23.896
- type: recall_at_10
value: 49.531
- type: recall_at_100
value: 73.977
- type: recall_at_1000
value: 91.393
- type: recall_at_3
value: 37.53
- type: recall_at_5
value: 43.373
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.153166666666667
- type: map_at_10
value: 32.7705
- type: map_at_100
value: 33.93133333333334
- type: map_at_1000
value: 34.052499999999995
- type: map_at_3
value: 30.158500000000004
- type: map_at_5
value: 31.595916666666664
- type: mrr_at_1
value: 28.87725
- type: mrr_at_10
value: 36.86358333333333
- type: mrr_at_100
value: 37.74550000000001
- type: mrr_at_1000
value: 37.80916666666666
- type: mrr_at_3
value: 34.634499999999996
- type: mrr_at_5
value: 35.926750000000006
- type: ndcg_at_1
value: 28.87725
- type: ndcg_at_10
value: 37.82341666666667
- type: ndcg_at_100
value: 42.98408333333333
- type: ndcg_at_1000
value: 45.44883333333333
- type: ndcg_at_3
value: 33.41875000000001
- type: ndcg_at_5
value: 35.45158333333333
- type: precision_at_1
value: 28.87725
- type: precision_at_10
value: 6.638249999999999
- type: precision_at_100
value: 1.0863333333333334
- type: precision_at_1000
value: 0.14858333333333335
- type: precision_at_3
value: 15.481
- type: precision_at_5
value: 10.953916666666668
- type: recall_at_1
value: 24.153166666666667
- type: recall_at_10
value: 48.796499999999995
- type: recall_at_100
value: 71.53716666666666
- type: recall_at_1000
value: 88.72158333333333
- type: recall_at_3
value: 36.419583333333335
- type: recall_at_5
value: 41.735833333333325
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.523
- type: map_at_10
value: 28.915000000000003
- type: map_at_100
value: 29.808
- type: map_at_1000
value: 29.910999999999998
- type: map_at_3
value: 26.863999999999997
- type: map_at_5
value: 27.801
- type: mrr_at_1
value: 24.387
- type: mrr_at_10
value: 31.703
- type: mrr_at_100
value: 32.481
- type: mrr_at_1000
value: 32.559
- type: mrr_at_3
value: 29.805999999999997
- type: mrr_at_5
value: 30.688
- type: ndcg_at_1
value: 24.387
- type: ndcg_at_10
value: 33.272
- type: ndcg_at_100
value: 37.79
- type: ndcg_at_1000
value: 40.428
- type: ndcg_at_3
value: 29.409000000000002
- type: ndcg_at_5
value: 30.813000000000002
- type: precision_at_1
value: 24.387
- type: precision_at_10
value: 5.337
- type: precision_at_100
value: 0.8240000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 13.19
- type: precision_at_5
value: 8.926
- type: recall_at_1
value: 21.523
- type: recall_at_10
value: 44.054
- type: recall_at_100
value: 64.80900000000001
- type: recall_at_1000
value: 84.265
- type: recall_at_3
value: 33.019999999999996
- type: recall_at_5
value: 36.561
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.461
- type: map_at_10
value: 21.802
- type: map_at_100
value: 22.825
- type: map_at_1000
value: 22.95
- type: map_at_3
value: 19.79
- type: map_at_5
value: 20.828
- type: mrr_at_1
value: 18.789
- type: mrr_at_10
value: 25.373
- type: mrr_at_100
value: 26.269
- type: mrr_at_1000
value: 26.355
- type: mrr_at_3
value: 23.394000000000002
- type: mrr_at_5
value: 24.451999999999998
- type: ndcg_at_1
value: 18.789
- type: ndcg_at_10
value: 25.948
- type: ndcg_at_100
value: 30.926
- type: ndcg_at_1000
value: 33.938
- type: ndcg_at_3
value: 22.281000000000002
- type: ndcg_at_5
value: 23.818
- type: precision_at_1
value: 18.789
- type: precision_at_10
value: 4.766
- type: precision_at_100
value: 0.848
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 10.633
- type: precision_at_5
value: 7.6259999999999994
- type: recall_at_1
value: 15.461
- type: recall_at_10
value: 34.967999999999996
- type: recall_at_100
value: 57.25900000000001
- type: recall_at_1000
value: 78.738
- type: recall_at_3
value: 24.495
- type: recall_at_5
value: 28.510999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.165
- type: map_at_10
value: 32.66
- type: map_at_100
value: 33.842
- type: map_at_1000
value: 33.952
- type: map_at_3
value: 30.503999999999998
- type: map_at_5
value: 31.546000000000003
- type: mrr_at_1
value: 29.851
- type: mrr_at_10
value: 37.112
- type: mrr_at_100
value: 38.057
- type: mrr_at_1000
value: 38.119
- type: mrr_at_3
value: 35.106
- type: mrr_at_5
value: 36.22
- type: ndcg_at_1
value: 29.851
- type: ndcg_at_10
value: 37.395
- type: ndcg_at_100
value: 42.906
- type: ndcg_at_1000
value: 45.427
- type: ndcg_at_3
value: 33.465
- type: ndcg_at_5
value: 35.02
- type: precision_at_1
value: 29.851
- type: precision_at_10
value: 6.166
- type: precision_at_100
value: 1.005
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 15.235999999999999
- type: precision_at_5
value: 10.354
- type: recall_at_1
value: 25.165
- type: recall_at_10
value: 47.439
- type: recall_at_100
value: 71.56099999999999
- type: recall_at_1000
value: 89.435
- type: recall_at_3
value: 36.275
- type: recall_at_5
value: 40.435
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.589000000000002
- type: map_at_10
value: 33.729
- type: map_at_100
value: 35.306
- type: map_at_1000
value: 35.552
- type: map_at_3
value: 30.988
- type: map_at_5
value: 32.406
- type: mrr_at_1
value: 30.830000000000002
- type: mrr_at_10
value: 38.446999999999996
- type: mrr_at_100
value: 39.478
- type: mrr_at_1000
value: 39.544000000000004
- type: mrr_at_3
value: 36.034
- type: mrr_at_5
value: 37.546
- type: ndcg_at_1
value: 30.830000000000002
- type: ndcg_at_10
value: 39.22
- type: ndcg_at_100
value: 45.004
- type: ndcg_at_1000
value: 47.837
- type: ndcg_at_3
value: 34.811
- type: ndcg_at_5
value: 36.831
- type: precision_at_1
value: 30.830000000000002
- type: precision_at_10
value: 7.489999999999999
- type: precision_at_100
value: 1.534
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 16.14
- type: precision_at_5
value: 11.66
- type: recall_at_1
value: 25.589000000000002
- type: recall_at_10
value: 49.238
- type: recall_at_100
value: 74.893
- type: recall_at_1000
value: 92.902
- type: recall_at_3
value: 36.75
- type: recall_at_5
value: 42.256
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.572
- type: map_at_10
value: 25.334
- type: map_at_100
value: 26.253
- type: map_at_1000
value: 26.346000000000004
- type: map_at_3
value: 23.122
- type: map_at_5
value: 24.425
- type: mrr_at_1
value: 19.409000000000002
- type: mrr_at_10
value: 27.118
- type: mrr_at_100
value: 28.032
- type: mrr_at_1000
value: 28.110000000000003
- type: mrr_at_3
value: 25.108000000000004
- type: mrr_at_5
value: 26.3
- type: ndcg_at_1
value: 19.409000000000002
- type: ndcg_at_10
value: 29.629
- type: ndcg_at_100
value: 34.572
- type: ndcg_at_1000
value: 37.289
- type: ndcg_at_3
value: 25.406000000000002
- type: ndcg_at_5
value: 27.55
- type: precision_at_1
value: 19.409000000000002
- type: precision_at_10
value: 4.769
- type: precision_at_100
value: 0.767
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 11.337
- type: precision_at_5
value: 8.096
- type: recall_at_1
value: 17.572
- type: recall_at_10
value: 41.177
- type: recall_at_100
value: 64.456
- type: recall_at_1000
value: 85.182
- type: recall_at_3
value: 29.747
- type: recall_at_5
value: 34.948
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.264
- type: map_at_10
value: 16.09
- type: map_at_100
value: 17.717
- type: map_at_1000
value: 17.903
- type: map_at_3
value: 13.422
- type: map_at_5
value: 14.78
- type: mrr_at_1
value: 20.326
- type: mrr_at_10
value: 31.274
- type: mrr_at_100
value: 32.312999999999995
- type: mrr_at_1000
value: 32.365
- type: mrr_at_3
value: 27.959
- type: mrr_at_5
value: 29.877
- type: ndcg_at_1
value: 20.326
- type: ndcg_at_10
value: 23.358
- type: ndcg_at_100
value: 30.36
- type: ndcg_at_1000
value: 33.883
- type: ndcg_at_3
value: 18.704
- type: ndcg_at_5
value: 20.374
- type: precision_at_1
value: 20.326
- type: precision_at_10
value: 7.303
- type: precision_at_100
value: 1.488
- type: precision_at_1000
value: 0.214
- type: precision_at_3
value: 13.811000000000002
- type: precision_at_5
value: 10.84
- type: recall_at_1
value: 9.264
- type: recall_at_10
value: 29.177999999999997
- type: recall_at_100
value: 53.61900000000001
- type: recall_at_1000
value: 73.48400000000001
- type: recall_at_3
value: 17.738
- type: recall_at_5
value: 22.279
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 14.494000000000002
- type: map_at_10
value: 21.37
- type: map_at_100
value: 22.741
- type: map_at_1000
value: 22.911
- type: map_at_3
value: 18.929000000000002
- type: map_at_5
value: 20.244
- type: mrr_at_1
value: 23.105999999999998
- type: mrr_at_10
value: 29.137999999999998
- type: mrr_at_100
value: 30.064
- type: mrr_at_1000
value: 30.152
- type: mrr_at_3
value: 27.119
- type: mrr_at_5
value: 28.301
- type: ndcg_at_1
value: 23.105999999999998
- type: ndcg_at_10
value: 26.182
- type: ndcg_at_100
value: 32.396
- type: ndcg_at_1000
value: 36.177
- type: ndcg_at_3
value: 22.708000000000002
- type: ndcg_at_5
value: 24.137
- type: precision_at_1
value: 23.105999999999998
- type: precision_at_10
value: 6.0040000000000004
- type: precision_at_100
value: 1.119
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 13.028
- type: precision_at_5
value: 9.557
- type: recall_at_1
value: 14.494000000000002
- type: recall_at_10
value: 32.910000000000004
- type: recall_at_100
value: 59.202999999999996
- type: recall_at_1000
value: 85.61
- type: recall_at_3
value: 22.397
- type: recall_at_5
value: 26.900000000000002
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 74.91280817799158
- type: cos_sim_ap
value: 83.32013347926805
- type: cos_sim_f1
value: 76.57387580299788
- type: cos_sim_precision
value: 70.63006122852063
- type: cos_sim_recall
value: 83.61000701426234
- type: dot_accuracy
value: 70.5832832230908
- type: dot_ap
value: 75.9647326130666
- type: dot_f1
value: 73.65528072241852
- type: dot_precision
value: 63.47487734731856
- type: dot_recall
value: 87.72504091653029
- type: euclidean_accuracy
value: 74.51593505712569
- type: euclidean_ap
value: 83.04382773676555
- type: euclidean_f1
value: 75.7739770513098
- type: euclidean_precision
value: 70.5502922797823
- type: euclidean_recall
value: 81.83306055646482
- type: manhattan_accuracy
value: 74.73241130487071
- type: manhattan_ap
value: 83.32768114935021
- type: manhattan_f1
value: 76.09116319071167
- type: manhattan_precision
value: 70.42786069651741
- type: manhattan_recall
value: 82.74491465980827
- type: max_accuracy
value: 74.91280817799158
- type: max_ap
value: 83.32768114935021
- type: max_f1
value: 76.57387580299788
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 55.032000000000004
- type: map_at_10
value: 63.517
- type: map_at_100
value: 64.159
- type: map_at_1000
value: 64.17699999999999
- type: map_at_3
value: 61.503
- type: map_at_5
value: 62.741
- type: mrr_at_1
value: 55.111
- type: mrr_at_10
value: 63.50900000000001
- type: mrr_at_100
value: 64.13499999999999
- type: mrr_at_1000
value: 64.153
- type: mrr_at_3
value: 61.521
- type: mrr_at_5
value: 62.759
- type: ndcg_at_1
value: 55.216
- type: ndcg_at_10
value: 67.569
- type: ndcg_at_100
value: 70.71
- type: ndcg_at_1000
value: 71.211
- type: ndcg_at_3
value: 63.543000000000006
- type: ndcg_at_5
value: 65.718
- type: precision_at_1
value: 55.216
- type: precision_at_10
value: 8.093
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 23.253
- type: precision_at_5
value: 15.026
- type: recall_at_1
value: 55.032000000000004
- type: recall_at_10
value: 80.163
- type: recall_at_100
value: 94.94200000000001
- type: recall_at_1000
value: 98.946
- type: recall_at_3
value: 69.231
- type: recall_at_5
value: 74.49900000000001
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.391
- type: map_at_10
value: 16.381999999999998
- type: map_at_100
value: 21.262
- type: map_at_1000
value: 22.461000000000002
- type: map_at_3
value: 12.471
- type: map_at_5
value: 14.016
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 69.64099999999999
- type: mrr_at_100
value: 70.114
- type: mrr_at_1000
value: 70.128
- type: mrr_at_3
value: 67.958
- type: mrr_at_5
value: 68.996
- type: ndcg_at_1
value: 50.375
- type: ndcg_at_10
value: 34.542
- type: ndcg_at_100
value: 37.265
- type: ndcg_at_1000
value: 44.324000000000005
- type: ndcg_at_3
value: 40.113
- type: ndcg_at_5
value: 37.177
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 26.05
- type: precision_at_100
value: 7.632999999999999
- type: precision_at_1000
value: 1.6209999999999998
- type: precision_at_3
value: 42.5
- type: precision_at_5
value: 35.199999999999996
- type: recall_at_1
value: 8.391
- type: recall_at_10
value: 21.099
- type: recall_at_100
value: 40.886
- type: recall_at_1000
value: 63.805
- type: recall_at_3
value: 13.766
- type: recall_at_5
value: 16.128
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.933
- type: map_at_10
value: 65.739
- type: map_at_100
value: 69.245
- type: map_at_1000
value: 69.33399999999999
- type: map_at_3
value: 44.874
- type: map_at_5
value: 56.242999999999995
- type: mrr_at_1
value: 78.95
- type: mrr_at_10
value: 85.37700000000001
- type: mrr_at_100
value: 85.474
- type: mrr_at_1000
value: 85.481
- type: mrr_at_3
value: 84.63300000000001
- type: mrr_at_5
value: 85.141
- type: ndcg_at_1
value: 78.95
- type: ndcg_at_10
value: 75.81599999999999
- type: ndcg_at_100
value: 80.42399999999999
- type: ndcg_at_1000
value: 81.357
- type: ndcg_at_3
value: 73.821
- type: ndcg_at_5
value: 72.497
- type: precision_at_1
value: 78.95
- type: precision_at_10
value: 37.285000000000004
- type: precision_at_100
value: 4.589
- type: precision_at_1000
value: 0.481
- type: precision_at_3
value: 66.333
- type: precision_at_5
value: 55.879999999999995
- type: recall_at_1
value: 21.933
- type: recall_at_10
value: 77.943
- type: recall_at_100
value: 92.17
- type: recall_at_1000
value: 96.986
- type: recall_at_3
value: 48.079
- type: recall_at_5
value: 62.65500000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 38.2
- type: map_at_10
value: 46.785
- type: map_at_100
value: 47.635
- type: map_at_1000
value: 47.675
- type: map_at_3
value: 44.583
- type: map_at_5
value: 45.848
- type: mrr_at_1
value: 38.2
- type: mrr_at_10
value: 46.785
- type: mrr_at_100
value: 47.635
- type: mrr_at_1000
value: 47.675
- type: mrr_at_3
value: 44.583
- type: mrr_at_5
value: 45.848
- type: ndcg_at_1
value: 38.2
- type: ndcg_at_10
value: 51.282000000000004
- type: ndcg_at_100
value: 55.608000000000004
- type: ndcg_at_1000
value: 56.726
- type: ndcg_at_3
value: 46.763
- type: ndcg_at_5
value: 49.035000000000004
- type: precision_at_1
value: 38.2
- type: precision_at_10
value: 6.550000000000001
- type: precision_at_100
value: 0.8619999999999999
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 17.7
- type: precision_at_5
value: 11.72
- type: recall_at_1
value: 38.2
- type: recall_at_10
value: 65.5
- type: recall_at_100
value: 86.2
- type: recall_at_1000
value: 95.1
- type: recall_at_3
value: 53.1
- type: recall_at_5
value: 58.599999999999994
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.88
- type: f1
value: 43.30537129784135
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 54.423
- type: map_at_10
value: 66.136
- type: map_at_100
value: 66.557
- type: map_at_1000
value: 66.57300000000001
- type: map_at_3
value: 64.042
- type: map_at_5
value: 65.366
- type: mrr_at_1
value: 58.745999999999995
- type: mrr_at_10
value: 70.456
- type: mrr_at_100
value: 70.801
- type: mrr_at_1000
value: 70.809
- type: mrr_at_3
value: 68.504
- type: mrr_at_5
value: 69.746
- type: ndcg_at_1
value: 58.745999999999995
- type: ndcg_at_10
value: 71.96000000000001
- type: ndcg_at_100
value: 73.83
- type: ndcg_at_1000
value: 74.17
- type: ndcg_at_3
value: 68.033
- type: ndcg_at_5
value: 70.22
- type: precision_at_1
value: 58.745999999999995
- type: precision_at_10
value: 9.397
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 27.208
- type: precision_at_5
value: 17.561
- type: recall_at_1
value: 54.423
- type: recall_at_10
value: 85.703
- type: recall_at_100
value: 93.989
- type: recall_at_1000
value: 96.35000000000001
- type: recall_at_3
value: 75.05
- type: recall_at_5
value: 80.447
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.286
- type: map_at_10
value: 27.499000000000002
- type: map_at_100
value: 29.176999999999996
- type: map_at_1000
value: 29.354999999999997
- type: map_at_3
value: 23.684
- type: map_at_5
value: 25.544
- type: mrr_at_1
value: 32.87
- type: mrr_at_10
value: 41.906
- type: mrr_at_100
value: 42.739
- type: mrr_at_1000
value: 42.78
- type: mrr_at_3
value: 38.992
- type: mrr_at_5
value: 40.535
- type: ndcg_at_1
value: 32.87
- type: ndcg_at_10
value: 35.124
- type: ndcg_at_100
value: 41.638
- type: ndcg_at_1000
value: 44.869
- type: ndcg_at_3
value: 30.975
- type: ndcg_at_5
value: 32.112
- type: precision_at_1
value: 32.87
- type: precision_at_10
value: 10.062
- type: precision_at_100
value: 1.653
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 20.833
- type: precision_at_5
value: 15.340000000000002
- type: recall_at_1
value: 16.286
- type: recall_at_10
value: 42.734
- type: recall_at_100
value: 67.582
- type: recall_at_1000
value: 86.735
- type: recall_at_3
value: 28.438000000000002
- type: recall_at_5
value: 33.944
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.606
- type: map_at_10
value: 46.085
- type: map_at_100
value: 46.796
- type: map_at_1000
value: 46.866
- type: map_at_3
value: 43.614000000000004
- type: map_at_5
value: 45.094
- type: mrr_at_1
value: 67.211
- type: mrr_at_10
value: 73.447
- type: mrr_at_100
value: 73.734
- type: mrr_at_1000
value: 73.752
- type: mrr_at_3
value: 72.233
- type: mrr_at_5
value: 72.982
- type: ndcg_at_1
value: 67.211
- type: ndcg_at_10
value: 55.125
- type: ndcg_at_100
value: 57.904999999999994
- type: ndcg_at_1000
value: 59.40800000000001
- type: ndcg_at_3
value: 51.283
- type: ndcg_at_5
value: 53.32599999999999
- type: precision_at_1
value: 67.211
- type: precision_at_10
value: 11.198
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 31.631999999999998
- type: precision_at_5
value: 20.591
- type: recall_at_1
value: 33.606
- type: recall_at_10
value: 55.989
- type: recall_at_100
value: 67.01599999999999
- type: recall_at_1000
value: 77.076
- type: recall_at_3
value: 47.448
- type: recall_at_5
value: 51.479
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 45.02500961908426
- type: f1
value: 36.80024928040335
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 77.698
- type: ap
value: 72.08492726312224
- type: f1
value: 77.57721549038352
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 83.63977485928706
- type: ap
value: 48.33680179995013
- type: f1
value: 77.42875376726259
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 67.71826986847978
- type: cos_sim_spearman
value: 75.31951271324436
- type: euclidean_pearson
value: 73.99129929755692
- type: euclidean_spearman
value: 75.50510874612128
- type: manhattan_pearson
value: 74.1581557667118
- type: manhattan_spearman
value: 75.62495446886778
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 64.305
- type: map_at_10
value: 73.286
- type: map_at_100
value: 73.661
- type: map_at_1000
value: 73.675
- type: map_at_3
value: 71.433
- type: map_at_5
value: 72.596
- type: mrr_at_1
value: 66.562
- type: mrr_at_10
value: 73.932
- type: mrr_at_100
value: 74.265
- type: mrr_at_1000
value: 74.278
- type: mrr_at_3
value: 72.333
- type: mrr_at_5
value: 73.322
- type: ndcg_at_1
value: 66.562
- type: ndcg_at_10
value: 76.998
- type: ndcg_at_100
value: 78.684
- type: ndcg_at_1000
value: 79.038
- type: ndcg_at_3
value: 73.491
- type: ndcg_at_5
value: 75.436
- type: precision_at_1
value: 66.562
- type: precision_at_10
value: 9.34
- type: precision_at_100
value: 1.018
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 27.683999999999997
- type: precision_at_5
value: 17.645
- type: recall_at_1
value: 64.305
- type: recall_at_10
value: 87.825
- type: recall_at_100
value: 95.451
- type: recall_at_1000
value: 98.17
- type: recall_at_3
value: 78.522
- type: recall_at_5
value: 83.146
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.862000000000002
- type: map_at_10
value: 33.635999999999996
- type: map_at_100
value: 34.833
- type: map_at_1000
value: 34.886
- type: map_at_3
value: 29.916999999999998
- type: map_at_5
value: 32.042
- type: mrr_at_1
value: 22.493
- type: mrr_at_10
value: 34.217999999999996
- type: mrr_at_100
value: 35.365
- type: mrr_at_1000
value: 35.411
- type: mrr_at_3
value: 30.585
- type: mrr_at_5
value: 32.659
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.247
- type: ndcg_at_100
value: 46.025
- type: ndcg_at_1000
value: 47.343
- type: ndcg_at_3
value: 32.696999999999996
- type: ndcg_at_5
value: 36.476
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.334
- type: precision_at_100
value: 0.922
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.863
- type: precision_at_5
value: 10.232
- type: recall_at_1
value: 21.862000000000002
- type: recall_at_10
value: 60.56700000000001
- type: recall_at_100
value: 87.261
- type: recall_at_1000
value: 97.365
- type: recall_at_3
value: 40.081
- type: recall_at_5
value: 49.16
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.34154126766987
- type: f1
value: 92.05415284766352
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 70.63155494756043
- type: f1
value: 53.392602505424435
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.39340954942837
- type: f1
value: 68.85705470713275
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.18897108271688
- type: f1
value: 77.36699772115247
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 40.699999999999996
- type: map_at_10
value: 45.304
- type: map_at_100
value: 45.862
- type: map_at_1000
value: 45.923
- type: map_at_3
value: 44.433
- type: map_at_5
value: 44.753
- type: mrr_at_1
value: 40.8
- type: mrr_at_10
value: 45.354
- type: mrr_at_100
value: 45.912
- type: mrr_at_1000
value: 45.973000000000006
- type: mrr_at_3
value: 44.483
- type: mrr_at_5
value: 44.803
- type: ndcg_at_1
value: 40.699999999999996
- type: ndcg_at_10
value: 47.477999999999994
- type: ndcg_at_100
value: 50.51
- type: ndcg_at_1000
value: 52.367
- type: ndcg_at_3
value: 45.609
- type: ndcg_at_5
value: 46.186
- type: precision_at_1
value: 40.699999999999996
- type: precision_at_10
value: 5.43
- type: precision_at_100
value: 0.692
- type: precision_at_1000
value: 0.084
- type: precision_at_3
value: 16.333000000000002
- type: precision_at_5
value: 10.08
- type: recall_at_1
value: 40.699999999999996
- type: recall_at_10
value: 54.300000000000004
- type: recall_at_100
value: 69.19999999999999
- type: recall_at_1000
value: 84.3
- type: recall_at_3
value: 49.0
- type: recall_at_5
value: 50.4
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.70883822617504
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.801248513598072
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.97227673339198
- type: mrr
value: 32.03205560232119
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 25.89977615357687
- type: mrr
value: 24.192857142857143
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 67.16666666666666
- type: f1
value: 67.15765577091656
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.079000000000001
- type: map_at_10
value: 12.04
- type: map_at_100
value: 15.375
- type: map_at_1000
value: 16.878
- type: map_at_3
value: 8.851
- type: map_at_5
value: 10.23
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 52.886
- type: mrr_at_100
value: 53.498000000000005
- type: mrr_at_1000
value: 53.54
- type: mrr_at_3
value: 50.876999999999995
- type: mrr_at_5
value: 52.254999999999995
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 33.660000000000004
- type: ndcg_at_100
value: 31.008000000000003
- type: ndcg_at_1000
value: 40.016
- type: ndcg_at_3
value: 39.329
- type: ndcg_at_5
value: 36.687999999999995
- type: precision_at_1
value: 43.963
- type: precision_at_10
value: 25.356
- type: precision_at_100
value: 8.245
- type: precision_at_1000
value: 2.106
- type: precision_at_3
value: 37.255
- type: precision_at_5
value: 31.95
- type: recall_at_1
value: 5.079000000000001
- type: recall_at_10
value: 15.838
- type: recall_at_100
value: 32.159
- type: recall_at_1000
value: 64.91799999999999
- type: recall_at_3
value: 10.152999999999999
- type: recall_at_5
value: 12.4
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.605999999999998
- type: map_at_10
value: 43.518
- type: map_at_100
value: 44.583
- type: map_at_1000
value: 44.622
- type: map_at_3
value: 39.673
- type: map_at_5
value: 41.897
- type: mrr_at_1
value: 33.604
- type: mrr_at_10
value: 46.156000000000006
- type: mrr_at_100
value: 46.974
- type: mrr_at_1000
value: 47.002
- type: mrr_at_3
value: 42.907000000000004
- type: mrr_at_5
value: 44.792
- type: ndcg_at_1
value: 33.575
- type: ndcg_at_10
value: 50.61600000000001
- type: ndcg_at_100
value: 55.129
- type: ndcg_at_1000
value: 56.084
- type: ndcg_at_3
value: 43.297999999999995
- type: ndcg_at_5
value: 46.979
- type: precision_at_1
value: 33.575
- type: precision_at_10
value: 8.297
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 19.602
- type: precision_at_5
value: 13.934
- type: recall_at_1
value: 29.605999999999998
- type: recall_at_10
value: 69.718
- type: recall_at_100
value: 89.352
- type: recall_at_1000
value: 96.543
- type: recall_at_3
value: 50.617999999999995
- type: recall_at_5
value: 59.031
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 65.83649160801299
- type: cos_sim_ap
value: 69.86408265006916
- type: cos_sim_f1
value: 70.50709939148074
- type: cos_sim_precision
value: 57.2463768115942
- type: cos_sim_recall
value: 91.76346356916578
- type: dot_accuracy
value: 61.93827828911749
- type: dot_ap
value: 64.26140500313572
- type: dot_f1
value: 68.97081413210446
- type: dot_precision
value: 54.19432709716355
- type: dot_recall
value: 94.82576557550159
- type: euclidean_accuracy
value: 66.32376827287493
- type: euclidean_ap
value: 70.58216586017075
- type: euclidean_f1
value: 71.31782945736435
- type: euclidean_precision
value: 58.11170212765957
- type: euclidean_recall
value: 92.29144667370645
- type: manhattan_accuracy
value: 66.54033567948024
- type: manhattan_ap
value: 70.88996923294056
- type: manhattan_f1
value: 71.45256087321579
- type: manhattan_precision
value: 59.30313588850174
- type: manhattan_recall
value: 89.86272439281943
- type: max_accuracy
value: 66.54033567948024
- type: max_ap
value: 70.88996923294056
- type: max_f1
value: 71.45256087321579
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 90.41
- type: ap
value: 88.15736492425235
- type: f1
value: 90.40118324200982
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 14.718326697461064
- type: cos_sim_spearman
value: 17.458017383716168
- type: euclidean_pearson
value: 19.416710995216608
- type: euclidean_spearman
value: 17.87886266073602
- type: manhattan_pearson
value: 19.508696307778063
- type: manhattan_spearman
value: 18.026398724663487
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 31.330102731068386
- type: cos_sim_spearman
value: 33.69612492132476
- type: euclidean_pearson
value: 33.83912666711584
- type: euclidean_spearman
value: 35.58666712573462
- type: manhattan_pearson
value: 34.257595977157706
- type: manhattan_spearman
value: 36.08587604692898
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.37
- type: map_at_10
value: 84.22699999999999
- type: map_at_100
value: 84.871
- type: map_at_1000
value: 84.88900000000001
- type: map_at_3
value: 81.277
- type: map_at_5
value: 83.16799999999999
- type: mrr_at_1
value: 80.97
- type: mrr_at_10
value: 87.24300000000001
- type: mrr_at_100
value: 87.346
- type: mrr_at_1000
value: 87.347
- type: mrr_at_3
value: 86.258
- type: mrr_at_5
value: 86.914
- type: ndcg_at_1
value: 81.0
- type: ndcg_at_10
value: 88.009
- type: ndcg_at_100
value: 89.251
- type: ndcg_at_1000
value: 89.374
- type: ndcg_at_3
value: 85.169
- type: ndcg_at_5
value: 86.75399999999999
- type: precision_at_1
value: 81.0
- type: precision_at_10
value: 13.343
- type: precision_at_100
value: 1.526
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.25
- type: precision_at_5
value: 24.504
- type: recall_at_1
value: 70.37
- type: recall_at_10
value: 95.158
- type: recall_at_100
value: 99.39
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 86.942
- type: recall_at_5
value: 91.446
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 49.71370818375339
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 55.07451965473589
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.508
- type: map_at_10
value: 10.825
- type: map_at_100
value: 12.598
- type: map_at_1000
value: 12.854
- type: map_at_3
value: 7.892
- type: map_at_5
value: 9.349
- type: mrr_at_1
value: 22.2
- type: mrr_at_10
value: 32.611000000000004
- type: mrr_at_100
value: 33.61
- type: mrr_at_1000
value: 33.671
- type: mrr_at_3
value: 29.15
- type: mrr_at_5
value: 31.225
- type: ndcg_at_1
value: 22.2
- type: ndcg_at_10
value: 18.502
- type: ndcg_at_100
value: 25.424999999999997
- type: ndcg_at_1000
value: 30.233999999999998
- type: ndcg_at_3
value: 17.711
- type: ndcg_at_5
value: 15.501000000000001
- type: precision_at_1
value: 22.2
- type: precision_at_10
value: 9.49
- type: precision_at_100
value: 1.941
- type: precision_at_1000
value: 0.31
- type: precision_at_3
value: 16.433
- type: precision_at_5
value: 13.54
- type: recall_at_1
value: 4.508
- type: recall_at_10
value: 19.243
- type: recall_at_100
value: 39.407
- type: recall_at_1000
value: 62.953
- type: recall_at_3
value: 9.993
- type: recall_at_5
value: 13.733
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.88096352325879
- type: cos_sim_spearman
value: 80.84882728439892
- type: euclidean_pearson
value: 82.89512161923362
- type: euclidean_spearman
value: 80.69723454935396
- type: manhattan_pearson
value: 82.94365287299226
- type: manhattan_spearman
value: 80.64700541831023
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.09030569824817
- type: cos_sim_spearman
value: 76.10288448289813
- type: euclidean_pearson
value: 82.19317617787483
- type: euclidean_spearman
value: 78.51206398528993
- type: manhattan_pearson
value: 82.50688072451729
- type: manhattan_spearman
value: 78.71694597298867
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.04298066236511
- type: cos_sim_spearman
value: 85.49051395372348
- type: euclidean_pearson
value: 85.7369561800059
- type: euclidean_spearman
value: 86.35626949911497
- type: manhattan_pearson
value: 85.86766305481635
- type: manhattan_spearman
value: 86.5115276036124
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.98107748125086
- type: cos_sim_spearman
value: 80.43502071880916
- type: euclidean_pearson
value: 82.24603130661005
- type: euclidean_spearman
value: 80.94302742946145
- type: manhattan_pearson
value: 82.4215619893203
- type: manhattan_spearman
value: 81.13824893869541
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.95857345426359
- type: cos_sim_spearman
value: 87.7540379885978
- type: euclidean_pearson
value: 87.86433964223119
- type: euclidean_spearman
value: 88.43585275816753
- type: manhattan_pearson
value: 87.90915813062988
- type: manhattan_spearman
value: 88.49038031429657
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.84530028548023
- type: cos_sim_spearman
value: 85.42197371225963
- type: euclidean_pearson
value: 84.12042159341938
- type: euclidean_spearman
value: 84.69864997658445
- type: manhattan_pearson
value: 84.09772815909784
- type: manhattan_spearman
value: 84.63986468736967
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.89281017946413
- type: cos_sim_spearman
value: 89.94783195991867
- type: euclidean_pearson
value: 89.19342633226815
- type: euclidean_spearman
value: 88.6692137120815
- type: manhattan_pearson
value: 89.19006596701496
- type: manhattan_spearman
value: 88.65041672073397
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.05176237336566
- type: cos_sim_spearman
value: 65.12758602746149
- type: euclidean_pearson
value: 67.44468889455905
- type: euclidean_spearman
value: 67.42836832904808
- type: manhattan_pearson
value: 67.99438187200471
- type: manhattan_spearman
value: 67.96190936270705
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 81.36171514729287
- type: cos_sim_spearman
value: 81.51752389848613
- type: euclidean_pearson
value: 81.14136234145765
- type: euclidean_spearman
value: 81.27609983297867
- type: manhattan_pearson
value: 81.44966268348165
- type: manhattan_spearman
value: 81.53484018091312
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.92195724268996
- type: cos_sim_spearman
value: 87.70682082313391
- type: euclidean_pearson
value: 86.24220109166684
- type: euclidean_spearman
value: 86.51998671092596
- type: manhattan_pearson
value: 86.17577571663554
- type: manhattan_spearman
value: 86.45961101071687
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.62106635785725
- type: mrr
value: 93.84658279266121
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 53.761
- type: map_at_10
value: 64.56
- type: map_at_100
value: 65.243
- type: map_at_1000
value: 65.269
- type: map_at_3
value: 62.156
- type: map_at_5
value: 63.55
- type: mrr_at_1
value: 56.667
- type: mrr_at_10
value: 66.084
- type: mrr_at_100
value: 66.58500000000001
- type: mrr_at_1000
value: 66.61
- type: mrr_at_3
value: 64.333
- type: mrr_at_5
value: 65.3
- type: ndcg_at_1
value: 56.667
- type: ndcg_at_10
value: 69.43
- type: ndcg_at_100
value: 72.031
- type: ndcg_at_1000
value: 72.75
- type: ndcg_at_3
value: 65.282
- type: ndcg_at_5
value: 67.24900000000001
- type: precision_at_1
value: 56.667
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 16.866999999999997
- type: recall_at_1
value: 53.761
- type: recall_at_10
value: 82.678
- type: recall_at_100
value: 93.667
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.578
- type: recall_at_5
value: 76.25
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.80594059405941
- type: cos_sim_ap
value: 95.35711574476811
- type: cos_sim_f1
value: 90.12096774193547
- type: cos_sim_precision
value: 90.85365853658537
- type: cos_sim_recall
value: 89.4
- type: dot_accuracy
value: 99.76732673267327
- type: dot_ap
value: 93.20624501431367
- type: dot_f1
value: 87.74126238914971
- type: dot_precision
value: 91.71210468920393
- type: dot_recall
value: 84.1
- type: euclidean_accuracy
value: 99.80594059405941
- type: euclidean_ap
value: 95.35758863966429
- type: euclidean_f1
value: 90.15075376884421
- type: euclidean_precision
value: 90.6060606060606
- type: euclidean_recall
value: 89.7
- type: manhattan_accuracy
value: 99.80990099009901
- type: manhattan_ap
value: 95.48335466728275
- type: manhattan_f1
value: 90.2672718103883
- type: manhattan_precision
value: 91.04781281790437
- type: manhattan_recall
value: 89.5
- type: max_accuracy
value: 99.80990099009901
- type: max_ap
value: 95.48335466728275
- type: max_f1
value: 90.2672718103883
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.422562431402845
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.695493629721373
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.070077950465965
- type: mrr
value: 50.72293311263899
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.59608436984981
- type: cos_sim_spearman
value: 30.617289383193103
- type: dot_pearson
value: 30.78715584903813
- type: dot_spearman
value: 31.269245492805283
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.49332760690612
- type: mrr
value: 76.52668294806075
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.607
- type: map_at_10
value: 67.009
- type: map_at_100
value: 70.838
- type: map_at_1000
value: 70.954
- type: map_at_3
value: 47.573
- type: map_at_5
value: 58.10999999999999
- type: mrr_at_1
value: 84.333
- type: mrr_at_10
value: 87.822
- type: mrr_at_100
value: 87.969
- type: mrr_at_1000
value: 87.97500000000001
- type: mrr_at_3
value: 87.16000000000001
- type: mrr_at_5
value: 87.587
- type: ndcg_at_1
value: 84.333
- type: ndcg_at_10
value: 76.303
- type: ndcg_at_100
value: 81.05499999999999
- type: ndcg_at_1000
value: 82.218
- type: ndcg_at_3
value: 78.691
- type: ndcg_at_5
value: 76.66
- type: precision_at_1
value: 84.333
- type: precision_at_10
value: 38.019999999999996
- type: precision_at_100
value: 4.7669999999999995
- type: precision_at_1000
value: 0.505
- type: precision_at_3
value: 68.939
- type: precision_at_5
value: 57.306999999999995
- type: recall_at_1
value: 24.607
- type: recall_at_10
value: 74.971
- type: recall_at_100
value: 90.108
- type: recall_at_1000
value: 95.917
- type: recall_at_3
value: 49.586000000000006
- type: recall_at_5
value: 62.232
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 47.702
- type: f1
value: 46.274469606672426
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.252
- type: map_at_10
value: 2.178
- type: map_at_100
value: 12.781999999999998
- type: map_at_1000
value: 29.494999999999997
- type: map_at_3
value: 0.73
- type: map_at_5
value: 1.169
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 97.0
- type: mrr_at_100
value: 97.0
- type: mrr_at_1000
value: 97.0
- type: mrr_at_3
value: 97.0
- type: mrr_at_5
value: 97.0
- type: ndcg_at_1
value: 88.0
- type: ndcg_at_10
value: 83.21
- type: ndcg_at_100
value: 63.31
- type: ndcg_at_1000
value: 54.734
- type: ndcg_at_3
value: 87.408
- type: ndcg_at_5
value: 86.20100000000001
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 88.2
- type: precision_at_100
value: 64.68
- type: precision_at_1000
value: 23.966
- type: precision_at_3
value: 93.333
- type: precision_at_5
value: 91.60000000000001
- type: recall_at_1
value: 0.252
- type: recall_at_10
value: 2.307
- type: recall_at_100
value: 15.703
- type: recall_at_1000
value: 51.111
- type: recall_at_3
value: 0.749
- type: recall_at_5
value: 1.212
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 16.8
- type: f1
value: 13.168299935527422
- type: precision
value: 12.209559281760876
- type: recall
value: 16.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 35.83815028901734
- type: f1
value: 29.0852500101055
- type: precision
value: 26.965317919075147
- type: recall
value: 35.83815028901734
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 15.121951219512194
- type: f1
value: 11.844149203614325
- type: precision
value: 11.042929292929294
- type: recall
value: 15.121951219512194
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.9
- type: f1
value: 7.1396348187007215
- type: precision
value: 6.501835713997978
- type: recall
value: 9.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.6
- type: f1
value: 72.73241758241758
- type: precision
value: 71.18867647058823
- type: recall
value: 76.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.0
- type: f1
value: 36.81003102453103
- type: precision
value: 35.19870269535562
- type: recall
value: 42.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 35.3
- type: f1
value: 30.353777056277053
- type: precision
value: 28.773956778515604
- type: recall
value: 35.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 35.82089552238806
- type: f1
value: 27.44136460554371
- type: precision
value: 24.340796019900495
- type: recall
value: 35.82089552238806
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 51.800000000000004
- type: f1
value: 45.82491836793846
- type: precision
value: 43.729303094622864
- type: recall
value: 51.800000000000004
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 25.853658536585368
- type: f1
value: 19.79869362796192
- type: precision
value: 18.250680214094846
- type: recall
value: 25.853658536585368
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.0
- type: f1
value: 6.926590762281661
- type: precision
value: 6.507185696775364
- type: recall
value: 9.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.33778857837181
- type: f1
value: 10.888963524130242
- type: precision
value: 10.189272116928368
- type: recall
value: 14.33778857837181
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.304347826086957
- type: f1
value: 8.459121175343064
- type: precision
value: 7.7218644669759975
- type: recall
value: 11.304347826086957
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.521739130434783
- type: f1
value: 6.751744703151353
- type: precision
value: 6.387004921960017
- type: recall
value: 8.521739130434783
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.3
- type: f1
value: 5.626766011766011
- type: precision
value: 5.1270385799923
- type: recall
value: 7.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 3.2
- type: f1
value: 1.91950282507703
- type: precision
value: 1.6684431360304504
- type: recall
value: 3.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.790108564535585
- type: f1
value: 4.128499324411468
- type: precision
value: 3.8151453928788914
- type: recall
value: 5.790108564535585
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.3
- type: f1
value: 65.18318181818181
- type: precision
value: 63.126911976911984
- type: recall
value: 70.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.300000000000004
- type: f1
value: 38.339152873270514
- type: precision
value: 36.130903304212126
- type: recall
value: 45.300000000000004
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 16.0
- type: f1
value: 12.172850459161385
- type: precision
value: 11.27855570316309
- type: recall
value: 16.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.714285714285715
- type: f1
value: 32.188793178089945
- type: precision
value: 30.457500778089013
- type: recall
value: 37.714285714285715
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.5
- type: f1
value: 4.528544131928126
- type: precision
value: 4.171387799947767
- type: recall
value: 6.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.0
- type: f1
value: 17.006564035803166
- type: precision
value: 15.844832112332114
- type: recall
value: 21.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 25.5
- type: f1
value: 22.79430820164996
- type: precision
value: 21.938476924594045
- type: recall
value: 25.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 33.7
- type: f1
value: 26.898922166422164
- type: precision
value: 24.939117884031678
- type: recall
value: 33.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.0
- type: f1
value: 63.68992285492286
- type: precision
value: 61.72837301587302
- type: recall
value: 69.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.3999999999999995
- type: f1
value: 5.5655686223658565
- type: precision
value: 5.119921502146487
- type: recall
value: 7.3999999999999995
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1.5
- type: f1
value: 1.001208686507139
- type: precision
value: 0.9683730903243098
- type: recall
value: 1.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.0
- type: f1
value: 62.61056277056276
- type: precision
value: 59.96357142857143
- type: recall
value: 69.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.3
- type: f1
value: 97.76666666666668
- type: precision
value: 97.51666666666668
- type: recall
value: 98.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 2.0215633423180592
- type: f1
value: 1.5634923413129036
- type: precision
value: 1.4895885785373653
- type: recall
value: 2.0215633423180592
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.33333333333334
- type: f1
value: 79.3019943019943
- type: precision
value: 77.45726495726495
- type: recall
value: 83.33333333333334
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.400000000000002
- type: f1
value: 18.655079988631996
- type: precision
value: 17.338269096494905
- type: recall
value: 23.400000000000002
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.363636363636363
- type: f1
value: 4.48376251469035
- type: precision
value: 4.071778641679957
- type: recall
value: 6.363636363636363
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.56813417190776
- type: f1
value: 73.16561844863732
- type: precision
value: 71.3440484509667
- type: recall
value: 77.56813417190776
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.299999999999997
- type: f1
value: 13.693204564375854
- type: precision
value: 12.830651358081276
- type: recall
value: 17.299999999999997
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 50.58736335000926
- type: recall
value: 59.92217898832685
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 25.64102564102564
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value: 17.413105413105416
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value: 25.64102564102564
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 29.7
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value: 29.7
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 32.2
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value: 25.423187804627435
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- type: recall
value: 32.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.11214953271028
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value: 5.296401380795872
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value: 9.11214953271028
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 7.199999999999999
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value: 5.508698718788661
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value: 7.199999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.2
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value: 83.88333333333333
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value: 82.42833333333333
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value: 87.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 53.7
- type: f1
value: 48.25312435500516
- type: precision
value: 46.34107401656314
- type: recall
value: 53.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.1
- type: f1
value: 85.21690476190476
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value: 83.96761904761905
- type: recall
value: 88.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 78.10000000000001
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value: 71.47583333333334
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value: 78.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.1
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value: 96.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.0
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value: 6.457724621713984
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value: 9.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 84.7
- type: f1
value: 80.97880952380953
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value: 79.36428571428571
- type: recall
value: 84.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.5
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value: 8.146458694813958
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value: 7.618942433110826
- type: recall
value: 10.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.4
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value: 6.144921607886653
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value: 5.5261043562899586
- type: recall
value: 8.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.39999999999999
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value: 80.65333333333334
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value: 78.97833333333332
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value: 84.39999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 28.57142857142857
- type: f1
value: 22.767379679144387
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value: 21.2016369047619
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value: 28.57142857142857
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 34.24807903402854
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value: 29.241572730305222
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value: 27.6428310072657
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value: 34.24807903402854
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 2.9000000000000004
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- type: recall
value: 2.9000000000000004
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
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value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.0
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value: 95.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 6.3
- type: f1
value: 4.920070356472795
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value: 4.565811270125224
- type: recall
value: 6.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 47.4
- type: f1
value: 41.08392857142857
- type: precision
value: 38.999704968944094
- type: recall
value: 47.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pol-eng)
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 18.2
- type: f1
value: 14.826165036734295
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value: 13.988559330454489
- type: recall
value: 18.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (war-eng)
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 13.3
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value: 10.73451225789461
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value: 10.06524508030025
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value: 13.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (aze-eng)
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.3
- type: f1
value: 7.613044370901514
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value: 7.184100384035204
- type: recall
value: 9.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (vie-eng)
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.0
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value: 96.05
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value: 95.58333333333334
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value: 97.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nno-eng)
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 19.8
- type: f1
value: 16.070523504273503
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value: 14.848185626325227
- type: recall
value: 19.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cha-eng)
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 20.792945550165477
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value: 29.1970802919708
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mhr-eng)
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 4.3
- type: f1
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value: 2.6280916815877506
- type: recall
value: 4.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dan-eng)
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 28.7
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value: 24.9056519214062
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value: 23.800155414494334
- type: recall
value: 28.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.5
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value: 9.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 1.7857142857142856
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value: 1.7857142857142856
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pam-eng)
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.1
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- type: recall
value: 9.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hsb-eng)
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 15.113871635610765
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value: 12.353104530336957
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value: 11.66106754766342
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value: 15.113871635610765
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 18.4
- type: f1
value: 15.091645001025805
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- type: recall
value: 18.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 33.2
- type: f1
value: 28.066634199134192
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value: 26.54372717117398
- type: recall
value: 33.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 7.6
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value: 5.992580343865051
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value: 5.7409125738839055
- type: recall
value: 7.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 52.81385281385281
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value: 46.86834810211434
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value: 45.13687899402185
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value: 52.81385281385281
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 16.030534351145036
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value: 12.902313597194603
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value: 12.19757977391565
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value: 16.030534351145036
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.75982532751091
- type: f1
value: 93.11984473556527
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value: 92.3216885007278
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value: 94.75982532751091
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 70.19999999999999
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value: 64.41237595737596
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value: 62.074285714285715
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value: 70.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 19.2090395480226
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value: 14.986259497894084
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value: 14.08083152750014
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value: 19.2090395480226
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.800000000000001
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value: 4.004811414639001
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value: 3.611296721493974
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value: 5.800000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
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value: 91.17333333333335
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value: 90.27833333333334
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value: 93.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.2
- type: f1
value: 63.805870279146134
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value: 62.064924029458915
- type: recall
value: 68.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.9
- type: f1
value: 86.38250000000001
- type: precision
value: 85.345
- type: recall
value: 88.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 26.3
- type: f1
value: 21.72601907540825
- type: precision
value: 20.3161132602622
- type: recall
value: 26.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.6000000000000005
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value: 5.4107919446503585
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value: 5.143205186348676
- type: recall
value: 6.6000000000000005
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1.2064343163538873
- type: f1
value: 0.7118331023204635
- type: precision
value: 0.6930197065411955
- type: recall
value: 1.2064343163538873
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jpn-eng)
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.0
- type: f1
value: 73.95134920634919
- type: precision
value: 72.3770634920635
- type: recall
value: 78.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (csb-eng)
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 12.648221343873518
- type: f1
value: 10.259994816302727
- type: precision
value: 9.677206851119895
- type: recall
value: 12.648221343873518
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.56338028169014
- type: f1
value: 7.792644757433489
- type: precision
value: 7.299087316692951
- type: recall
value: 10.56338028169014
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.1437125748503
- type: f1
value: 5.6113303405098724
- type: precision
value: 5.156075980223929
- type: recall
value: 8.1437125748503
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ind-eng)
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.5
- type: f1
value: 90.53999999999999
- type: precision
value: 89.64500000000001
- type: recall
value: 92.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tuk-eng)
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.374384236453201
- type: f1
value: 5.831645092728836
- type: precision
value: 5.241568776051535
- type: recall
value: 8.374384236453201
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.42253521126761
- type: f1
value: 40.878561970111264
- type: precision
value: 39.52681669728516
- type: recall
value: 45.42253521126761
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swh-eng)
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 32.05128205128205
- type: f1
value: 25.433010420698523
- type: precision
value: 23.545685308843208
- type: recall
value: 32.05128205128205
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 92.86666666666666
- type: precision
value: 92.01666666666667
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.822546972860126
- type: f1
value: 12.439321820122155
- type: precision
value: 11.940341857811413
- type: recall
value: 14.822546972860126
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.7
- type: f1
value: 5.534443298607457
- type: precision
value: 5.299107273391812
- type: recall
value: 6.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.94788273615634
- type: f1
value: 84.65798045602605
- type: precision
value: 83.2084690553746
- type: recall
value: 87.94788273615634
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 13.8
- type: f1
value: 11.356912127897372
- type: precision
value: 10.778191051205624
- type: recall
value: 13.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 13.700000000000001
- type: f1
value: 10.74774895608627
- type: precision
value: 9.966243757837463
- type: recall
value: 13.700000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.37795275590551
- type: f1
value: 71.24671916010499
- type: precision
value: 69.20697412823397
- type: recall
value: 76.37795275590551
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 18.099999999999998
- type: f1
value: 13.934122253809159
- type: precision
value: 12.815974391105971
- type: recall
value: 18.099999999999998
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.6925207756232686
- type: f1
value: 0.08966600365830146
- type: precision
value: 0.05066184676394412
- type: recall
value: 0.6925207756232686
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.1
- type: f1
value: 8.28646043238052
- type: precision
value: 7.686198801198802
- type: recall
value: 11.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.46153846153847
- type: f1
value: 31.640899949723472
- type: precision
value: 29.298878205128204
- type: recall
value: 38.46153846153847
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.2
- type: f1
value: 76.77103174603175
- type: precision
value: 74.96511904761905
- type: recall
value: 81.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.60000000000001
- type: f1
value: 88.20666666666665
- type: precision
value: 87.14833333333334
- type: recall
value: 90.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 35.699999999999996
- type: f1
value: 29.159127620745267
- type: precision
value: 27.109529030910608
- type: recall
value: 35.699999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.9433962264150944
- type: f1
value: 0.28088681664921333
- type: precision
value: 0.22694150916099465
- type: recall
value: 0.9433962264150944
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.5
- type: f1
value: 5.825362182391272
- type: precision
value: 5.526187577939453
- type: recall
value: 7.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 4.197080291970803
- type: f1
value: 3.079215618580677
- type: precision
value: 2.8501768792419
- type: recall
value: 4.197080291970803
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.9
- type: f1
value: 84.60499999999999
- type: precision
value: 83.11428571428571
- type: recall
value: 87.9
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 50.23655676494653
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 49.54033078256682
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.299
- type: map_at_10
value: 9.232999999999999
- type: map_at_100
value: 15.156
- type: map_at_1000
value: 16.63
- type: map_at_3
value: 4.2250000000000005
- type: map_at_5
value: 6.078
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 45.158
- type: mrr_at_100
value: 45.9
- type: mrr_at_1000
value: 45.910000000000004
- type: mrr_at_3
value: 39.456
- type: mrr_at_5
value: 42.925000000000004
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 25.166
- type: ndcg_at_100
value: 35.35
- type: ndcg_at_1000
value: 46.67
- type: ndcg_at_3
value: 24.545
- type: ndcg_at_5
value: 25.112000000000002
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 23.673
- type: precision_at_100
value: 7.428999999999999
- type: precision_at_1000
value: 1.482
- type: precision_at_3
value: 23.810000000000002
- type: precision_at_5
value: 25.306
- type: recall_at_1
value: 2.299
- type: recall_at_10
value: 16.801
- type: recall_at_100
value: 45.506
- type: recall_at_1000
value: 79.985
- type: recall_at_3
value: 5.069
- type: recall_at_5
value: 8.863999999999999
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.1314
- type: ap
value: 14.605968497007712
- type: f1
value: 55.37284214772282
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.044142614601014
- type: f1
value: 61.30028928459138
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 41.28707371610032
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.09864695714371
- type: cos_sim_ap
value: 70.63738634684302
- type: cos_sim_f1
value: 66.12903225806453
- type: cos_sim_precision
value: 64.22178020885131
- type: cos_sim_recall
value: 68.15303430079156
- type: dot_accuracy
value: 83.59063002920665
- type: dot_ap
value: 66.68356189934075
- type: dot_f1
value: 63.27201851626264
- type: dot_precision
value: 58.76895225164064
- type: dot_recall
value: 68.52242744063325
- type: euclidean_accuracy
value: 85.027120462538
- type: euclidean_ap
value: 69.99328290454234
- type: euclidean_f1
value: 65.23797657612758
- type: euclidean_precision
value: 61.803588290840416
- type: euclidean_recall
value: 69.07651715039577
- type: manhattan_accuracy
value: 85.02115992132086
- type: manhattan_ap
value: 69.91284274429754
- type: manhattan_f1
value: 65.19297407097623
- type: manhattan_precision
value: 59.5763267088884
- type: manhattan_recall
value: 71.97889182058047
- type: max_accuracy
value: 85.09864695714371
- type: max_ap
value: 70.63738634684302
- type: max_f1
value: 66.12903225806453
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.119804400978
- type: cos_sim_ap
value: 86.1777422918812
- type: cos_sim_f1
value: 78.57841293719444
- type: cos_sim_precision
value: 76.80488163505366
- type: cos_sim_recall
value: 80.4357868801971
- type: dot_accuracy
value: 88.86366282454303
- type: dot_ap
value: 84.1891332504211
- type: dot_f1
value: 78.31691507672025
- type: dot_precision
value: 74.67700258397933
- type: dot_recall
value: 82.32984293193716
- type: euclidean_accuracy
value: 88.74141343578997
- type: euclidean_ap
value: 85.60421594792011
- type: euclidean_f1
value: 77.79556879538262
- type: euclidean_precision
value: 75.32991995384727
- type: euclidean_recall
value: 80.42808746535263
- type: manhattan_accuracy
value: 88.7782822990647
- type: manhattan_ap
value: 85.61374819166252
- type: manhattan_f1
value: 77.78237795927583
- type: manhattan_precision
value: 76.08423532876813
- type: manhattan_recall
value: 79.55805358792732
- type: max_accuracy
value: 89.119804400978
- type: max_ap
value: 86.1777422918812
- type: max_f1
value: 78.57841293719444
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 41.8
- type: map_at_10
value: 51.456999999999994
- type: map_at_100
value: 52.107000000000006
- type: map_at_1000
value: 52.141999999999996
- type: map_at_3
value: 48.717
- type: map_at_5
value: 50.452
- type: mrr_at_1
value: 41.8
- type: mrr_at_10
value: 51.441
- type: mrr_at_100
value: 52.091
- type: mrr_at_1000
value: 52.125
- type: mrr_at_3
value: 48.699999999999996
- type: mrr_at_5
value: 50.434999999999995
- type: ndcg_at_1
value: 41.8
- type: ndcg_at_10
value: 56.537000000000006
- type: ndcg_at_100
value: 59.901
- type: ndcg_at_1000
value: 60.889
- type: ndcg_at_3
value: 51.019999999999996
- type: ndcg_at_5
value: 54.106
- type: precision_at_1
value: 41.8
- type: precision_at_10
value: 7.26
- type: precision_at_100
value: 0.8880000000000001
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 19.233
- type: precision_at_5
value: 13.020000000000001
- type: recall_at_1
value: 41.8
- type: recall_at_10
value: 72.6
- type: recall_at_100
value: 88.8
- type: recall_at_1000
value: 96.7
- type: recall_at_3
value: 57.699999999999996
- type: recall_at_5
value: 65.10000000000001
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 84.07
- type: ap
value: 65.23766736490957
- type: f1
value: 82.17794239849368
---
# Model Card for udever-bloom
<!-- Provide a quick summary of what the model is/does. -->
`udever-bloom-3b` is finetuned from [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b) via [BitFit](https://aclanthology.org/2022.acl-short.1/) on MS MARCO Passage Ranking, SNLI and MultiNLI data.
It is a universal embedding model across tasks, natural and programming languages.
(From the technical view, `udever` is merely with some minor improvements to `sgpt-bloom`)
<div align=center><img width="338" height="259" src="https://user-images.githubusercontent.com/26690193/277643721-cdb7f227-cae5-40e1-b6e1-a201bde00339.png" /></div>
## Model Details
### Model Description
- **Developed by:** Alibaba Group
- **Model type:** Transformer-based Language Model (decoder-only)
- **Language(s) (NLP):** Multiple; see [bloom training data](https://huggingface.co/bigscience/bloom-3b#training-data)
- **Finetuned from model :** [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [github.com/izhx/uni-rep](https://github.com/izhx/uni-rep)
- **Paper :** [Language Models are Universal Embedders](https://arxiv.org/pdf/2310.08232.pdf)
- **Training Date :** 2023-06
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import torch
from transformers import AutoTokenizer, BloomModel
tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-3b')
model = BloomModel.from_pretrained('izhx/udever-bloom-3b')
boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]'
eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod])
if tokenizer.padding_side != 'left':
print('!!!', tokenizer.padding_side)
tokenizer.padding_side = 'left'
def encode(texts: list, is_query: bool = True, max_length=300):
bos = boq if is_query else bod
eos_id = eoq_id if is_query else eod_id
texts = [bos + t for t in texts]
encoding = tokenizer(
texts, truncation=True, max_length=max_length - 1, padding=True
)
for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']):
ids.append(eos_id)
mask.append(1)
inputs = tokenizer.pad(encoding, return_tensors='pt')
with torch.inference_mode():
outputs = model(**inputs)
embeds = outputs.last_hidden_state[:, -1]
return embeds
encode(['I am Bert', 'You are Elmo'])
```
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
- MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86)
- SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz)
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing
MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86).
Negatives for SNLI and MultiNLI are randomly sampled.
#### Training Hyperparameters
- **Training regime:** tf32, BitFit
- **Batch size:** 1024
- **Epochs:** 3
- **Optimizer:** AdamW
- **Learning rate:** 1e-4
- **Scheduler:** constant with warmup.
- **Warmup:** 0.25 epoch
## Evaluation
### Table 1: Massive Text Embedding Benchmark [MTEB](https://huggingface.co/spaces/mteb/leaderboard)
| MTEB | Avg. | Class. | Clust. | PairClass. | Rerank. | Retr. | STS | Summ. |
|-----------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------|
| #Datasets ➡️ | 56 | 12 | 11 | 3 | 4 | 15 | 10 | 1 |
||
| bge-large-en-v1.5 | **64.23** | **75.97** | 46.08| **87.12** | **60.03** | **54.29** | 83.11| 31.61 |
| bge-base-en-v1.5 | 63.55| 75.53| 45.77| 86.55| 58.86| 53.25| 82.4| 31.07 |
| gte-large | 63.13| 73.33| **46.84** | 85| 59.13| 52.22| **83.35** | 31.66 |
| gte-base | 62.39| 73.01| 46.2| 84.57| 58.61| 51.14| 82.3| 31.17 |
| e5-large-v2 | 62.25| 75.24| 44.49| 86.03| 56.61| 50.56| 82.05| 30.19 |
| instructor-xl | 61.79| 73.12| 44.74| 86.62| 57.29| 49.26| 83.06| 32.32 |
| instructor-large | 61.59| 73.86| 45.29| 85.89| 57.54| 47.57| 83.15| 31.84 |
| e5-base-v2 | 61.5 | 73.84| 43.8| 85.73| 55.91| 50.29| 81.05| 30.28 |
| e5-large | 61.42| 73.14| 43.33| 85.94| 56.53| 49.99| 82.06| 30.97 |
| text-embedding-ada-002 (OpenAI API) | 60.99| 70.93| 45.9 | 84.89| 56.32| 49.25| 80.97| 30.8 |
| e5-base | 60.44| 72.63| 42.11| 85.09| 55.7 | 48.75| 80.96| 31.01 |
| SGPT-5.8B-msmarco | 58.93| 68.13| 40.34| 82 | 56.56| 50.25| 78.1 | 31.46 |
| sgpt-bloom-7b1-msmarco | 57.59| 66.19| 38.93| 81.9 | 55.65| 48.22| 77.74| **33.6** |
||
| Udever-bloom-560m | 55.80| 68.04| 36.89| 81.05| 52.60| 41.19| 79.93| 32.06 |
| Udever-bloom-1b1 | 58.28| 70.18| 39.11| 83.11| 54.28| 45.27| 81.52| 31.10 |
| Udever-bloom-3b | 59.86| 71.91| 40.74| 84.06| 54.90| 47.67| 82.37| 30.62 |
| Udever-bloom-7b1 | 60.63 | 72.13| 40.81| 85.40| 55.91| 49.34| 83.01| 30.97 |
### Table 2: [CodeSearchNet](https://github.com/github/CodeSearchNet)
| CodeSearchNet | Go | Ruby | Python | Java | JS | PHP | Avg. |
|-|-|-|-|-|-|-|-|
| CodeBERT | 69.3 | 70.6 | 84.0 | 86.8 | 74.8 | 70.6 | 76.0 |
| GraphCodeBERT | 84.1 | 73.2 | 87.9 | 75.7 | 71.1 | 72.5 | 77.4 |
| cpt-code S | **97.7** | **86.3** | 99.8 | 94.0 | 86.0 | 96.7 | 93.4 |
| cpt-code M | 97.5 | 85.5 | **99.9** | **94.4** | **86.5** | **97.2** | **93.5** |
| sgpt-bloom-7b1-msmarco | 76.79 | 69.25 | 95.68 | 77.93 | 70.35 | 73.45 | 77.24 |
||
| Udever-bloom-560m | 75.38 | 66.67 | 96.23 | 78.99 | 69.39 | 73.69 | 76.73 |
| Udever-bloom-1b1 | 78.76 | 72.85 | 97.67 | 82.77 | 74.38 | 78.97 | 80.90 |
| Udever-bloom-3b | 80.63 | 75.40 | 98.02 | 83.88 | 76.18 | 79.67 | 82.29 |
| Udever-bloom-7b1 | 79.37 | 76.59 | 98.38 | 84.68 | 77.49 | 80.03 | 82.76 |
### Table 3: Chinese multi-domain retrieval [Multi-cpr](https://dl.acm.org/doi/10.1145/3477495.3531736)
| | | |E-commerce | | Entertainment video | | Medical | |
|--|--|--|--|--|--|--|--|--|
| Model | Train | Backbone | MRR@10 | Recall@1k | MRR@10 | Recall@1k | MRR@10 | Recall@1k |
||
| BM25 | - | - | 0.225 | 0.815 | 0.225 | 0.780 | 0.187 | 0.482 |
| Doc2Query | - | - | 0.239 | 0.826 | 0.238 | 0.794 | 0.210 | 0.505 |
| DPR-1 | In-Domain | BERT | 0.270 | 0.921 | 0.254 | 0.934 | 0.327 | 0.747 |
| DPR-2 | In-Domain | BERT-CT | 0.289 | **0.926** | 0.263 | **0.935** | 0.339 | **0.769** |
| text-embedding-ada-002 | General | GPT | 0.183 | 0.825 | 0.159 | 0.786 | 0.245 | 0.593 |
| sgpt-bloom-7b1-msmarco | General | BLOOM | 0.242 | 0.840 | 0.227 | 0.829 | 0.311 | 0.675 |
||
| Udever-bloom-560m | General | BLOOM | 0.156 | 0.802 | 0.149 | 0.749 | 0.245 | 0.571 |
| Udever-bloom-1b1 | General | BLOOM | 0.244 | 0.863 | 0.208 | 0.815 | 0.241 | 0.557 |
| Udever-bloom-3b | General | BLOOM | 0.267 | 0.871 | 0.228 | 0.836 | 0.288 | 0.619 |
| Udever-bloom-7b1 | General | BLOOM | **0.296** | 0.889 | **0.267** | 0.907 | **0.343** | 0.705 |
#### More results refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 3.
## Technical Specifications
### Model Architecture and Objective
- Model: [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b).
- Objective: Constrastive loss with hard negatives (refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 2.2).
### Compute Infrastructure
- Nvidia A100 SXM4 80GB.
- torch 2.0.0, transformers 4.29.2.
## Citation
**BibTeX:**
```BibTeX
@article{zhang2023language,
title={Language Models are Universal Embedders},
author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min},
journal={arXiv preprint arXiv:2310.08232},
year={2023}
}
```
|
h2oai/h2ogpt-gm-oasst1-en-1024-20b | h2oai | 2023-05-02T19:14:58Z | 4,909 | 4 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-05-02T12:42:21Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
datasets:
- OpenAssistant/oasst1
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b)
- Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `torch` libraries installed.
```bash
pip install transformers==4.28.1
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="h2oai/h2ogpt-gm-oasst1-en-1024-20b",
torch_dtype=torch.float16,
trust_remote_code=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-1024-20b",
padding_side="left"
)
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-1024-20b",
torch_dtype=torch.float16,
device_map={"": "cuda:0"}
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "h2oai/h2ogpt-gm-oasst1-en-1024-20b" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50432, 6144)
(layers): ModuleList(
(0-43): 44 x GPTNeoXLayer(
(input_layernorm): LayerNorm((6144,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((6144,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=6144, out_features=18432, bias=True)
(dense): Linear(in_features=6144, out_features=6144, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=6144, out_features=24576, bias=True)
(dense_4h_to_h): Linear(in_features=24576, out_features=6144, bias=True)
(act): FastGELUActivation()
)
)
)
(final_layer_norm): LayerNorm((6144,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=6144, out_features=50432, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=h2oai/h2ogpt-gm-oasst1-en-1024-20b --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.3490|± |0.0139|
| | |acc_norm|0.3737|± |0.0141|
|arc_easy | 0|acc |0.6271|± |0.0099|
| | |acc_norm|0.5951|± |0.0101|
|boolq | 1|acc |0.6440|± |0.0084|
|hellaswag | 0|acc |0.5366|± |0.0050|
| | |acc_norm|0.7173|± |0.0045|
|openbookqa | 0|acc |0.2920|± |0.0204|
| | |acc_norm|0.4160|± |0.0221|
|piqa | 0|acc |0.7546|± |0.0100|
| | |acc_norm|0.7650|± |0.0099|
|winogrande | 0|acc |0.6527|± |0.0134|
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
internlm/internlm-xcomposer2-4khd-7b | internlm | 2024-04-18T08:41:46Z | 4,908 | 68 | transformers | [
"transformers",
"pytorch",
"internlmxcomposer2",
"text-generation",
"visual-question-answering",
"custom_code",
"arxiv:2401.16420",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | visual-question-answering | 2024-04-07T08:03:48Z | ---
license: other
pipeline_tag: visual-question-answering
---
<p align="center">
<img src="logo_4k_en.png" width="600"/>
<p>
<p align="center">
<b><font size="6">InternLM-XComposer2-4KHD</font></b>
<p>
<div align="center">
[💻Github Repo](https://github.com/InternLM/InternLM-XComposer)
[Paper](https://arxiv.org/abs/2401.16420)
</div>
**InternLM-XComposer2-4KHD** is a general vision-language large model (VLLM) based on [InternLM2](https://github.com/InternLM/InternLM), with the capability of 4K resolution image understanding.
### Import from Transformers
To load the InternLM-XComposer2-4KHD model using Transformers, use the following code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
ckpt_path = "internlm/internlm-xcomposer2-4khd-7b"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True).cuda()
# Set `torch_dtype=torch.floatb16` to load model in bfloat16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
model = model.eval()
```
## Quickstart
We provide a simple example to show how to use InternLM-XComposer with 🤗 Transformers.
```python
import torch
from transformers import AutoModel, AutoTokenizer
torch.set_grad_enabled(False)
# init model and tokenizer
model = AutoModel.from_pretrained('internlm/internlm-xcomposer2-4khd-7b', torch_dtype=torch.bfloat16, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2-4khd-7b', trust_remote_code=True)
###############
# First Round
###############
query1 = '<ImageHere>Illustrate the fine details present in the image'
image = './example.webp'
with torch.cuda.amp.autocast():
response, his = model.chat(tokenizer, query=query, image=image, hd_num=55, history=[], do_sample=False, num_beams=3)
print(response)
# The image is a vibrant and colorful infographic that showcases 7 graphic design trends that will dominate in 2021. The infographic is divided into 7 sections, each representing a different trend.
# Starting from the top, the first section focuses on "Muted Color Palettes", highlighting the use of muted colors in design.
# The second section delves into "Simple Data Visualizations", emphasizing the importance of easy-to-understand data visualizations.
# The third section introduces "Geometric Shapes Everywhere", showcasing the use of geometric shapes in design.
# The fourth section discusses "Flat Icons and Illustrations", explaining how flat icons and illustrations are being used in design.
# The fifth section is dedicated to "Classic Serif Fonts", illustrating the resurgence of classic serif fonts in design.
# The sixth section explores "Social Media Slide Decks", illustrating how slide decks are being used on social media.
# Finally, the seventh section focuses on "Text Heavy Videos", illustrating the trend of using text-heavy videos in design.
# Each section is filled with relevant images and text, providing a comprehensive overview of the 7 graphic design trends that will dominate in 2021.
###############
# Second Round
###############
query1 = 'what is the detailed explanation of the third part.'
with torch.cuda.amp.autocast():
response, _ = model.chat(tokenizer, query=query1, image=image, hd_num=55, history=his, do_sample=False, num_beams=3)
print(response)
# The third part of the infographic is about "Geometric Shapes Everywhere". It explains that last year, designers used a lot of
# flowing and abstract shapes in their designs. However, this year, they have been replaced with rigid, hard-edged geometric
# shapes and patterns. The hard edges of a geometric shape create a great contrast against muted colors.
```
### Open Source License
The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact [email protected]. |
fxmeng/PiSSA-Llama-3-8B-r128 | fxmeng | 2024-05-09T04:26:58Z | 4,905 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-24T14:30:19Z | Entry not found |
Yntec/CinematicReality | Yntec | 2024-03-27T00:00:12Z | 4,901 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-08-05T06:55:25Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
inference: false
language:
- en
---
Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW.
# TODO: Finish this model card. |
ostris/ikea-instructions-lora-sdxl | ostris | 2023-09-29T11:55:15Z | 4,898 | 206 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"style",
"styles",
"instruction_manual",
"ikea",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] | text-to-image | 2023-09-29T11:55:12Z | ---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- style
- styles
- instruction_manual
- ikea
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt:
widget:
- text: "where is waldo "
- text: "sleep"
- text: "hamburger,, lettuce, mayo, lettuce, no tomato "
- text: "barbie and ken "
- text: "back to the future "
- text: "the dude, form the movie the big lebowski, drinking, rug wet, bowling ball "
- text: "hippie "
- text: " fat man, eat pizza, eat hamburgers, drink beer"
- text: " dayman, fighter of the night man, champion of the sun"
- text: " nightman, pay troll toll to get into that boys hole"
---
# Ikea Instructions - LoRA - SDXL
> where is waldo
<p>No trigger word needed. Weight of 1.0 works well on the SDXL 1.0 base. Negatives are usually not needed, but "blurry" and "low quality" seem to help. You can use simple prompts such as "hamburger" or describe steps you want it to show. SDXL does a pretty good job of figuring out the steps to make it.</p>
## Image examples for the model:
> sleep
> hamburger,, lettuce, mayo, lettuce, no tomato
> barbie and ken
> back to the future
> the dude, form the movie the big lebowski, drinking, rug wet, bowling ball
> hippie
> fat man, eat pizza, eat hamburgers, drink beer
> dayman, fighter of the night man, champion of the sun
> nightman, pay troll toll to get into that boys hole
|
ramsrigouthamg/t5_paraphraser | ramsrigouthamg | 2020-12-11T22:00:04Z | 4,897 | 14 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ## Model in Action 🚀
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_paraphraser')
tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_paraphraser')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print ("device ",device)
model = model.to(device)
sentence = "Which course should I take to get started in data science?"
# sentence = "What are the ingredients required to bake a perfect cake?"
# sentence = "What is the best possible approach to learn aeronautical engineering?"
# sentence = "Do apples taste better than oranges in general?"
text = "paraphrase: " + sentence + " </s>"
max_len = 256
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
# set top_k = 50 and set top_p = 0.95 and num_return_sequences = 3
beam_outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
do_sample=True,
max_length=256,
top_k=120,
top_p=0.98,
early_stopping=True,
num_return_sequences=10
)
print ("\nOriginal Question ::")
print (sentence)
print ("\n")
print ("Paraphrased Questions :: ")
final_outputs =[]
for beam_output in beam_outputs:
sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
if sent.lower() != sentence.lower() and sent not in final_outputs:
final_outputs.append(sent)
for i, final_output in enumerate(final_outputs):
print("{}: {}".format(i, final_output))
```
## Output
```
Original Question ::
Which course should I take to get started in data science?
Paraphrased Questions ::
0: What should I learn to become a data scientist?
1: How do I get started with data science?
2: How would you start a data science career?
3: How can I start learning data science?
4: How do you get started in data science?
5: What's the best course for data science?
6: Which course should I start with for data science?
7: What courses should I follow to get started in data science?
8: What degree should be taken by a data scientist?
9: Which course should I follow to become a Data Scientist?
```
## Detailed blog post available here :
https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555
|
KBlueLeaf/kohaku-v2.1 | KBlueLeaf | 2023-08-14T12:58:51Z | 4,896 | 15 | diffusers | [
"diffusers",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-29T10:22:18Z | ---
license: cc-by-nc-nd-4.0
---
|
databricks/dolly-v2-7b | databricks | 2023-06-30T18:33:41Z | 4,895 | 146 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"dataset:databricks/databricks-dolly-15k",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-04-13T05:19:39Z | ---
license: mit
language:
- en
library_name: transformers
inference: false
datasets:
- databricks/databricks-dolly-15k
---
# dolly-v2-7b Model Card
## Summary
Databricks' `dolly-v2-7b`, an instruction-following large language model trained on the Databricks machine learning platform
that is licensed for commercial use. Based on `pythia-6.9b`, Dolly is trained on ~15k instruction/response fine tuning records
[`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated
by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
information extraction, open QA and summarization. `dolly-v2-7b` is not a state-of-the-art model, but does exhibit surprisingly
high quality instruction following behavior not characteristic of the foundation model on which it is based.
Dolly v2 is also available in these other models sizes:
* [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b`
* [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b`
Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on
running inference for various GPU configurations.
**Owner**: Databricks, Inc.
## Model Overview
`dolly-v2-7b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from
[EleutherAI's](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned
on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed.
In a Databricks notebook you could run:
```python
%pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
```
The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline`
found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required.
Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality.
It is also fine to remove it if there is sufficient memory.
```python
import torch
from transformers import pipeline
generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
```
You can then use the pipeline to answer instructions:
```python
res = generate_text("Explain to me the difference between nuclear fission and fusion.")
print(res[0]["generated_text"])
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py),
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-7b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-7b", device_map="auto", torch_dtype=torch.bfloat16)
generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
```
### LangChain Usage
To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned
and the default for the pipeline is to only return the new text.
```python
import torch
from transformers import pipeline
generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16,
trust_remote_code=True, device_map="auto", return_full_text=True)
```
You can create a prompt that either has only an instruction or has an instruction with context:
```python
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline
# template for an instrution with no input
prompt = PromptTemplate(
input_variables=["instruction"],
template="{instruction}")
# template for an instruction with input
prompt_with_context = PromptTemplate(
input_variables=["instruction", "context"],
template="{instruction}\n\nInput:\n{context}")
hf_pipeline = HuggingFacePipeline(pipeline=generate_text)
llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
```
Example predicting using a simple instruction:
```python
print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip())
```
Example predicting using an instruction with context:
```python
context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman,
and Founding Father who served as the first president of the United States from 1789 to 1797."""
print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip())
```
## Known Limitations
### Performance Limitations
**`dolly-v2-7b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform
competitively with more modern model architectures or models subject to larger pretraining corpuses.
The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community.
In particular, `dolly-v2-7b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors,
dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.
Moreover, we find that `dolly-v2-7b` does not have some capabilities, such as well-formatted letter writing, present in the original model.
### Dataset Limitations
Like all language models, `dolly-v2-7b` reflects the content and limitations of its training corpuses.
- **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,
it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly
in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit
associations.
- **`databricks-dolly-15k`**: The training data on which `dolly-v2-7b` is instruction tuned represents natural language instructions generated
by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages
for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or
personally identifying information about non-public figures, but it may contain typos and factual errors.
The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects
the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large.
Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that
maximize the potential of all individuals and organizations.
### Benchmark Metrics
Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness);
model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-7b` is not state of the art,
and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets,
but a robust statement as to the sources of these variations requires further study.
| model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean |
| --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------|
| EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 |
| EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 |
| databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 |
| EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 |
| EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 |
| databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 |
| databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 |
| databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 |
| EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 |
# Citation
```
@online{DatabricksBlog2023DollyV2,
author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
urldate = {2023-06-30}
}
```
# Happy Hacking! |
philschmid/flan-t5-base-samsum | philschmid | 2022-12-23T19:32:18Z | 4,893 | 83 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2022-12-23T19:26:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: train
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 47.2358
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3716
- Rouge1: 47.2358
- Rouge2: 23.5135
- Rougel: 39.6266
- Rougelsum: 43.3458
- Gen Len: 17.3907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4379 | 1.0 | 1842 | 1.3805 | 47.1075 | 23.531 | 39.6919 | 43.549 | 17.1197 |
| 1.3559 | 2.0 | 3684 | 1.3716 | 47.2358 | 23.5135 | 39.6266 | 43.3458 | 17.3907 |
| 1.2783 | 3.0 | 5526 | 1.3721 | 47.4581 | 23.7339 | 39.7726 | 43.4568 | 17.1832 |
| 1.2378 | 4.0 | 7368 | 1.3757 | 47.8557 | 24.0593 | 40.2324 | 44.0085 | 17.3053 |
| 1.1983 | 5.0 | 9210 | 1.3751 | 47.8156 | 24.0038 | 40.2169 | 43.8918 | 17.3040 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.8.0
- Tokenizers 0.12.1
|
backyardai/L3-8B-Stheno-v3.2-GGUF | backyardai | 2024-06-14T21:26:00Z | 4,892 | 4 | null | [
"gguf",
"en",
"dataset:Gryphe/Opus-WritingPrompts",
"dataset:Sao10K/Claude-3-Opus-Instruct-15K",
"dataset:Sao10K/Short-Storygen-v2",
"dataset:Sao10K/c2-Logs-Filtered",
"base_model:Sao10K/L3-8B-Stheno-v3.2",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-06-14T21:10:53Z | ---
language:
- en
license: cc-by-nc-4.0
base_model: Sao10K/L3-8B-Stheno-v3.2
datasets:
- Gryphe/Opus-WritingPrompts
- Sao10K/Claude-3-Opus-Instruct-15K
- Sao10K/Short-Storygen-v2
- Sao10K/c2-Logs-Filtered
model_name: L3-8B-Stheno-v3.2-GGUF
quantized_by: brooketh
parameter_count: 8030261248
---
<img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;">
**<p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p>**
<p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p>
<p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p>
***
# Llama 3 Stheno V3.2 8B
- **Creator:** [Sao10K](https://huggingface.co/Sao10K/)
- **Original:** [Llama 3 Stheno V3.2 8B](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2)
- **Date Created:** 2024-06-05
- **Trained Context:** 8192 tokens
- **Description:** High performing roleplaying model trained on thousands of SFW and NSFW short stories curated from Reddit. Said to be more balanced than previous iterations of this model, with better multi-turn coherency but slightly less creativity.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
***
<img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;">
## Backyard AI
- Free, local AI chat application.
- One-click installation on Mac and PC.
- Automatically use GPU for maximum speed.
- Built-in model manager.
- High-quality character hub.
- Zero-config desktop-to-mobile tethering.
Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable.
**Join us on [Discord](https://discord.gg/SyNN2vC9tQ)**
***
|
Remek/Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT | Remek | 2024-04-24T07:23:40Z | 4,890 | 14 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"pl",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-22T19:23:07Z | ---
language:
- pl
- en
pipeline_tag: text-generation
license: cc-by-4.0
---
## Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT
Repozytorium zawiera model Meta Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT w wersji polskojęzycznej. Jest to model INSTRUCT (instrukcyjny). Model postał na podstawie finetuningu modelu bazowego Llama-3-8B. Wykorzystano do tego dataset instrukcji Omnibus-1-PL (stworzyłem go na własne potrzeby przeprowadzania eksperymenów finetuningu modeli w języku polskim). Szczegóły parametrów treningu w sekcji Trening. Celem tego eksperymentu było sprawdzenie czy można namówić Llama-3-8B do płynnego rozmawiania w języku polskim (oryginalny model instrukcyjny 8B ma z tym problem - woli zdecydowanie bardziej rozmawiać po angielsku).
<img src="Llama-3-8B-PL-small.jpg" width="420" />
Uwaga!
* Model NIE jest CENZUROWANY. To wersja do zabawy. Nie została ujarzmiona.
* Model będzie dalej rozwijany ponieważ eksperymentuję z a. kolejnymi wersjami datasetu, b. model jest świetną bazą do testowania różnych technik finetunowania (LoRA, QLoRA; DPO, ORPO itd.)
* Udostępniłem go spontanicznie by użytkownicy mogli go używać i sprawdzać jakość Llama 3 ale w kontekście języka polskiego.
* Po informacji, że baza była trenowana na 15T tokenów (tylko 5% nie angielskich) uznałem, że to świetny model do finetuningu. Być może lekkie dotrenowanie modelu za pomocą contingued-pretraining da jeszcze większy uzysk.
### Sposób kodowania nazwy modelu
* Nazwa modelu bazowego: Llama-3-8B
* Nazwa datasetu: Omnibus-1
* Wersja językowa: PL (polska)
* Wersja modelu: v01
### Dataset
Omnibus-1 to zbiór polskich instrukcji (100% kontekstu Polskiego - fakty, osoby, miejsca osadzone w Polsce), który został w 100% syntetycznie wygenerowany. Zawiera on instrukcje z kategorii - matematyka, umiejętność pisania, dialogi, tematy medyczne, zagadki logiczne, tłumaczenia itd. Powstał on w ramach moich prac związanych z badaniem jakości modeli w kontekście języka polskiego. Pozwala on na finetuning modelu i sprawdzenie podatności modelu do mówienia w naszym rodzimym języku. Dataset zawiera obecnie 75.000 instrukcji. Nie jest on idealny i wymaga jeszcze dużo nakładu pracy by model pracował w przewidywalnie ale będzie cały czas udoskonalony i być może w przyszłości udostępniony (jak uznam, że już jest wtstarczająco pełen i obejmuje szerokie spektrum tematyki i umiejętności). Dataset jest w 100% generowany za pomocą innych LLM.
### Użycie Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT z biblioteką Transformers
Poniższy kod używa bibliotekę Transformers:
```python
import transformers
import torch
model_id = "Remek/Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability."},
{"role": "user", "content": "Napisz czym jest Warszawa?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
Przykładowa odpowiedź:
> Warszawa to stolica Polski, położona na terenie Mazowsza. Jest to największe miasto w Polsce pod względem liczby mieszkańców oraz powierzchni. Warszawa jest również siedzibą rządu, parlamentu, prezydenta, sądu najwyższego oraz wielu innych instytucji państwowych i międzynarodowych. Miasto jest znane z wielu zabytków, takich jak Stare Miasto, Zamek Królewski, Pałac Kultury i Nauki, a także z nowoczesnych atrakcji, takich jak Centrum Nauki Kopernik czy Centrum Nauki i Sztuki. Warszawa jest również ważnym centrum kulturalnym, gospodarczym i naukowym. Miasto jest również siedzibą wielu firm międzynarodowych i polskich przedsiębiorstw. Warszawa jest miastem, które łączy w sobie przeszłość z nowoczesnością, oferując mieszkańcom i turystom szeroki zakres możliwości. Miasto jest również znane z wielu festiwali i wydarzeń kulturalnych, które przyciągają miliony turystów z całego świata. Warszawa to miasto pełne życia, kultury, historii i nowoczesności, które zdecydowanie zasługuje na uwagę. <|im_end|>
### Szablon konwersacji
Szablon konwersacji to oryginalna wersja Llama3
```
<|start_header_id|>system<|end_header_id|>
You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.
<|eot_id|>
<|start_header_id|>user<|end_header_id|>
{User}
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{Assistant}
```
### Wersje quantized
Wersje poddane quantyzacji są dostępne w repozytorium:
* Llama-3-8B-Omnibus-1-PL-v01-instruct-GGUF - przetestowane w LM Studio (wybierz szablon - Llama3) oraz ollama
*
| Version | Model card |
| ------- | -------------------------------------------------------------------------- |
| Instruct| [🤗 HuggingFace](https://huggingface.co/Remek/Llama-3-8B-Omnibus-1-PL-v01-instruct-GGUF) |
### Trening
Poniżej szczegóły hiperparametrów treningu:
* learning_rate: 2e-05
* train_batch_size: 8
* eval_batch_size: 8
* seed: 42
* distributed_type: single-GPU (Nvidia A6000 Ada)
* num_devices: 1
* gradient_accumulation_steps: 4
* optimizer: adamw_8bit
* lr_scheduler_type: linear
* lr_scheduler_warmup_steps: 5
* num_epochs: 1
* QLoRa - 4bit: rank 64, alpha 128
#### Unsloth
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200px" align="center" />
[Unsloth](https://unsloth.ai), narzędzie dzięki któremu powstał ten model.
### Licencja
Licencja na zasadzie nie do komercyjnego użycia (ze względu na dataset - generowany syntetycznie za pomocą modeli GPT4, GPT3.5) oraz licencja Llama3 (proszę o zapoznanie się ze szczegółami licencji). |
mradermacher/Rawr_Llama3_8B-i1-GGUF | mradermacher | 2024-06-06T21:49:21Z | 4,890 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ResplendentAI/Rawr_Llama3_8B",
"endpoints_compatible",
"region:us"
] | null | 2024-06-05T14:46:47Z | ---
base_model: ResplendentAI/Rawr_Llama3_8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ResplendentAI/Rawr_Llama3_8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Rawr_Llama3_8B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Rawr_Llama3_8B-i1-GGUF/resolve/main/Rawr_Llama3_8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
Yntec/a-ZovyaRPGV4 | Yntec | 2024-05-08T21:22:45Z | 4,888 | 2 | diffusers | [
"diffusers",
"safetensors",
"General Purpose",
"Digital Art",
"Photorealistic",
"Zovya",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-08T20:45:23Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- General Purpose
- Digital Art
- Photorealistic
- Zovya
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# A-Zovya RPG Artist Tools V4
Original page: https://civitai.com/models/8124?modelVersionId=251729
Samples and prompts:
)
(Click for larger)
Top left: pretty cute girl, jewelry, solo, earrings, long hair, necklace, blonde hair, looking at viewer, smile, pearl necklace, blue eyes, upper body, eyelashes, simple background, shirt, white shirt, hairband, pink hairband, lips, curly hair, closed mouth, wavy hair, light particles, makeup, pink lips, lipstick, pink bow, blush, ribbon, bow, red lips, pink background, hair ribbon, looking to the side, from side, hair bow, pink ribbon, bead necklace, sparkle, artist name, portrait, bangs, flower earrings, hair over shoulder, light smile, looking back, pearl \(gemstone\), mascara, aqua eyes, brown background, grey background, Barbie,
Top right: street art, japanese, kawaii, anime, action scene
Bottom left: Jungle, waterfall, floating island, dragon,
Bottom right: (high quality, best quality:1.2), sexy outfit, adorable plant girl, collar, plant hybrid, (teal skin:1.5), in the amazon rainforest |
mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF | mradermacher | 2024-06-16T09:54:03Z | 4,887 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Kaoeiri/L3MaidRPKraiKei-V1.6-8B",
"Sao10K/L3-8B-Stheno-v3.2",
"openlynn/Llama-3-Soliloquy-8B-v2",
"en",
"base_model:Kaoeiri/L3MaidRPKraiKei-V1.66-8B-2",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T05:08:30Z | ---
base_model: Kaoeiri/L3MaidRPKraiKei-V1.66-8B-2
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- Kaoeiri/L3MaidRPKraiKei-V1.6-8B
- Sao10K/L3-8B-Stheno-v3.2
- openlynn/Llama-3-Soliloquy-8B-v2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Kaoeiri/L3MaidRPKraiKei-V1.66-8B-2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3MaidRPKraiKei-V1.66-8B-2-i1-GGUF/resolve/main/L3MaidRPKraiKei-V1.66-8B-2.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
NousResearch/Meta-Llama-3-70B | NousResearch | 2024-04-30T05:21:54Z | 4,884 | 10 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-18T17:21:50Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
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distributed by Meta at https://llama.meta.com/get-started/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into
this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
regulations to provide legal consent and that has legal authority to bind your employer or such other
person or entity if you are entering in this Agreement on their behalf.
"Meta Llama 3" means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
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"Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
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c. If you institute litigation or other proceedings against Meta or any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or
results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other
rights owned or licensable by you, then any licenses granted to you under this Agreement shall
terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related to your use or
distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this
Agreement or access to the Llama Materials and will continue in full force and effect until terminated in
accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in
breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of
the State of California without regard to choice of law principles, and the UN Convention on Contracts
for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
exclusive jurisdiction of any dispute arising out of this Agreement.
### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
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---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
>>> import transformers
>>> import torch
>>> model_id = "meta-llama/Meta-Llama-3-70B"
>>> pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
>>> pipeline("Hey how are you doing today?")
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-70B --include "original/*" --local-dir Meta-Llama-3-70B
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit | Muennighoff | 2023-03-27T22:21:38Z | 4,883 | 5 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | ---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: SGPT-1.3B-weightedmean-msmarco-specb-bitfit
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 65.20895522388061
- type: ap
value: 29.59212705444778
- type: f1
value: 59.97099864321921
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 73.20565
- type: ap
value: 67.36680643550963
- type: f1
value: 72.90420520325125
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 34.955999999999996
- type: f1
value: 34.719324437696955
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 26.101999999999997
- type: map_at_10
value: 40.958
- type: map_at_100
value: 42.033
- type: map_at_1000
value: 42.042
- type: map_at_3
value: 36.332
- type: map_at_5
value: 38.608
- type: mrr_at_1
value: 26.387
- type: mrr_at_10
value: 41.051
- type: mrr_at_100
value: 42.118
- type: mrr_at_1000
value: 42.126999999999995
- type: mrr_at_3
value: 36.415
- type: mrr_at_5
value: 38.72
- type: ndcg_at_1
value: 26.101999999999997
- type: ndcg_at_10
value: 49.68
- type: ndcg_at_100
value: 54.257999999999996
- type: ndcg_at_1000
value: 54.486000000000004
- type: ndcg_at_3
value: 39.864
- type: ndcg_at_5
value: 43.980000000000004
- type: precision_at_1
value: 26.101999999999997
- type: precision_at_10
value: 7.781000000000001
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.714000000000002
- type: precision_at_5
value: 12.034
- type: recall_at_1
value: 26.101999999999997
- type: recall_at_10
value: 77.809
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 50.141999999999996
- type: recall_at_5
value: 60.171
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 43.384194916953774
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 33.70962633433912
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 58.133058996870076
- type: mrr
value: 72.10922041946972
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 86.62153841660047
- type: cos_sim_spearman
value: 83.01514456843276
- type: euclidean_pearson
value: 86.00431518427241
- type: euclidean_spearman
value: 83.85552516285783
- type: manhattan_pearson
value: 85.83025803351181
- type: manhattan_spearman
value: 83.86636878343106
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 82.05844155844156
- type: f1
value: 82.0185837884764
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 35.05918333141837
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 30.71055028830579
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 26.519
- type: map_at_10
value: 35.634
- type: map_at_100
value: 36.961
- type: map_at_1000
value: 37.088
- type: map_at_3
value: 32.254
- type: map_at_5
value: 34.22
- type: mrr_at_1
value: 32.332
- type: mrr_at_10
value: 41.168
- type: mrr_at_100
value: 41.977
- type: mrr_at_1000
value: 42.028999999999996
- type: mrr_at_3
value: 38.196999999999996
- type: mrr_at_5
value: 40.036
- type: ndcg_at_1
value: 32.332
- type: ndcg_at_10
value: 41.471000000000004
- type: ndcg_at_100
value: 46.955999999999996
- type: ndcg_at_1000
value: 49.262
- type: ndcg_at_3
value: 35.937999999999995
- type: ndcg_at_5
value: 38.702999999999996
- type: precision_at_1
value: 32.332
- type: precision_at_10
value: 7.7829999999999995
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 16.834
- type: precision_at_5
value: 12.418
- type: recall_at_1
value: 26.519
- type: recall_at_10
value: 53.190000000000005
- type: recall_at_100
value: 76.56500000000001
- type: recall_at_1000
value: 91.47800000000001
- type: recall_at_3
value: 38.034
- type: recall_at_5
value: 45.245999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 25.356
- type: map_at_10
value: 34.596
- type: map_at_100
value: 35.714
- type: map_at_1000
value: 35.839999999999996
- type: map_at_3
value: 32.073
- type: map_at_5
value: 33.475
- type: mrr_at_1
value: 31.274
- type: mrr_at_10
value: 39.592
- type: mrr_at_100
value: 40.284
- type: mrr_at_1000
value: 40.339999999999996
- type: mrr_at_3
value: 37.378
- type: mrr_at_5
value: 38.658
- type: ndcg_at_1
value: 31.274
- type: ndcg_at_10
value: 39.766
- type: ndcg_at_100
value: 44.028
- type: ndcg_at_1000
value: 46.445
- type: ndcg_at_3
value: 35.934
- type: ndcg_at_5
value: 37.751000000000005
- type: precision_at_1
value: 31.274
- type: precision_at_10
value: 7.452
- type: precision_at_100
value: 1.217
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 17.431
- type: precision_at_5
value: 12.306000000000001
- type: recall_at_1
value: 25.356
- type: recall_at_10
value: 49.344
- type: recall_at_100
value: 67.497
- type: recall_at_1000
value: 83.372
- type: recall_at_3
value: 38.227
- type: recall_at_5
value: 43.187999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 32.759
- type: map_at_10
value: 43.937
- type: map_at_100
value: 45.004
- type: map_at_1000
value: 45.07
- type: map_at_3
value: 40.805
- type: map_at_5
value: 42.497
- type: mrr_at_1
value: 37.367
- type: mrr_at_10
value: 47.237
- type: mrr_at_100
value: 47.973
- type: mrr_at_1000
value: 48.010999999999996
- type: mrr_at_3
value: 44.65
- type: mrr_at_5
value: 46.050999999999995
- type: ndcg_at_1
value: 37.367
- type: ndcg_at_10
value: 49.659
- type: ndcg_at_100
value: 54.069
- type: ndcg_at_1000
value: 55.552
- type: ndcg_at_3
value: 44.169000000000004
- type: ndcg_at_5
value: 46.726
- type: precision_at_1
value: 37.367
- type: precision_at_10
value: 8.163
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 19.707
- type: precision_at_5
value: 13.718
- type: recall_at_1
value: 32.759
- type: recall_at_10
value: 63.341
- type: recall_at_100
value: 82.502
- type: recall_at_1000
value: 93.259
- type: recall_at_3
value: 48.796
- type: recall_at_5
value: 54.921
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.962
- type: map_at_10
value: 25.863000000000003
- type: map_at_100
value: 26.817999999999998
- type: map_at_1000
value: 26.918
- type: map_at_3
value: 23.043
- type: map_at_5
value: 24.599
- type: mrr_at_1
value: 20.452
- type: mrr_at_10
value: 27.301
- type: mrr_at_100
value: 28.233000000000004
- type: mrr_at_1000
value: 28.310000000000002
- type: mrr_at_3
value: 24.539
- type: mrr_at_5
value: 26.108999999999998
- type: ndcg_at_1
value: 20.452
- type: ndcg_at_10
value: 30.354999999999997
- type: ndcg_at_100
value: 35.336
- type: ndcg_at_1000
value: 37.927
- type: ndcg_at_3
value: 24.705
- type: ndcg_at_5
value: 27.42
- type: precision_at_1
value: 20.452
- type: precision_at_10
value: 4.949
- type: precision_at_100
value: 0.7799999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 10.358
- type: precision_at_5
value: 7.774
- type: recall_at_1
value: 18.962
- type: recall_at_10
value: 43.056
- type: recall_at_100
value: 66.27300000000001
- type: recall_at_1000
value: 85.96000000000001
- type: recall_at_3
value: 27.776
- type: recall_at_5
value: 34.287
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 11.24
- type: map_at_10
value: 18.503
- type: map_at_100
value: 19.553
- type: map_at_1000
value: 19.689999999999998
- type: map_at_3
value: 16.150000000000002
- type: map_at_5
value: 17.254
- type: mrr_at_1
value: 13.806
- type: mrr_at_10
value: 21.939
- type: mrr_at_100
value: 22.827
- type: mrr_at_1000
value: 22.911
- type: mrr_at_3
value: 19.32
- type: mrr_at_5
value: 20.558
- type: ndcg_at_1
value: 13.806
- type: ndcg_at_10
value: 23.383000000000003
- type: ndcg_at_100
value: 28.834
- type: ndcg_at_1000
value: 32.175
- type: ndcg_at_3
value: 18.651999999999997
- type: ndcg_at_5
value: 20.505000000000003
- type: precision_at_1
value: 13.806
- type: precision_at_10
value: 4.714
- type: precision_at_100
value: 0.864
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 9.328
- type: precision_at_5
value: 6.841
- type: recall_at_1
value: 11.24
- type: recall_at_10
value: 34.854
- type: recall_at_100
value: 59.50299999999999
- type: recall_at_1000
value: 83.25
- type: recall_at_3
value: 22.02
- type: recall_at_5
value: 26.715
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.012
- type: map_at_10
value: 33.048
- type: map_at_100
value: 34.371
- type: map_at_1000
value: 34.489
- type: map_at_3
value: 29.942999999999998
- type: map_at_5
value: 31.602000000000004
- type: mrr_at_1
value: 28.104000000000003
- type: mrr_at_10
value: 37.99
- type: mrr_at_100
value: 38.836
- type: mrr_at_1000
value: 38.891
- type: mrr_at_3
value: 35.226
- type: mrr_at_5
value: 36.693999999999996
- type: ndcg_at_1
value: 28.104000000000003
- type: ndcg_at_10
value: 39.037
- type: ndcg_at_100
value: 44.643
- type: ndcg_at_1000
value: 46.939
- type: ndcg_at_3
value: 33.784
- type: ndcg_at_5
value: 36.126000000000005
- type: precision_at_1
value: 28.104000000000003
- type: precision_at_10
value: 7.2669999999999995
- type: precision_at_100
value: 1.193
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 16.298000000000002
- type: precision_at_5
value: 11.684
- type: recall_at_1
value: 23.012
- type: recall_at_10
value: 52.054
- type: recall_at_100
value: 75.622
- type: recall_at_1000
value: 90.675
- type: recall_at_3
value: 37.282
- type: recall_at_5
value: 43.307
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 30.209999999999997
- type: map_at_100
value: 31.52
- type: map_at_1000
value: 31.625999999999998
- type: map_at_3
value: 26.951000000000004
- type: map_at_5
value: 28.938999999999997
- type: mrr_at_1
value: 26.941
- type: mrr_at_10
value: 35.13
- type: mrr_at_100
value: 36.15
- type: mrr_at_1000
value: 36.204
- type: mrr_at_3
value: 32.42
- type: mrr_at_5
value: 34.155
- type: ndcg_at_1
value: 26.941
- type: ndcg_at_10
value: 35.726
- type: ndcg_at_100
value: 41.725
- type: ndcg_at_1000
value: 44.105
- type: ndcg_at_3
value: 30.184
- type: ndcg_at_5
value: 33.176
- type: precision_at_1
value: 26.941
- type: precision_at_10
value: 6.654999999999999
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 14.346
- type: precision_at_5
value: 10.868
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 47.359
- type: recall_at_100
value: 73.436
- type: recall_at_1000
value: 89.988
- type: recall_at_3
value: 32.34
- type: recall_at_5
value: 39.856
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.67566666666667
- type: map_at_10
value: 28.479333333333333
- type: map_at_100
value: 29.612249999999996
- type: map_at_1000
value: 29.731166666666663
- type: map_at_3
value: 25.884
- type: map_at_5
value: 27.298916666666667
- type: mrr_at_1
value: 24.402583333333332
- type: mrr_at_10
value: 32.07041666666667
- type: mrr_at_100
value: 32.95841666666667
- type: mrr_at_1000
value: 33.025416666666665
- type: mrr_at_3
value: 29.677749999999996
- type: mrr_at_5
value: 31.02391666666667
- type: ndcg_at_1
value: 24.402583333333332
- type: ndcg_at_10
value: 33.326166666666666
- type: ndcg_at_100
value: 38.51566666666667
- type: ndcg_at_1000
value: 41.13791666666667
- type: ndcg_at_3
value: 28.687749999999994
- type: ndcg_at_5
value: 30.84766666666667
- type: precision_at_1
value: 24.402583333333332
- type: precision_at_10
value: 5.943749999999999
- type: precision_at_100
value: 1.0098333333333334
- type: precision_at_1000
value: 0.14183333333333334
- type: precision_at_3
value: 13.211500000000001
- type: precision_at_5
value: 9.548416666666668
- type: recall_at_1
value: 20.67566666666667
- type: recall_at_10
value: 44.245583333333336
- type: recall_at_100
value: 67.31116666666667
- type: recall_at_1000
value: 85.87841666666665
- type: recall_at_3
value: 31.49258333333333
- type: recall_at_5
value: 36.93241666666667
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.34
- type: map_at_10
value: 23.988
- type: map_at_100
value: 24.895
- type: map_at_1000
value: 24.992
- type: map_at_3
value: 21.831
- type: map_at_5
value: 23.0
- type: mrr_at_1
value: 20.399
- type: mrr_at_10
value: 26.186
- type: mrr_at_100
value: 27.017999999999997
- type: mrr_at_1000
value: 27.090999999999998
- type: mrr_at_3
value: 24.08
- type: mrr_at_5
value: 25.230000000000004
- type: ndcg_at_1
value: 20.399
- type: ndcg_at_10
value: 27.799000000000003
- type: ndcg_at_100
value: 32.579
- type: ndcg_at_1000
value: 35.209
- type: ndcg_at_3
value: 23.684
- type: ndcg_at_5
value: 25.521
- type: precision_at_1
value: 20.399
- type: precision_at_10
value: 4.585999999999999
- type: precision_at_100
value: 0.755
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 10.276
- type: precision_at_5
value: 7.362
- type: recall_at_1
value: 18.34
- type: recall_at_10
value: 37.456
- type: recall_at_100
value: 59.86
- type: recall_at_1000
value: 79.703
- type: recall_at_3
value: 26.163999999999998
- type: recall_at_5
value: 30.652
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 12.327
- type: map_at_10
value: 17.572
- type: map_at_100
value: 18.534
- type: map_at_1000
value: 18.653
- type: map_at_3
value: 15.703
- type: map_at_5
value: 16.752
- type: mrr_at_1
value: 15.038000000000002
- type: mrr_at_10
value: 20.726
- type: mrr_at_100
value: 21.61
- type: mrr_at_1000
value: 21.695
- type: mrr_at_3
value: 18.829
- type: mrr_at_5
value: 19.885
- type: ndcg_at_1
value: 15.038000000000002
- type: ndcg_at_10
value: 21.241
- type: ndcg_at_100
value: 26.179000000000002
- type: ndcg_at_1000
value: 29.316
- type: ndcg_at_3
value: 17.762
- type: ndcg_at_5
value: 19.413
- type: precision_at_1
value: 15.038000000000002
- type: precision_at_10
value: 3.8920000000000003
- type: precision_at_100
value: 0.75
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 8.351
- type: precision_at_5
value: 6.187
- type: recall_at_1
value: 12.327
- type: recall_at_10
value: 29.342000000000002
- type: recall_at_100
value: 51.854
- type: recall_at_1000
value: 74.648
- type: recall_at_3
value: 19.596
- type: recall_at_5
value: 23.899
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.594
- type: map_at_10
value: 27.878999999999998
- type: map_at_100
value: 28.926000000000002
- type: map_at_1000
value: 29.041
- type: map_at_3
value: 25.668999999999997
- type: map_at_5
value: 26.773999999999997
- type: mrr_at_1
value: 23.694000000000003
- type: mrr_at_10
value: 31.335
- type: mrr_at_100
value: 32.218
- type: mrr_at_1000
value: 32.298
- type: mrr_at_3
value: 29.26
- type: mrr_at_5
value: 30.328
- type: ndcg_at_1
value: 23.694000000000003
- type: ndcg_at_10
value: 32.456
- type: ndcg_at_100
value: 37.667
- type: ndcg_at_1000
value: 40.571
- type: ndcg_at_3
value: 28.283
- type: ndcg_at_5
value: 29.986
- type: precision_at_1
value: 23.694000000000003
- type: precision_at_10
value: 5.448
- type: precision_at_100
value: 0.9119999999999999
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 12.717999999999998
- type: precision_at_5
value: 8.843
- type: recall_at_1
value: 20.594
- type: recall_at_10
value: 43.004999999999995
- type: recall_at_100
value: 66.228
- type: recall_at_1000
value: 87.17099999999999
- type: recall_at_3
value: 31.554
- type: recall_at_5
value: 35.838
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.855999999999998
- type: map_at_10
value: 28.372000000000003
- type: map_at_100
value: 29.87
- type: map_at_1000
value: 30.075000000000003
- type: map_at_3
value: 26.054
- type: map_at_5
value: 27.128999999999998
- type: mrr_at_1
value: 25.494
- type: mrr_at_10
value: 32.735
- type: mrr_at_100
value: 33.794000000000004
- type: mrr_at_1000
value: 33.85
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 31.897
- type: ndcg_at_1
value: 25.494
- type: ndcg_at_10
value: 33.385
- type: ndcg_at_100
value: 39.436
- type: ndcg_at_1000
value: 42.313
- type: ndcg_at_3
value: 29.612
- type: ndcg_at_5
value: 31.186999999999998
- type: precision_at_1
value: 25.494
- type: precision_at_10
value: 6.422999999999999
- type: precision_at_100
value: 1.383
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 13.834
- type: precision_at_5
value: 10.0
- type: recall_at_1
value: 20.855999999999998
- type: recall_at_10
value: 42.678
- type: recall_at_100
value: 70.224
- type: recall_at_1000
value: 89.369
- type: recall_at_3
value: 31.957
- type: recall_at_5
value: 36.026
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.519000000000002
- type: map_at_10
value: 22.15
- type: map_at_100
value: 23.180999999999997
- type: map_at_1000
value: 23.291999999999998
- type: map_at_3
value: 20.132
- type: map_at_5
value: 21.346
- type: mrr_at_1
value: 17.93
- type: mrr_at_10
value: 23.506
- type: mrr_at_100
value: 24.581
- type: mrr_at_1000
value: 24.675
- type: mrr_at_3
value: 21.503
- type: mrr_at_5
value: 22.686
- type: ndcg_at_1
value: 17.93
- type: ndcg_at_10
value: 25.636
- type: ndcg_at_100
value: 30.736
- type: ndcg_at_1000
value: 33.841
- type: ndcg_at_3
value: 21.546000000000003
- type: ndcg_at_5
value: 23.658
- type: precision_at_1
value: 17.93
- type: precision_at_10
value: 3.993
- type: precision_at_100
value: 0.6890000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 9.057
- type: precision_at_5
value: 6.58
- type: recall_at_1
value: 16.519000000000002
- type: recall_at_10
value: 35.268
- type: recall_at_100
value: 58.17
- type: recall_at_1000
value: 81.66799999999999
- type: recall_at_3
value: 24.165
- type: recall_at_5
value: 29.254
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 10.363
- type: map_at_10
value: 18.301000000000002
- type: map_at_100
value: 20.019000000000002
- type: map_at_1000
value: 20.207
- type: map_at_3
value: 14.877
- type: map_at_5
value: 16.544
- type: mrr_at_1
value: 22.866
- type: mrr_at_10
value: 34.935
- type: mrr_at_100
value: 35.802
- type: mrr_at_1000
value: 35.839999999999996
- type: mrr_at_3
value: 30.965999999999998
- type: mrr_at_5
value: 33.204
- type: ndcg_at_1
value: 22.866
- type: ndcg_at_10
value: 26.595000000000002
- type: ndcg_at_100
value: 33.513999999999996
- type: ndcg_at_1000
value: 36.872
- type: ndcg_at_3
value: 20.666999999999998
- type: ndcg_at_5
value: 22.728
- type: precision_at_1
value: 22.866
- type: precision_at_10
value: 8.632
- type: precision_at_100
value: 1.6119999999999999
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 15.504999999999999
- type: precision_at_5
value: 12.404
- type: recall_at_1
value: 10.363
- type: recall_at_10
value: 33.494
- type: recall_at_100
value: 57.593
- type: recall_at_1000
value: 76.342
- type: recall_at_3
value: 19.157
- type: recall_at_5
value: 24.637999999999998
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 7.436
- type: map_at_10
value: 14.760000000000002
- type: map_at_100
value: 19.206
- type: map_at_1000
value: 20.267
- type: map_at_3
value: 10.894
- type: map_at_5
value: 12.828999999999999
- type: mrr_at_1
value: 54.25
- type: mrr_at_10
value: 63.769
- type: mrr_at_100
value: 64.193
- type: mrr_at_1000
value: 64.211
- type: mrr_at_3
value: 61.458
- type: mrr_at_5
value: 63.096
- type: ndcg_at_1
value: 42.875
- type: ndcg_at_10
value: 31.507
- type: ndcg_at_100
value: 34.559
- type: ndcg_at_1000
value: 41.246
- type: ndcg_at_3
value: 35.058
- type: ndcg_at_5
value: 33.396
- type: precision_at_1
value: 54.25
- type: precision_at_10
value: 24.45
- type: precision_at_100
value: 7.383000000000001
- type: precision_at_1000
value: 1.582
- type: precision_at_3
value: 38.083
- type: precision_at_5
value: 32.6
- type: recall_at_1
value: 7.436
- type: recall_at_10
value: 19.862
- type: recall_at_100
value: 38.981
- type: recall_at_1000
value: 61.038000000000004
- type: recall_at_3
value: 11.949
- type: recall_at_5
value: 15.562000000000001
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 46.39
- type: f1
value: 42.26424885856703
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 50.916
- type: map_at_10
value: 62.258
- type: map_at_100
value: 62.741
- type: map_at_1000
value: 62.763000000000005
- type: map_at_3
value: 60.01800000000001
- type: map_at_5
value: 61.419999999999995
- type: mrr_at_1
value: 54.964999999999996
- type: mrr_at_10
value: 66.554
- type: mrr_at_100
value: 66.96600000000001
- type: mrr_at_1000
value: 66.97800000000001
- type: mrr_at_3
value: 64.414
- type: mrr_at_5
value: 65.77
- type: ndcg_at_1
value: 54.964999999999996
- type: ndcg_at_10
value: 68.12
- type: ndcg_at_100
value: 70.282
- type: ndcg_at_1000
value: 70.788
- type: ndcg_at_3
value: 63.861999999999995
- type: ndcg_at_5
value: 66.216
- type: precision_at_1
value: 54.964999999999996
- type: precision_at_10
value: 8.998000000000001
- type: precision_at_100
value: 1.016
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 25.618000000000002
- type: precision_at_5
value: 16.676
- type: recall_at_1
value: 50.916
- type: recall_at_10
value: 82.04
- type: recall_at_100
value: 91.689
- type: recall_at_1000
value: 95.34899999999999
- type: recall_at_3
value: 70.512
- type: recall_at_5
value: 76.29899999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 13.568
- type: map_at_10
value: 23.264000000000003
- type: map_at_100
value: 24.823999999999998
- type: map_at_1000
value: 25.013999999999996
- type: map_at_3
value: 19.724
- type: map_at_5
value: 21.772
- type: mrr_at_1
value: 27.315
- type: mrr_at_10
value: 35.935
- type: mrr_at_100
value: 36.929
- type: mrr_at_1000
value: 36.985
- type: mrr_at_3
value: 33.591
- type: mrr_at_5
value: 34.848
- type: ndcg_at_1
value: 27.315
- type: ndcg_at_10
value: 29.988
- type: ndcg_at_100
value: 36.41
- type: ndcg_at_1000
value: 40.184999999999995
- type: ndcg_at_3
value: 26.342
- type: ndcg_at_5
value: 27.68
- type: precision_at_1
value: 27.315
- type: precision_at_10
value: 8.565000000000001
- type: precision_at_100
value: 1.508
- type: precision_at_1000
value: 0.219
- type: precision_at_3
value: 17.849999999999998
- type: precision_at_5
value: 13.672999999999998
- type: recall_at_1
value: 13.568
- type: recall_at_10
value: 37.133
- type: recall_at_100
value: 61.475
- type: recall_at_1000
value: 84.372
- type: recall_at_3
value: 24.112000000000002
- type: recall_at_5
value: 29.507
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 30.878
- type: map_at_10
value: 40.868
- type: map_at_100
value: 41.693999999999996
- type: map_at_1000
value: 41.775
- type: map_at_3
value: 38.56
- type: map_at_5
value: 39.947
- type: mrr_at_1
value: 61.756
- type: mrr_at_10
value: 68.265
- type: mrr_at_100
value: 68.671
- type: mrr_at_1000
value: 68.694
- type: mrr_at_3
value: 66.78399999999999
- type: mrr_at_5
value: 67.704
- type: ndcg_at_1
value: 61.756
- type: ndcg_at_10
value: 49.931
- type: ndcg_at_100
value: 53.179
- type: ndcg_at_1000
value: 54.94799999999999
- type: ndcg_at_3
value: 46.103
- type: ndcg_at_5
value: 48.147
- type: precision_at_1
value: 61.756
- type: precision_at_10
value: 10.163
- type: precision_at_100
value: 1.2710000000000001
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 28.179
- type: precision_at_5
value: 18.528
- type: recall_at_1
value: 30.878
- type: recall_at_10
value: 50.817
- type: recall_at_100
value: 63.544999999999995
- type: recall_at_1000
value: 75.361
- type: recall_at_3
value: 42.269
- type: recall_at_5
value: 46.32
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 64.04799999999999
- type: ap
value: 59.185251455339284
- type: f1
value: 63.947123181349255
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 18.9
- type: map_at_10
value: 29.748
- type: map_at_100
value: 30.976
- type: map_at_1000
value: 31.041
- type: map_at_3
value: 26.112999999999996
- type: map_at_5
value: 28.197
- type: mrr_at_1
value: 19.413
- type: mrr_at_10
value: 30.322
- type: mrr_at_100
value: 31.497000000000003
- type: mrr_at_1000
value: 31.555
- type: mrr_at_3
value: 26.729000000000003
- type: mrr_at_5
value: 28.788999999999998
- type: ndcg_at_1
value: 19.413
- type: ndcg_at_10
value: 36.048
- type: ndcg_at_100
value: 42.152
- type: ndcg_at_1000
value: 43.772
- type: ndcg_at_3
value: 28.642
- type: ndcg_at_5
value: 32.358
- type: precision_at_1
value: 19.413
- type: precision_at_10
value: 5.785
- type: precision_at_100
value: 0.8869999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.192
- type: precision_at_5
value: 9.189
- type: recall_at_1
value: 18.9
- type: recall_at_10
value: 55.457
- type: recall_at_100
value: 84.09100000000001
- type: recall_at_1000
value: 96.482
- type: recall_at_3
value: 35.359
- type: recall_at_5
value: 44.275
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 92.07706338349293
- type: f1
value: 91.56680443236652
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 71.18559051527589
- type: f1
value: 52.42887061726789
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 68.64828513786148
- type: f1
value: 66.54281381596097
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.04236718224612
- type: f1
value: 75.89170458655639
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 32.0840369055247
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 29.448729560244537
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.340856463122375
- type: mrr
value: 32.398547669840916
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 5.526
- type: map_at_10
value: 11.745
- type: map_at_100
value: 14.831
- type: map_at_1000
value: 16.235
- type: map_at_3
value: 8.716
- type: map_at_5
value: 10.101
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 51.06699999999999
- type: mrr_at_100
value: 51.881
- type: mrr_at_1000
value: 51.912000000000006
- type: mrr_at_3
value: 49.02
- type: mrr_at_5
value: 50.288999999999994
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 32.083
- type: ndcg_at_100
value: 30.049999999999997
- type: ndcg_at_1000
value: 38.661
- type: ndcg_at_3
value: 37.940000000000005
- type: ndcg_at_5
value: 35.455999999999996
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 23.437
- type: precision_at_100
value: 7.829999999999999
- type: precision_at_1000
value: 2.053
- type: precision_at_3
value: 35.501
- type: precision_at_5
value: 30.464000000000002
- type: recall_at_1
value: 5.526
- type: recall_at_10
value: 15.445999999999998
- type: recall_at_100
value: 31.179000000000002
- type: recall_at_1000
value: 61.578
- type: recall_at_3
value: 9.71
- type: recall_at_5
value: 12.026
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 23.467
- type: map_at_10
value: 36.041000000000004
- type: map_at_100
value: 37.268
- type: map_at_1000
value: 37.322
- type: map_at_3
value: 32.09
- type: map_at_5
value: 34.414
- type: mrr_at_1
value: 26.738
- type: mrr_at_10
value: 38.665
- type: mrr_at_100
value: 39.64
- type: mrr_at_1000
value: 39.681
- type: mrr_at_3
value: 35.207
- type: mrr_at_5
value: 37.31
- type: ndcg_at_1
value: 26.709
- type: ndcg_at_10
value: 42.942
- type: ndcg_at_100
value: 48.296
- type: ndcg_at_1000
value: 49.651
- type: ndcg_at_3
value: 35.413
- type: ndcg_at_5
value: 39.367999999999995
- type: precision_at_1
value: 26.709
- type: precision_at_10
value: 7.306
- type: precision_at_100
value: 1.0290000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 16.348
- type: precision_at_5
value: 12.068
- type: recall_at_1
value: 23.467
- type: recall_at_10
value: 61.492999999999995
- type: recall_at_100
value: 85.01100000000001
- type: recall_at_1000
value: 95.261
- type: recall_at_3
value: 41.952
- type: recall_at_5
value: 51.105999999999995
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 67.51700000000001
- type: map_at_10
value: 81.054
- type: map_at_100
value: 81.727
- type: map_at_1000
value: 81.75200000000001
- type: map_at_3
value: 78.018
- type: map_at_5
value: 79.879
- type: mrr_at_1
value: 77.52
- type: mrr_at_10
value: 84.429
- type: mrr_at_100
value: 84.58200000000001
- type: mrr_at_1000
value: 84.584
- type: mrr_at_3
value: 83.268
- type: mrr_at_5
value: 84.013
- type: ndcg_at_1
value: 77.53
- type: ndcg_at_10
value: 85.277
- type: ndcg_at_100
value: 86.80499999999999
- type: ndcg_at_1000
value: 87.01
- type: ndcg_at_3
value: 81.975
- type: ndcg_at_5
value: 83.723
- type: precision_at_1
value: 77.53
- type: precision_at_10
value: 12.961
- type: precision_at_100
value: 1.502
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.713
- type: precision_at_5
value: 23.574
- type: recall_at_1
value: 67.51700000000001
- type: recall_at_10
value: 93.486
- type: recall_at_100
value: 98.9
- type: recall_at_1000
value: 99.92999999999999
- type: recall_at_3
value: 84.17999999999999
- type: recall_at_5
value: 88.97500000000001
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 48.225994608749915
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 53.17635557157765
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 3.988
- type: map_at_10
value: 9.4
- type: map_at_100
value: 10.968
- type: map_at_1000
value: 11.257
- type: map_at_3
value: 7.123
- type: map_at_5
value: 8.221
- type: mrr_at_1
value: 19.7
- type: mrr_at_10
value: 29.098000000000003
- type: mrr_at_100
value: 30.247
- type: mrr_at_1000
value: 30.318
- type: mrr_at_3
value: 26.55
- type: mrr_at_5
value: 27.915
- type: ndcg_at_1
value: 19.7
- type: ndcg_at_10
value: 16.176
- type: ndcg_at_100
value: 22.931
- type: ndcg_at_1000
value: 28.301
- type: ndcg_at_3
value: 16.142
- type: ndcg_at_5
value: 13.633999999999999
- type: precision_at_1
value: 19.7
- type: precision_at_10
value: 8.18
- type: precision_at_100
value: 1.8010000000000002
- type: precision_at_1000
value: 0.309
- type: precision_at_3
value: 15.1
- type: precision_at_5
value: 11.74
- type: recall_at_1
value: 3.988
- type: recall_at_10
value: 16.625
- type: recall_at_100
value: 36.61
- type: recall_at_1000
value: 62.805
- type: recall_at_3
value: 9.168
- type: recall_at_5
value: 11.902
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 77.29330379162072
- type: cos_sim_spearman
value: 67.22953551111448
- type: euclidean_pearson
value: 71.44682700059415
- type: euclidean_spearman
value: 66.33178012153247
- type: manhattan_pearson
value: 71.46941734657887
- type: manhattan_spearman
value: 66.43234359835814
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 75.40943196466576
- type: cos_sim_spearman
value: 66.59241013465915
- type: euclidean_pearson
value: 71.32500540796616
- type: euclidean_spearman
value: 67.86667467202591
- type: manhattan_pearson
value: 71.48209832089134
- type: manhattan_spearman
value: 67.94511626964879
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 77.08302398877518
- type: cos_sim_spearman
value: 77.33151317062642
- type: euclidean_pearson
value: 76.77020279715008
- type: euclidean_spearman
value: 77.13893776083225
- type: manhattan_pearson
value: 76.76732290707477
- type: manhattan_spearman
value: 77.14500877396631
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 77.46886184932168
- type: cos_sim_spearman
value: 71.82815265534886
- type: euclidean_pearson
value: 75.19783284299076
- type: euclidean_spearman
value: 71.36479611710412
- type: manhattan_pearson
value: 75.30375233959337
- type: manhattan_spearman
value: 71.46280266488021
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 80.093017609484
- type: cos_sim_spearman
value: 80.65931167868882
- type: euclidean_pearson
value: 80.36786337117047
- type: euclidean_spearman
value: 81.30521389642827
- type: manhattan_pearson
value: 80.37922433220973
- type: manhattan_spearman
value: 81.30496664496285
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 77.98998347238742
- type: cos_sim_spearman
value: 78.91151365939403
- type: euclidean_pearson
value: 76.40510899217841
- type: euclidean_spearman
value: 76.8551459824213
- type: manhattan_pearson
value: 76.3986079603294
- type: manhattan_spearman
value: 76.8848053254288
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 85.63510653472044
- type: cos_sim_spearman
value: 86.98674844768605
- type: euclidean_pearson
value: 85.205080538809
- type: euclidean_spearman
value: 85.53630494151886
- type: manhattan_pearson
value: 85.48612469885626
- type: manhattan_spearman
value: 85.81741413931921
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 66.7257987615171
- type: cos_sim_spearman
value: 67.30387805090024
- type: euclidean_pearson
value: 69.46877227885867
- type: euclidean_spearman
value: 69.33161798704344
- type: manhattan_pearson
value: 69.82773311626424
- type: manhattan_spearman
value: 69.57199940498796
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 79.37322139418472
- type: cos_sim_spearman
value: 77.5887175717799
- type: euclidean_pearson
value: 78.23006410562164
- type: euclidean_spearman
value: 77.18470385673044
- type: manhattan_pearson
value: 78.40868369362455
- type: manhattan_spearman
value: 77.36675823897656
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 77.21233007730808
- type: mrr
value: 93.0502386139641
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 54.567
- type: map_at_10
value: 63.653000000000006
- type: map_at_100
value: 64.282
- type: map_at_1000
value: 64.31099999999999
- type: map_at_3
value: 60.478
- type: map_at_5
value: 62.322
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 64.759
- type: mrr_at_100
value: 65.274
- type: mrr_at_1000
value: 65.301
- type: mrr_at_3
value: 62.333000000000006
- type: mrr_at_5
value: 63.817
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 68.28699999999999
- type: ndcg_at_100
value: 70.98400000000001
- type: ndcg_at_1000
value: 71.695
- type: ndcg_at_3
value: 62.656
- type: ndcg_at_5
value: 65.523
- type: precision_at_1
value: 56.99999999999999
- type: precision_at_10
value: 9.232999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.333000000000002
- type: recall_at_1
value: 54.567
- type: recall_at_10
value: 81.45599999999999
- type: recall_at_100
value: 93.5
- type: recall_at_1000
value: 99.0
- type: recall_at_3
value: 66.228
- type: recall_at_5
value: 73.489
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.74455445544554
- type: cos_sim_ap
value: 92.57836032673468
- type: cos_sim_f1
value: 87.0471464019851
- type: cos_sim_precision
value: 86.4039408866995
- type: cos_sim_recall
value: 87.7
- type: dot_accuracy
value: 99.56039603960396
- type: dot_ap
value: 82.47233353407186
- type: dot_f1
value: 76.78207739307537
- type: dot_precision
value: 78.21576763485477
- type: dot_recall
value: 75.4
- type: euclidean_accuracy
value: 99.73069306930694
- type: euclidean_ap
value: 91.70507666665775
- type: euclidean_f1
value: 86.26262626262626
- type: euclidean_precision
value: 87.14285714285714
- type: euclidean_recall
value: 85.39999999999999
- type: manhattan_accuracy
value: 99.73861386138614
- type: manhattan_ap
value: 91.96809459281754
- type: manhattan_f1
value: 86.6
- type: manhattan_precision
value: 86.6
- type: manhattan_recall
value: 86.6
- type: max_accuracy
value: 99.74455445544554
- type: max_ap
value: 92.57836032673468
- type: max_f1
value: 87.0471464019851
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 60.85593925770172
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 32.356772998237496
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 49.320607035290735
- type: mrr
value: 50.09196481622952
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 31.17573968015504
- type: cos_sim_spearman
value: 30.43371643155132
- type: dot_pearson
value: 30.164319483092744
- type: dot_spearman
value: 29.207082242868754
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.22100000000000003
- type: map_at_10
value: 1.7229999999999999
- type: map_at_100
value: 9.195
- type: map_at_1000
value: 21.999
- type: map_at_3
value: 0.6479999999999999
- type: map_at_5
value: 0.964
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 90.667
- type: mrr_at_100
value: 90.858
- type: mrr_at_1000
value: 90.858
- type: mrr_at_3
value: 90.667
- type: mrr_at_5
value: 90.667
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 72.98
- type: ndcg_at_100
value: 52.868
- type: ndcg_at_1000
value: 46.541
- type: ndcg_at_3
value: 80.39699999999999
- type: ndcg_at_5
value: 76.303
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 75.8
- type: precision_at_100
value: 53.5
- type: precision_at_1000
value: 20.946
- type: precision_at_3
value: 85.333
- type: precision_at_5
value: 79.2
- type: recall_at_1
value: 0.22100000000000003
- type: recall_at_10
value: 1.9109999999999998
- type: recall_at_100
value: 12.437
- type: recall_at_1000
value: 43.606
- type: recall_at_3
value: 0.681
- type: recall_at_5
value: 1.023
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 2.5
- type: map_at_10
value: 9.568999999999999
- type: map_at_100
value: 15.653
- type: map_at_1000
value: 17.188
- type: map_at_3
value: 5.335999999999999
- type: map_at_5
value: 6.522
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 49.184
- type: mrr_at_100
value: 50.512
- type: mrr_at_1000
value: 50.512
- type: mrr_at_3
value: 46.259
- type: mrr_at_5
value: 48.299
- type: ndcg_at_1
value: 30.612000000000002
- type: ndcg_at_10
value: 24.45
- type: ndcg_at_100
value: 35.870999999999995
- type: ndcg_at_1000
value: 47.272999999999996
- type: ndcg_at_3
value: 28.528
- type: ndcg_at_5
value: 25.768
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 21.429000000000002
- type: precision_at_100
value: 7.265000000000001
- type: precision_at_1000
value: 1.504
- type: precision_at_3
value: 29.252
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 2.5
- type: recall_at_10
value: 15.844
- type: recall_at_100
value: 45.469
- type: recall_at_1000
value: 81.148
- type: recall_at_3
value: 6.496
- type: recall_at_5
value: 8.790000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 68.7272
- type: ap
value: 13.156450706152686
- type: f1
value: 52.814703437064395
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 55.6677985285795
- type: f1
value: 55.9373937514999
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 40.05809562275603
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.76807534124099
- type: cos_sim_ap
value: 62.37052608803734
- type: cos_sim_f1
value: 59.077414934916646
- type: cos_sim_precision
value: 52.07326892109501
- type: cos_sim_recall
value: 68.25857519788919
- type: dot_accuracy
value: 80.56267509089825
- type: dot_ap
value: 54.75349561321037
- type: dot_f1
value: 54.75483794372552
- type: dot_precision
value: 49.77336499028707
- type: dot_recall
value: 60.844327176781
- type: euclidean_accuracy
value: 82.476008821601
- type: euclidean_ap
value: 61.17417554210511
- type: euclidean_f1
value: 57.80318696022382
- type: euclidean_precision
value: 53.622207176709544
- type: euclidean_recall
value: 62.69129287598945
- type: manhattan_accuracy
value: 82.48792990403528
- type: manhattan_ap
value: 61.044816292966544
- type: manhattan_f1
value: 58.03033951360462
- type: manhattan_precision
value: 53.36581045172719
- type: manhattan_recall
value: 63.58839050131926
- type: max_accuracy
value: 82.76807534124099
- type: max_ap
value: 62.37052608803734
- type: max_f1
value: 59.077414934916646
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.97881010594946
- type: cos_sim_ap
value: 83.78748636891035
- type: cos_sim_f1
value: 75.94113995691386
- type: cos_sim_precision
value: 72.22029307590805
- type: cos_sim_recall
value: 80.06621496766245
- type: dot_accuracy
value: 85.69294058291614
- type: dot_ap
value: 78.15363722278026
- type: dot_f1
value: 72.08894926888564
- type: dot_precision
value: 67.28959487419075
- type: dot_recall
value: 77.62550046196489
- type: euclidean_accuracy
value: 87.73625179493149
- type: euclidean_ap
value: 83.19012184470559
- type: euclidean_f1
value: 75.5148064623461
- type: euclidean_precision
value: 72.63352535381551
- type: euclidean_recall
value: 78.6341238065907
- type: manhattan_accuracy
value: 87.74013272790779
- type: manhattan_ap
value: 83.23305405113403
- type: manhattan_f1
value: 75.63960775639607
- type: manhattan_precision
value: 72.563304569246
- type: manhattan_recall
value: 78.9882968894364
- type: max_accuracy
value: 87.97881010594946
- type: max_ap
value: 83.78748636891035
- type: max_f1
value: 75.94113995691386
---
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 62398 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit | Muennighoff | 2022-10-03T12:16:09Z | 4,882 | 6 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"gptj",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:04Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: SGPT-5.8B-weightedmean-nli-bitfit
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 74.07462686567165
- type: ap
value: 37.44692407529112
- type: f1
value: 68.28971003916419
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 66.63811563169165
- type: ap
value: 78.57252079915924
- type: f1
value: 64.5543087846584
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 77.21889055472263
- type: ap
value: 25.663426367826712
- type: f1
value: 64.26265688503176
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 58.06209850107067
- type: ap
value: 14.028219107023915
- type: f1
value: 48.10387189660778
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 82.30920000000002
- type: ap
value: 76.88786578621213
- type: f1
value: 82.15455656065011
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 41.584
- type: f1
value: 41.203137944390114
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 35.288000000000004
- type: f1
value: 34.672995558518096
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 38.34
- type: f1
value: 37.608755629529455
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 37.839999999999996
- type: f1
value: 36.86898201563507
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 30.936000000000003
- type: f1
value: 30.49401738527071
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 33.75
- type: f1
value: 33.38338946025617
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 13.727
- type: map_at_10
value: 26.740000000000002
- type: map_at_100
value: 28.218
- type: map_at_1000
value: 28.246
- type: map_at_3
value: 21.728
- type: map_at_5
value: 24.371000000000002
- type: ndcg_at_1
value: 13.727
- type: ndcg_at_10
value: 35.07
- type: ndcg_at_100
value: 41.947
- type: ndcg_at_1000
value: 42.649
- type: ndcg_at_3
value: 24.484
- type: ndcg_at_5
value: 29.282999999999998
- type: precision_at_1
value: 13.727
- type: precision_at_10
value: 6.223
- type: precision_at_100
value: 0.9369999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 10.835
- type: precision_at_5
value: 8.848
- type: recall_at_1
value: 13.727
- type: recall_at_10
value: 62.233000000000004
- type: recall_at_100
value: 93.67
- type: recall_at_1000
value: 99.14699999999999
- type: recall_at_3
value: 32.504
- type: recall_at_5
value: 44.239
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 40.553923271901695
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 32.49323183712211
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 55.89811361443445
- type: mrr
value: 70.16235764850724
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 82.50506557805856
- type: cos_sim_spearman
value: 79.50000423261176
- type: euclidean_pearson
value: 75.76190885392926
- type: euclidean_spearman
value: 76.7330737163434
- type: manhattan_pearson
value: 75.825318036112
- type: manhattan_spearman
value: 76.7415076434559
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 75.49060542797494
- type: f1
value: 75.15379262352123
- type: precision
value: 74.99391092553932
- type: recall
value: 75.49060542797494
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 0.4182258419546555
- type: f1
value: 0.4182258419546555
- type: precision
value: 0.4182258419546555
- type: recall
value: 0.4182258419546555
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 0.013855213023900243
- type: f1
value: 0.0115460108532502
- type: precision
value: 0.010391409767925183
- type: recall
value: 0.013855213023900243
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 0.315955766192733
- type: f1
value: 0.315955766192733
- type: precision
value: 0.315955766192733
- type: recall
value: 0.315955766192733
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 81.74025974025973
- type: f1
value: 81.66568824876
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 33.59451202614059
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 29.128241446157165
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 26.715
- type: map_at_10
value: 35.007
- type: map_at_100
value: 36.352000000000004
- type: map_at_1000
value: 36.51
- type: map_at_3
value: 32.257999999999996
- type: map_at_5
value: 33.595000000000006
- type: ndcg_at_1
value: 33.906
- type: ndcg_at_10
value: 40.353
- type: ndcg_at_100
value: 45.562999999999995
- type: ndcg_at_1000
value: 48.454
- type: ndcg_at_3
value: 36.349
- type: ndcg_at_5
value: 37.856
- type: precision_at_1
value: 33.906
- type: precision_at_10
value: 7.854
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 17.549
- type: precision_at_5
value: 12.561
- type: recall_at_1
value: 26.715
- type: recall_at_10
value: 49.508
- type: recall_at_100
value: 71.76599999999999
- type: recall_at_1000
value: 91.118
- type: recall_at_3
value: 37.356
- type: recall_at_5
value: 41.836
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 19.663
- type: map_at_10
value: 27.086
- type: map_at_100
value: 28.066999999999997
- type: map_at_1000
value: 28.18
- type: map_at_3
value: 24.819
- type: map_at_5
value: 26.332
- type: ndcg_at_1
value: 25.732
- type: ndcg_at_10
value: 31.613999999999997
- type: ndcg_at_100
value: 35.757
- type: ndcg_at_1000
value: 38.21
- type: ndcg_at_3
value: 28.332
- type: ndcg_at_5
value: 30.264000000000003
- type: precision_at_1
value: 25.732
- type: precision_at_10
value: 6.038
- type: precision_at_100
value: 1.034
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 13.864
- type: precision_at_5
value: 10.241999999999999
- type: recall_at_1
value: 19.663
- type: recall_at_10
value: 39.585
- type: recall_at_100
value: 57.718
- type: recall_at_1000
value: 74.26700000000001
- type: recall_at_3
value: 29.845
- type: recall_at_5
value: 35.105
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 30.125
- type: map_at_10
value: 39.824
- type: map_at_100
value: 40.935
- type: map_at_1000
value: 41.019
- type: map_at_3
value: 37.144
- type: map_at_5
value: 38.647999999999996
- type: ndcg_at_1
value: 34.922
- type: ndcg_at_10
value: 45.072
- type: ndcg_at_100
value: 50.046
- type: ndcg_at_1000
value: 51.895
- type: ndcg_at_3
value: 40.251
- type: ndcg_at_5
value: 42.581
- type: precision_at_1
value: 34.922
- type: precision_at_10
value: 7.303999999999999
- type: precision_at_100
value: 1.0739999999999998
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 17.994
- type: precision_at_5
value: 12.475999999999999
- type: recall_at_1
value: 30.125
- type: recall_at_10
value: 57.253
- type: recall_at_100
value: 79.35799999999999
- type: recall_at_1000
value: 92.523
- type: recall_at_3
value: 44.088
- type: recall_at_5
value: 49.893
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.298000000000002
- type: map_at_10
value: 21.479
- type: map_at_100
value: 22.387
- type: map_at_1000
value: 22.483
- type: map_at_3
value: 19.743
- type: map_at_5
value: 20.444000000000003
- type: ndcg_at_1
value: 17.740000000000002
- type: ndcg_at_10
value: 24.887
- type: ndcg_at_100
value: 29.544999999999998
- type: ndcg_at_1000
value: 32.417
- type: ndcg_at_3
value: 21.274
- type: ndcg_at_5
value: 22.399
- type: precision_at_1
value: 17.740000000000002
- type: precision_at_10
value: 3.932
- type: precision_at_100
value: 0.666
- type: precision_at_1000
value: 0.094
- type: precision_at_3
value: 8.927
- type: precision_at_5
value: 6.056
- type: recall_at_1
value: 16.298000000000002
- type: recall_at_10
value: 34.031
- type: recall_at_100
value: 55.769000000000005
- type: recall_at_1000
value: 78.19500000000001
- type: recall_at_3
value: 23.799999999999997
- type: recall_at_5
value: 26.562
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 10.958
- type: map_at_10
value: 16.999
- type: map_at_100
value: 17.979
- type: map_at_1000
value: 18.112000000000002
- type: map_at_3
value: 15.010000000000002
- type: map_at_5
value: 16.256999999999998
- type: ndcg_at_1
value: 14.179
- type: ndcg_at_10
value: 20.985
- type: ndcg_at_100
value: 26.216
- type: ndcg_at_1000
value: 29.675
- type: ndcg_at_3
value: 17.28
- type: ndcg_at_5
value: 19.301
- type: precision_at_1
value: 14.179
- type: precision_at_10
value: 3.968
- type: precision_at_100
value: 0.784
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 8.541
- type: precision_at_5
value: 6.468
- type: recall_at_1
value: 10.958
- type: recall_at_10
value: 29.903000000000002
- type: recall_at_100
value: 53.413
- type: recall_at_1000
value: 78.74799999999999
- type: recall_at_3
value: 19.717000000000002
- type: recall_at_5
value: 24.817
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 21.217
- type: map_at_10
value: 29.677
- type: map_at_100
value: 30.928
- type: map_at_1000
value: 31.063000000000002
- type: map_at_3
value: 26.611
- type: map_at_5
value: 28.463
- type: ndcg_at_1
value: 26.083000000000002
- type: ndcg_at_10
value: 35.217
- type: ndcg_at_100
value: 40.715
- type: ndcg_at_1000
value: 43.559
- type: ndcg_at_3
value: 30.080000000000002
- type: ndcg_at_5
value: 32.701
- type: precision_at_1
value: 26.083000000000002
- type: precision_at_10
value: 6.622
- type: precision_at_100
value: 1.115
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 14.629
- type: precision_at_5
value: 10.837
- type: recall_at_1
value: 21.217
- type: recall_at_10
value: 47.031
- type: recall_at_100
value: 70.378
- type: recall_at_1000
value: 89.704
- type: recall_at_3
value: 32.427
- type: recall_at_5
value: 39.31
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 19.274
- type: map_at_10
value: 26.398
- type: map_at_100
value: 27.711000000000002
- type: map_at_1000
value: 27.833000000000002
- type: map_at_3
value: 24.294
- type: map_at_5
value: 25.385
- type: ndcg_at_1
value: 24.886
- type: ndcg_at_10
value: 30.909
- type: ndcg_at_100
value: 36.941
- type: ndcg_at_1000
value: 39.838
- type: ndcg_at_3
value: 27.455000000000002
- type: ndcg_at_5
value: 28.828
- type: precision_at_1
value: 24.886
- type: precision_at_10
value: 5.6739999999999995
- type: precision_at_100
value: 1.0290000000000001
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 13.242
- type: precision_at_5
value: 9.292
- type: recall_at_1
value: 19.274
- type: recall_at_10
value: 39.643
- type: recall_at_100
value: 66.091
- type: recall_at_1000
value: 86.547
- type: recall_at_3
value: 29.602
- type: recall_at_5
value: 33.561
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.653666666666666
- type: map_at_10
value: 25.606666666666666
- type: map_at_100
value: 26.669333333333334
- type: map_at_1000
value: 26.795833333333334
- type: map_at_3
value: 23.43433333333333
- type: map_at_5
value: 24.609666666666666
- type: ndcg_at_1
value: 22.742083333333333
- type: ndcg_at_10
value: 29.978333333333335
- type: ndcg_at_100
value: 34.89808333333333
- type: ndcg_at_1000
value: 37.806583333333336
- type: ndcg_at_3
value: 26.223666666666674
- type: ndcg_at_5
value: 27.91033333333333
- type: precision_at_1
value: 22.742083333333333
- type: precision_at_10
value: 5.397083333333334
- type: precision_at_100
value: 0.9340000000000002
- type: precision_at_1000
value: 0.13691666666666663
- type: precision_at_3
value: 12.331083333333332
- type: precision_at_5
value: 8.805499999999999
- type: recall_at_1
value: 18.653666666666666
- type: recall_at_10
value: 39.22625000000001
- type: recall_at_100
value: 61.31049999999999
- type: recall_at_1000
value: 82.19058333333334
- type: recall_at_3
value: 28.517333333333333
- type: recall_at_5
value: 32.9565
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.07
- type: map_at_10
value: 21.509
- type: map_at_100
value: 22.335
- type: map_at_1000
value: 22.437
- type: map_at_3
value: 19.717000000000002
- type: map_at_5
value: 20.574
- type: ndcg_at_1
value: 18.865000000000002
- type: ndcg_at_10
value: 25.135999999999996
- type: ndcg_at_100
value: 29.483999999999998
- type: ndcg_at_1000
value: 32.303
- type: ndcg_at_3
value: 21.719
- type: ndcg_at_5
value: 23.039
- type: precision_at_1
value: 18.865000000000002
- type: precision_at_10
value: 4.263999999999999
- type: precision_at_100
value: 0.696
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 9.866999999999999
- type: precision_at_5
value: 6.902
- type: recall_at_1
value: 16.07
- type: recall_at_10
value: 33.661
- type: recall_at_100
value: 54.001999999999995
- type: recall_at_1000
value: 75.564
- type: recall_at_3
value: 23.956
- type: recall_at_5
value: 27.264
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 10.847
- type: map_at_10
value: 15.518
- type: map_at_100
value: 16.384
- type: map_at_1000
value: 16.506
- type: map_at_3
value: 14.093
- type: map_at_5
value: 14.868
- type: ndcg_at_1
value: 13.764999999999999
- type: ndcg_at_10
value: 18.766
- type: ndcg_at_100
value: 23.076
- type: ndcg_at_1000
value: 26.344
- type: ndcg_at_3
value: 16.150000000000002
- type: ndcg_at_5
value: 17.373
- type: precision_at_1
value: 13.764999999999999
- type: precision_at_10
value: 3.572
- type: precision_at_100
value: 0.6779999999999999
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 7.88
- type: precision_at_5
value: 5.712
- type: recall_at_1
value: 10.847
- type: recall_at_10
value: 25.141999999999996
- type: recall_at_100
value: 44.847
- type: recall_at_1000
value: 68.92099999999999
- type: recall_at_3
value: 17.721999999999998
- type: recall_at_5
value: 20.968999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.377
- type: map_at_10
value: 26.005
- type: map_at_100
value: 26.996
- type: map_at_1000
value: 27.116
- type: map_at_3
value: 23.712
- type: map_at_5
value: 24.859
- type: ndcg_at_1
value: 22.201
- type: ndcg_at_10
value: 30.635
- type: ndcg_at_100
value: 35.623
- type: ndcg_at_1000
value: 38.551
- type: ndcg_at_3
value: 26.565
- type: ndcg_at_5
value: 28.28
- type: precision_at_1
value: 22.201
- type: precision_at_10
value: 5.41
- type: precision_at_100
value: 0.88
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 12.531
- type: precision_at_5
value: 8.806
- type: recall_at_1
value: 18.377
- type: recall_at_10
value: 40.908
- type: recall_at_100
value: 63.563
- type: recall_at_1000
value: 84.503
- type: recall_at_3
value: 29.793999999999997
- type: recall_at_5
value: 34.144999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.246
- type: map_at_10
value: 27.528000000000002
- type: map_at_100
value: 28.78
- type: map_at_1000
value: 29.002
- type: map_at_3
value: 25.226
- type: map_at_5
value: 26.355
- type: ndcg_at_1
value: 25.099
- type: ndcg_at_10
value: 32.421
- type: ndcg_at_100
value: 37.2
- type: ndcg_at_1000
value: 40.693
- type: ndcg_at_3
value: 28.768
- type: ndcg_at_5
value: 30.23
- type: precision_at_1
value: 25.099
- type: precision_at_10
value: 6.245
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 13.767999999999999
- type: precision_at_5
value: 9.881
- type: recall_at_1
value: 20.246
- type: recall_at_10
value: 41.336
- type: recall_at_100
value: 63.098
- type: recall_at_1000
value: 86.473
- type: recall_at_3
value: 30.069000000000003
- type: recall_at_5
value: 34.262
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 14.054
- type: map_at_10
value: 20.25
- type: map_at_100
value: 21.178
- type: map_at_1000
value: 21.288999999999998
- type: map_at_3
value: 18.584999999999997
- type: map_at_5
value: 19.536
- type: ndcg_at_1
value: 15.527
- type: ndcg_at_10
value: 23.745
- type: ndcg_at_100
value: 28.610999999999997
- type: ndcg_at_1000
value: 31.740000000000002
- type: ndcg_at_3
value: 20.461
- type: ndcg_at_5
value: 22.072
- type: precision_at_1
value: 15.527
- type: precision_at_10
value: 3.882
- type: precision_at_100
value: 0.6930000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 9.181000000000001
- type: precision_at_5
value: 6.433
- type: recall_at_1
value: 14.054
- type: recall_at_10
value: 32.714
- type: recall_at_100
value: 55.723
- type: recall_at_1000
value: 79.72399999999999
- type: recall_at_3
value: 23.832
- type: recall_at_5
value: 27.754
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 6.122
- type: map_at_10
value: 11.556
- type: map_at_100
value: 12.998000000000001
- type: map_at_1000
value: 13.202
- type: map_at_3
value: 9.657
- type: map_at_5
value: 10.585
- type: ndcg_at_1
value: 15.049000000000001
- type: ndcg_at_10
value: 17.574
- type: ndcg_at_100
value: 24.465999999999998
- type: ndcg_at_1000
value: 28.511999999999997
- type: ndcg_at_3
value: 13.931
- type: ndcg_at_5
value: 15.112
- type: precision_at_1
value: 15.049000000000001
- type: precision_at_10
value: 5.831
- type: precision_at_100
value: 1.322
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 10.749
- type: precision_at_5
value: 8.365
- type: recall_at_1
value: 6.122
- type: recall_at_10
value: 22.207
- type: recall_at_100
value: 47.08
- type: recall_at_1000
value: 70.182
- type: recall_at_3
value: 13.416
- type: recall_at_5
value: 16.672
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 4.672
- type: map_at_10
value: 10.534
- type: map_at_100
value: 14.798
- type: map_at_1000
value: 15.927
- type: map_at_3
value: 7.317
- type: map_at_5
value: 8.726
- type: ndcg_at_1
value: 36.5
- type: ndcg_at_10
value: 26.098
- type: ndcg_at_100
value: 29.215999999999998
- type: ndcg_at_1000
value: 36.254999999999995
- type: ndcg_at_3
value: 29.247
- type: ndcg_at_5
value: 27.692
- type: precision_at_1
value: 47.25
- type: precision_at_10
value: 22.625
- type: precision_at_100
value: 7.042
- type: precision_at_1000
value: 1.6129999999999998
- type: precision_at_3
value: 34.083000000000006
- type: precision_at_5
value: 29.5
- type: recall_at_1
value: 4.672
- type: recall_at_10
value: 15.638
- type: recall_at_100
value: 36.228
- type: recall_at_1000
value: 58.831
- type: recall_at_3
value: 8.578
- type: recall_at_5
value: 11.18
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 49.919999999999995
- type: f1
value: 45.37973678791632
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 25.801000000000002
- type: map_at_10
value: 33.941
- type: map_at_100
value: 34.73
- type: map_at_1000
value: 34.793
- type: map_at_3
value: 31.705
- type: map_at_5
value: 33.047
- type: ndcg_at_1
value: 27.933000000000003
- type: ndcg_at_10
value: 38.644
- type: ndcg_at_100
value: 42.594
- type: ndcg_at_1000
value: 44.352000000000004
- type: ndcg_at_3
value: 34.199
- type: ndcg_at_5
value: 36.573
- type: precision_at_1
value: 27.933000000000003
- type: precision_at_10
value: 5.603000000000001
- type: precision_at_100
value: 0.773
- type: precision_at_1000
value: 0.094
- type: precision_at_3
value: 14.171
- type: precision_at_5
value: 9.786999999999999
- type: recall_at_1
value: 25.801000000000002
- type: recall_at_10
value: 50.876
- type: recall_at_100
value: 69.253
- type: recall_at_1000
value: 82.907
- type: recall_at_3
value: 38.879000000000005
- type: recall_at_5
value: 44.651999999999994
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 9.142
- type: map_at_10
value: 13.841999999999999
- type: map_at_100
value: 14.960999999999999
- type: map_at_1000
value: 15.187000000000001
- type: map_at_3
value: 11.966000000000001
- type: map_at_5
value: 12.921
- type: ndcg_at_1
value: 18.364
- type: ndcg_at_10
value: 18.590999999999998
- type: ndcg_at_100
value: 24.153
- type: ndcg_at_1000
value: 29.104000000000003
- type: ndcg_at_3
value: 16.323
- type: ndcg_at_5
value: 17.000999999999998
- type: precision_at_1
value: 18.364
- type: precision_at_10
value: 5.216
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 10.751
- type: precision_at_5
value: 7.932
- type: recall_at_1
value: 9.142
- type: recall_at_10
value: 22.747
- type: recall_at_100
value: 44.585
- type: recall_at_1000
value: 75.481
- type: recall_at_3
value: 14.602
- type: recall_at_5
value: 17.957
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 18.677
- type: map_at_10
value: 26.616
- type: map_at_100
value: 27.605
- type: map_at_1000
value: 27.711999999999996
- type: map_at_3
value: 24.396
- type: map_at_5
value: 25.627
- type: ndcg_at_1
value: 37.352999999999994
- type: ndcg_at_10
value: 33.995
- type: ndcg_at_100
value: 38.423
- type: ndcg_at_1000
value: 40.947
- type: ndcg_at_3
value: 29.885
- type: ndcg_at_5
value: 31.874999999999996
- type: precision_at_1
value: 37.352999999999994
- type: precision_at_10
value: 7.539999999999999
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.938
- type: precision_at_5
value: 12.943
- type: recall_at_1
value: 18.677
- type: recall_at_10
value: 37.698
- type: recall_at_100
value: 55.354000000000006
- type: recall_at_1000
value: 72.255
- type: recall_at_3
value: 28.406
- type: recall_at_5
value: 32.357
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 74.3292
- type: ap
value: 68.30186110189658
- type: f1
value: 74.20709636944783
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 6.889000000000001
- type: map_at_10
value: 12.321
- type: map_at_100
value: 13.416
- type: map_at_1000
value: 13.525
- type: map_at_3
value: 10.205
- type: map_at_5
value: 11.342
- type: ndcg_at_1
value: 7.092
- type: ndcg_at_10
value: 15.827
- type: ndcg_at_100
value: 21.72
- type: ndcg_at_1000
value: 24.836
- type: ndcg_at_3
value: 11.393
- type: ndcg_at_5
value: 13.462
- type: precision_at_1
value: 7.092
- type: precision_at_10
value: 2.7969999999999997
- type: precision_at_100
value: 0.583
- type: precision_at_1000
value: 0.08499999999999999
- type: precision_at_3
value: 5.019
- type: precision_at_5
value: 4.06
- type: recall_at_1
value: 6.889000000000001
- type: recall_at_10
value: 26.791999999999998
- type: recall_at_100
value: 55.371
- type: recall_at_1000
value: 80.12899999999999
- type: recall_at_3
value: 14.573
- type: recall_at_5
value: 19.557
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 89.6374829001368
- type: f1
value: 89.20878379358307
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 84.54212454212454
- type: f1
value: 82.81080100037023
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 86.46430953969313
- type: f1
value: 86.00019824223267
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 81.31850923896022
- type: f1
value: 81.07860454762863
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 58.23234134098243
- type: f1
value: 56.63845098081841
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 72.28571428571429
- type: f1
value: 70.95796714592039
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 70.68171454628363
- type: f1
value: 52.57188062729139
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 60.521273598196665
- type: f1
value: 42.70492970339204
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 64.32288192128087
- type: f1
value: 45.97360620220273
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 58.67209520826808
- type: f1
value: 42.82844991304579
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 41.95769092864826
- type: f1
value: 28.914127631431263
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 55.28390596745027
- type: f1
value: 38.33899250561289
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 70.00336247478144
- type: f1
value: 68.72041942191649
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.0268997982515
- type: f1
value: 75.29844481506652
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 30.327566856300813
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 28.01650210863619
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.11041256752524
- type: mrr
value: 32.14172939750204
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 3.527
- type: map_at_10
value: 9.283
- type: map_at_100
value: 11.995000000000001
- type: map_at_1000
value: 13.33
- type: map_at_3
value: 6.223
- type: map_at_5
value: 7.68
- type: ndcg_at_1
value: 36.223
- type: ndcg_at_10
value: 28.255999999999997
- type: ndcg_at_100
value: 26.355
- type: ndcg_at_1000
value: 35.536
- type: ndcg_at_3
value: 31.962000000000003
- type: ndcg_at_5
value: 30.61
- type: precision_at_1
value: 37.771
- type: precision_at_10
value: 21.889
- type: precision_at_100
value: 7.1080000000000005
- type: precision_at_1000
value: 1.989
- type: precision_at_3
value: 30.857
- type: precision_at_5
value: 27.307
- type: recall_at_1
value: 3.527
- type: recall_at_10
value: 14.015
- type: recall_at_100
value: 28.402
- type: recall_at_1000
value: 59.795
- type: recall_at_3
value: 7.5969999999999995
- type: recall_at_5
value: 10.641
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 11.631
- type: map_at_10
value: 19.532
- type: map_at_100
value: 20.821
- type: map_at_1000
value: 20.910999999999998
- type: map_at_3
value: 16.597
- type: map_at_5
value: 18.197
- type: ndcg_at_1
value: 13.413
- type: ndcg_at_10
value: 24.628
- type: ndcg_at_100
value: 30.883
- type: ndcg_at_1000
value: 33.216
- type: ndcg_at_3
value: 18.697
- type: ndcg_at_5
value: 21.501
- type: precision_at_1
value: 13.413
- type: precision_at_10
value: 4.571
- type: precision_at_100
value: 0.812
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 8.845
- type: precision_at_5
value: 6.889000000000001
- type: recall_at_1
value: 11.631
- type: recall_at_10
value: 38.429
- type: recall_at_100
value: 67.009
- type: recall_at_1000
value: 84.796
- type: recall_at_3
value: 22.74
- type: recall_at_5
value: 29.266
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 66.64
- type: map_at_10
value: 80.394
- type: map_at_100
value: 81.099
- type: map_at_1000
value: 81.122
- type: map_at_3
value: 77.289
- type: map_at_5
value: 79.25999999999999
- type: ndcg_at_1
value: 76.85
- type: ndcg_at_10
value: 84.68
- type: ndcg_at_100
value: 86.311
- type: ndcg_at_1000
value: 86.49900000000001
- type: ndcg_at_3
value: 81.295
- type: ndcg_at_5
value: 83.199
- type: precision_at_1
value: 76.85
- type: precision_at_10
value: 12.928999999999998
- type: precision_at_100
value: 1.51
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.557
- type: precision_at_5
value: 23.576
- type: recall_at_1
value: 66.64
- type: recall_at_10
value: 93.059
- type: recall_at_100
value: 98.922
- type: recall_at_1000
value: 99.883
- type: recall_at_3
value: 83.49499999999999
- type: recall_at_5
value: 88.729
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 42.17131361041068
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 48.01815621479994
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 3.198
- type: map_at_10
value: 7.550999999999999
- type: map_at_100
value: 9.232
- type: map_at_1000
value: 9.51
- type: map_at_3
value: 5.2940000000000005
- type: map_at_5
value: 6.343999999999999
- type: ndcg_at_1
value: 15.8
- type: ndcg_at_10
value: 13.553999999999998
- type: ndcg_at_100
value: 20.776
- type: ndcg_at_1000
value: 26.204
- type: ndcg_at_3
value: 12.306000000000001
- type: ndcg_at_5
value: 10.952
- type: precision_at_1
value: 15.8
- type: precision_at_10
value: 7.180000000000001
- type: precision_at_100
value: 1.762
- type: precision_at_1000
value: 0.307
- type: precision_at_3
value: 11.333
- type: precision_at_5
value: 9.62
- type: recall_at_1
value: 3.198
- type: recall_at_10
value: 14.575
- type: recall_at_100
value: 35.758
- type: recall_at_1000
value: 62.317
- type: recall_at_3
value: 6.922000000000001
- type: recall_at_5
value: 9.767000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 84.5217161312271
- type: cos_sim_spearman
value: 79.58562467776268
- type: euclidean_pearson
value: 76.69364353942403
- type: euclidean_spearman
value: 74.68959282070473
- type: manhattan_pearson
value: 76.81159265133732
- type: manhattan_spearman
value: 74.7519444048176
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 83.70403706922605
- type: cos_sim_spearman
value: 74.28502198729447
- type: euclidean_pearson
value: 83.32719404608066
- type: euclidean_spearman
value: 75.92189433460788
- type: manhattan_pearson
value: 83.35841543005293
- type: manhattan_spearman
value: 75.94458615451978
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 84.94127878986795
- type: cos_sim_spearman
value: 85.35148434923192
- type: euclidean_pearson
value: 81.71127467071571
- type: euclidean_spearman
value: 82.88240481546771
- type: manhattan_pearson
value: 81.72826221967252
- type: manhattan_spearman
value: 82.90725064625128
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 83.1474704168523
- type: cos_sim_spearman
value: 79.20612995350827
- type: euclidean_pearson
value: 78.85993329596555
- type: euclidean_spearman
value: 78.91956572744715
- type: manhattan_pearson
value: 78.89999720522347
- type: manhattan_spearman
value: 78.93956842550107
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 84.81255514055894
- type: cos_sim_spearman
value: 85.5217140762934
- type: euclidean_pearson
value: 82.15024353784499
- type: euclidean_spearman
value: 83.04155334389833
- type: manhattan_pearson
value: 82.18598945053624
- type: manhattan_spearman
value: 83.07248357693301
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 80.63248465157822
- type: cos_sim_spearman
value: 82.53853238521991
- type: euclidean_pearson
value: 78.33936863828221
- type: euclidean_spearman
value: 79.16305579487414
- type: manhattan_pearson
value: 78.3888359870894
- type: manhattan_spearman
value: 79.18504473136467
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 90.09066290639687
- type: cos_sim_spearman
value: 90.43893699357069
- type: euclidean_pearson
value: 82.39520777222396
- type: euclidean_spearman
value: 81.23948185395952
- type: manhattan_pearson
value: 82.35529784653383
- type: manhattan_spearman
value: 81.12681522483975
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 63.52752323046846
- type: cos_sim_spearman
value: 63.19719780439462
- type: euclidean_pearson
value: 58.29085490641428
- type: euclidean_spearman
value: 58.975178656335046
- type: manhattan_pearson
value: 58.183542772416985
- type: manhattan_spearman
value: 59.190630462178994
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 85.45100366635687
- type: cos_sim_spearman
value: 85.66816193002651
- type: euclidean_pearson
value: 81.87976731329091
- type: euclidean_spearman
value: 82.01382867690964
- type: manhattan_pearson
value: 81.88260155706726
- type: manhattan_spearman
value: 82.05258597906492
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 77.53549990038017
- type: mrr
value: 93.37474163454556
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 31.167
- type: map_at_10
value: 40.778
- type: map_at_100
value: 42.063
- type: map_at_1000
value: 42.103
- type: map_at_3
value: 37.12
- type: map_at_5
value: 39.205
- type: ndcg_at_1
value: 33.667
- type: ndcg_at_10
value: 46.662
- type: ndcg_at_100
value: 51.995999999999995
- type: ndcg_at_1000
value: 53.254999999999995
- type: ndcg_at_3
value: 39.397999999999996
- type: ndcg_at_5
value: 42.934
- type: precision_at_1
value: 33.667
- type: precision_at_10
value: 7.1
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 16.111
- type: precision_at_5
value: 11.600000000000001
- type: recall_at_1
value: 31.167
- type: recall_at_10
value: 63.744
- type: recall_at_100
value: 87.156
- type: recall_at_1000
value: 97.556
- type: recall_at_3
value: 44.0
- type: recall_at_5
value: 52.556000000000004
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.55148514851486
- type: cos_sim_ap
value: 80.535236573428
- type: cos_sim_f1
value: 75.01331912626532
- type: cos_sim_precision
value: 80.27366020524515
- type: cos_sim_recall
value: 70.39999999999999
- type: dot_accuracy
value: 99.04851485148515
- type: dot_ap
value: 28.505358821499726
- type: dot_f1
value: 36.36363636363637
- type: dot_precision
value: 37.160751565762006
- type: dot_recall
value: 35.6
- type: euclidean_accuracy
value: 99.4990099009901
- type: euclidean_ap
value: 74.95819047075476
- type: euclidean_f1
value: 71.15489874110564
- type: euclidean_precision
value: 78.59733978234583
- type: euclidean_recall
value: 65.0
- type: manhattan_accuracy
value: 99.50198019801981
- type: manhattan_ap
value: 75.02070096015086
- type: manhattan_f1
value: 71.20535714285712
- type: manhattan_precision
value: 80.55555555555556
- type: manhattan_recall
value: 63.800000000000004
- type: max_accuracy
value: 99.55148514851486
- type: max_ap
value: 80.535236573428
- type: max_f1
value: 75.01331912626532
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 54.13314692311623
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 31.115181648287145
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 44.771112666694336
- type: mrr
value: 45.30415764790765
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 30.849429597669374
- type: cos_sim_spearman
value: 30.384175038360194
- type: dot_pearson
value: 29.030383429536823
- type: dot_spearman
value: 28.03273624951732
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.19499999999999998
- type: map_at_10
value: 1.0959999999999999
- type: map_at_100
value: 5.726
- type: map_at_1000
value: 13.611999999999998
- type: map_at_3
value: 0.45399999999999996
- type: map_at_5
value: 0.67
- type: ndcg_at_1
value: 71.0
- type: ndcg_at_10
value: 55.352999999999994
- type: ndcg_at_100
value: 40.797
- type: ndcg_at_1000
value: 35.955999999999996
- type: ndcg_at_3
value: 63.263000000000005
- type: ndcg_at_5
value: 60.14000000000001
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 56.99999999999999
- type: precision_at_100
value: 41.199999999999996
- type: precision_at_1000
value: 16.154
- type: precision_at_3
value: 66.667
- type: precision_at_5
value: 62.8
- type: recall_at_1
value: 0.19499999999999998
- type: recall_at_10
value: 1.3639999999999999
- type: recall_at_100
value: 9.317
- type: recall_at_1000
value: 33.629999999999995
- type: recall_at_3
value: 0.49300000000000005
- type: recall_at_5
value: 0.756
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 1.335
- type: map_at_10
value: 6.293
- type: map_at_100
value: 10.928
- type: map_at_1000
value: 12.359
- type: map_at_3
value: 3.472
- type: map_at_5
value: 4.935
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 16.178
- type: ndcg_at_100
value: 28.149
- type: ndcg_at_1000
value: 39.845000000000006
- type: ndcg_at_3
value: 19.171
- type: ndcg_at_5
value: 17.864
- type: precision_at_1
value: 20.408
- type: precision_at_10
value: 14.49
- type: precision_at_100
value: 6.306000000000001
- type: precision_at_1000
value: 1.3860000000000001
- type: precision_at_3
value: 21.088
- type: precision_at_5
value: 18.367
- type: recall_at_1
value: 1.335
- type: recall_at_10
value: 10.825999999999999
- type: recall_at_100
value: 39.251000000000005
- type: recall_at_1000
value: 74.952
- type: recall_at_3
value: 4.9110000000000005
- type: recall_at_5
value: 7.312
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 69.93339999999999
- type: ap
value: 13.87476602492533
- type: f1
value: 53.867357615848555
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 62.43916242218449
- type: f1
value: 62.870386304954685
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 37.202082549859796
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.65023544137807
- type: cos_sim_ap
value: 65.99787692764193
- type: cos_sim_f1
value: 62.10650887573965
- type: cos_sim_precision
value: 56.30901287553648
- type: cos_sim_recall
value: 69.23482849604221
- type: dot_accuracy
value: 79.10830303391549
- type: dot_ap
value: 48.80109642320246
- type: dot_f1
value: 51.418744625967314
- type: dot_precision
value: 40.30253107683091
- type: dot_recall
value: 71.00263852242745
- type: euclidean_accuracy
value: 82.45812719794957
- type: euclidean_ap
value: 60.09969493259607
- type: euclidean_f1
value: 57.658573789246226
- type: euclidean_precision
value: 55.62913907284768
- type: euclidean_recall
value: 59.84168865435356
- type: manhattan_accuracy
value: 82.46408773916671
- type: manhattan_ap
value: 60.116199786815116
- type: manhattan_f1
value: 57.683903860160235
- type: manhattan_precision
value: 53.41726618705036
- type: manhattan_recall
value: 62.69129287598945
- type: max_accuracy
value: 83.65023544137807
- type: max_ap
value: 65.99787692764193
- type: max_f1
value: 62.10650887573965
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.34943920518494
- type: cos_sim_ap
value: 84.5428891020442
- type: cos_sim_f1
value: 77.09709933923172
- type: cos_sim_precision
value: 74.83150952967607
- type: cos_sim_recall
value: 79.50415768401602
- type: dot_accuracy
value: 84.53448208949432
- type: dot_ap
value: 73.96328242371995
- type: dot_f1
value: 70.00553786515299
- type: dot_precision
value: 63.58777665995976
- type: dot_recall
value: 77.86418232214352
- type: euclidean_accuracy
value: 86.87662514068381
- type: euclidean_ap
value: 81.45499631520235
- type: euclidean_f1
value: 73.46567109816063
- type: euclidean_precision
value: 69.71037533697381
- type: euclidean_recall
value: 77.6485987064983
- type: manhattan_accuracy
value: 86.88244654014825
- type: manhattan_ap
value: 81.47180273946366
- type: manhattan_f1
value: 73.44624393136418
- type: manhattan_precision
value: 70.80385852090032
- type: manhattan_recall
value: 76.29350169387126
- type: max_accuracy
value: 88.34943920518494
- type: max_ap
value: 84.5428891020442
- type: max_f1
value: 77.09709933923172
---
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 249592 with parameters:
```
{'batch_size': 2, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTJModel
(1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
timm/ViT-B-16-SigLIP-512 | timm | 2023-10-25T21:55:44Z | 4,876 | 5 | open_clip | [
"open_clip",
"safetensors",
"clip",
"siglip",
"zero-shot-image-classification",
"dataset:webli",
"arxiv:2303.15343",
"license:apache-2.0",
"region:us"
] | zero-shot-image-classification | 2023-10-16T23:21:38Z | ---
tags:
- clip
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- webli
---
# Model card for ViT-B-16-SigLIP-512
A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
## Model Details
- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
- **Original:** https://github.com/google-research/big_vision
- **Dataset:** WebLI
- **Papers:**
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
## Model Usage
### With OpenCLIP
```
import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-512')
tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP-512')
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)
labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features = F.normalize(image_features, dim=-1)
text_features = F.normalize(text_features, dim=-1)
text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)
```
### With `timm` (for image embeddings)
```python
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_siglip_512',
pretrained=True,
num_classes=0,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
```
```bibtex
@misc{big_vision,
author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
title = {Big Vision},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/big_vision}}
}
```
|
mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF | mradermacher | 2024-06-26T20:51:17Z | 4,876 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:saishf/Long-Neural-SOVLish-Devil-8B-L3-262K",
"endpoints_compatible",
"region:us"
] | null | 2024-06-03T06:26:58Z | ---
base_model: saishf/Long-Neural-SOVLish-Devil-8B-L3-262K
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/saishf/Long-Neural-SOVLish-Devil-8B-L3-262K
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Long-Neural-SOVLish-Devil-8B-L3-262K-i1-GGUF/resolve/main/Long-Neural-SOVLish-Devil-8B-L3-262K.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
Biokukesi/llama-3-8b-Instruct-bnb-4bit-aiaustin-demo | Biokukesi | 2024-06-23T12:46:31Z | 4,871 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-23T12:36:41Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** Biokukesi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
timm/deit3_small_patch16_224.fb_in22k_ft_in1k | timm | 2024-02-10T23:37:11Z | 4,868 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-22k",
"arxiv:2204.07118",
"license:apache-2.0",
"region:us"
] | image-classification | 2023-03-28T01:26:49Z | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for deit3_small_patch16_224.fb_in22k_ft_in1k
A DeiT-III image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 22.1
- GMACs: 4.6
- Activations (M): 11.9
- Image size: 224 x 224
- **Papers:**
- DeiT III: Revenge of the ViT: https://arxiv.org/abs/2204.07118
- **Original:** https://github.com/facebookresearch/deit
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-22k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('deit3_small_patch16_224.fb_in22k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'deit3_small_patch16_224.fb_in22k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 384) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{Touvron2022DeiTIR,
title={DeiT III: Revenge of the ViT},
author={Hugo Touvron and Matthieu Cord and Herve Jegou},
journal={arXiv preprint arXiv:2204.07118},
year={2022},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
VAGOsolutions/SauerkrautLM-Gemma-7b | VAGOsolutions | 2024-03-02T02:37:49Z | 4,866 | 13 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"sft",
"dpo",
"laserRMT",
"laser-QLoRa",
"finetune",
"work in progress",
"alpha",
"de",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-27T22:32:12Z | ---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
language:
- de
- en
tags:
- sft
- dpo
- laserRMT
- laser-QLoRa
- finetune
- work in progress
- alpha
---
**Update**
- 01.03.2024 - Reuploaded the model in bfloat16 dtype.
- 02.03.2024 - **strongest Gemma finetune model so far: added AGIEval,GPT4ALL and Bigbench scoring with AVG of 54.13 and H6 Open LLM Leaderboard with AVG of 67.83**
## VAGO solutions SauerkrautLM-Gemma-7b (alpha)
Introducing **SauerkrautLM-Gemma-7b** – our Sauerkraut version of the powerful [google/gemma-7b](https://huggingface.co/google/gemma-7b) !
**It is an early stage finetuned model and should be used with caution!**
The model **SauerkrautLM-Gemma-7b** is a **joint effort** between **VAGO solutions** and **Hyperspace.ai.**
Much appreciation goes to the tremendous research effort of **Fernando Fernandes Neto, David Golchinfar and Eric Hartford on their laserRMT approach.**
Without their independent research collaboration this model release would not have been possible.
- Fintuned with **SFT**
- Aligned with **DPO**
- **Using a novel training technique: laser-QLoRA** - we partially freeze the model according to a laser-like analysis (Official Paper soon). It allows to evaluate the no free lunch theorem and supports better decision making when optimizing the theorem - created by the [LaserRMT research group](https://github.com/cognitivecomputations/laserRMT)
- Optimized with **LaserRMT**
# Table of Contents
1. [Overview of all SauerkrautLM-Gemma-7b models](#all-sauerkrautlm-gemma-7b-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training procedure](#proceed-of-the-training)
3. [Evaluation](#evaluation)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All SauerkrautLM-Gemma-7b Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-Gemma-7b | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-7b) | coming soon | coming soon | coming soon |
## Model Details
**SauerkrautLM-Gemma-7b**
- **Model Type:** SauerkrautLM-Gemma-7b is a finetuned Model based on [google/gemma-7b](https://huggingface.co/google/gemma-7b)
- **Language(s):** German, English
- **License:** [gemma-terms-of-use](https://ai.google.dev/gemma/terms)
- **Contact:** [VAGO solutions](https://vago-solutions.ai), [Hyperspace.ai](https://hyperspace.computer/)
### Training procedure:
**Warning**: **This finetuned model is in an early stage and we sometimes observed strange behavior. It is still work in progress!**
Anyone who has attempted or succeeded in fine-tuning a model is aware of the difficulty in nudging it towards a specific skill, such as mastering new languages, as well as the challenges associated with achieving significant improvements in performance.
Experimenting with a novel training strategy and Spherical Linear Interpolation alongside a lasered version of the model itself has proven to be both fascinating and revealing.
Furthermore, we developed one iteration of the model using our entire SFT -Sauerkraut dataset and two additional iterations using subsets of the full dataset—one focused on enhancing MMLU and TQA capabilities, and the other on boosting GSM8K and Winogrande skills.
After optimizing our primary SFT model, we applied a similar strategy to our new DPO Dataset, dividing it into further subsets. We trained one model on the entire dataset again and two more on these specialized subsets.
We actively monitor and assesed the results of each training. Whenever we found a decrease in perplexity on the gsm8k benchmark we intervined. By following this procedure we were able to improve the overall performance, especially in math abilities, without detracting from performance on other benchmarks—a task that is, in general, quite difficult.
This process not only helps in understanding the effectiveness of Spherical Linear Interpolation but also introduces a new method for refining models with enhanced skills through a cycle of targeted data selection (Laser data(x)) + SLERP, followed by a subsequent focus on different data (Laser again on data(y)).
Additionally, we integrated a novel training strategy on the SFT and DPO training process, where we partially freeze the model according to a laser-like analysis aiming to navigate and optimize the trade-offs highlighted by the no free lunch theorem. This innovative training method effectively prevents the significant problem of language models forgetting previously acquired knowledge.
This aspect is particularly crucial when attempting to teach the model specific skills, such as a new language, where in general, the model might lose a considerable amount of its prior knowledge and exhibit a decline in overall intelligence.
Detailed information on how the new training strategy works and the advantages it offers over conventional training methods will soon be published in a detailed paper by the LaserRMT research group.
**We teached German language skills on this model.** As far as we know, it is the first Gemma model with bilingual skills in German and English. Nevertheless, formulations may occur that are not entirely correct (still work in progress).
### Prompt Template:
We trained on vicuna prompt template. Please add the following stopping string to your client: ``` "</s>","</p>" ``` (we did not add the special tokens to the training config)
```
You are a helpful AI Assistant.
USER: Hello, how are you?
ASSISTANT:
```
## Evaluation
**Open LLM Leaderboard:**
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | **67.83** |
| ARC (25-shot) | 59.98 |
| HellaSwag (10-shot) | 81.91 |
| MMLU (5-shot) | 63.76|
| TruthfulQA (0-shot) | 61 |
| Winogrande (5-shot) | 76.64 |
| GSM8K (5-shot) | 63.68 |
**Performance**
| Model |AGIEval|GPT4All|TruthfulQA|BigBench|Average ⬇️|
|-----------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[VAGOsolutions/SauerkrautLM-Gemma-7b](https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-7b) | 37.5| 72.46| 61.24| 45.33| 54.13|
|[zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | 37.52| 71.77| 55.26| 39.77| 51.08|
|[zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)| 34.22| 66.37| 52.19| 37.10| 47.47|
|[google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 21.33| 40.84| 41.70| 30.25| 33.53|
<details><summary>Details of AGIEval, GPT4All, TruthfulQA, BigBench </summary>
**AGIEval**
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|------------------------------|------:|------|------|--------|-----:|---|-----:|
|agieval_sat_math | 1|none |None |acc |0.3682|± |0.0326|
| | |none |None |acc_norm|0.3364|± |0.0319|
|agieval_sat_en_without_passage| 1|none |None |acc |0.4272|± |0.0345|
| | |none |None |acc_norm|0.3738|± |0.0338|
|agieval_sat_en | 1|none |None |acc |0.7427|± |0.0305|
| | |none |None |acc_norm|0.6893|± |0.0323|
|agieval_lsat_rc | 1|none |None |acc |0.5539|± |0.0304|
| | |none |None |acc_norm|0.5167|± |0.0305|
|agieval_lsat_lr | 1|none |None |acc |0.3431|± |0.0210|
| | |none |None |acc_norm|0.3471|± |0.0211|
|agieval_lsat_ar | 1|none |None |acc |0.1913|± |0.0260|
| | |none |None |acc_norm|0.1739|± |0.0250|
|agieval_logiqa_en | 1|none |None |acc |0.3303|± |0.0184|
| | |none |None |acc_norm|0.3303|± |0.0184|
|agieval_aqua_rat | 1|none |None |acc |0.2480|± |0.0272|
| | |none |None |acc_norm|0.2323|± |0.0265|
Average: 37.5%
**GPT4All**
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------|------:|------|------|--------|-----:|---|-----:|
|arc_challenge| 1|none |None |acc |0.5358|± |0.0146|
| | |none |None |acc_norm|0.5597|± |0.0145|
|arc_easy | 1|none |None |acc |0.8249|± |0.0078|
| | |none |None |acc_norm|0.7955|± |0.0083|
|boolq | 2|none |None |acc |0.8651|± |0.006 |
|hellaswag | 1|none |None |acc |0.6162|± |0.0049|
| | |none |None |acc_norm|0.8117|± |0.0039|
|openbookqa | 1|none |None |acc |0.336|± |0.0211|
| | |none |None |acc_norm|0.470|± |0.0223|
|piqa | 1|none |None |acc |0.7900|± |0.0095|
| | |none |None |acc_norm|0.8096|± |0.00 |
|winogrande | 1|none |None |acc |0.7609|± |0.012 |
Average: 72.46%
**TruthfulQA**
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|--------------|------:|------|-----:|------|-----:|---|-----:|
|truthfulqa_mc2| 2|none | 0|acc |0.6124|± |0.0148|
Average: 61.24%
**Bigbench**
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|----------------------------------------------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|bbh_zeroshot_tracking_shuffled_objects_three_objects| 2|flexible-extract| 0|exact_match|0.2760|± |0.0283|
|bbh_zeroshot_tracking_shuffled_objects_seven_objects| 2|flexible-extract| 0|exact_match|0.1280|± |0.0212|
|bbh_zeroshot_tracking_shuffled_objects_five_objects | 2|flexible-extract| 0|exact_match|0.1240|± |0.0209|
|bbh_zeroshot_temporal_sequences | 2|flexible-extract| 0|exact_match|0.4520|± |0.0315|
|bbh_zeroshot_sports_understanding | 2|flexible-extract| 0|exact_match|0.7120|± |0.0287|
|bbh_zeroshot_snarks | 2|flexible-extract| 0|exact_match|0.5056|± |0.0376|
|bbh_zeroshot_salient_translation_error_detection | 2|flexible-extract| 0|exact_match|0.4480|± |0.0315|
|bbh_zeroshot_ruin_names | 2|flexible-extract| 0|exact_match|0.4520|± |0.0315|
|bbh_zeroshot_reasoning_about_colored_objects | 2|flexible-extract| 0|exact_match|0.4800|± |0.0317|
|bbh_zeroshot_navigate | 2|flexible-extract| 0|exact_match|0.5480|± |0.0315|
|bbh_zeroshot_movie_recommendation | 2|flexible-extract| 0|exact_match|0.7000|± |0.0290|
|bbh_zeroshot_logical_deduction_three_objects | 2|flexible-extract| 0|exact_match|0.5200|± |0.0317|
|bbh_zeroshot_logical_deduction_seven_objects | 2|flexible-extract| 0|exact_match|0.4120|± |0.0312|
|bbh_zeroshot_logical_deduction_five_objects | 2|flexible-extract| 0|exact_match|0.3840|± |0.0308|
|bbh_zeroshot_geometric_shapes | 2|flexible-extract| 0|exact_match|0.2920|± |0.0288|
|bbh_zeroshot_disambiguation_qa | 2|flexible-extract| 0|exact_match|0.6480|± |0.0303|
|bbh_zeroshot_date_understanding | 2|flexible-extract| 0|exact_match|0.5000|± |0.0317|
|bbh_zeroshot_causal_judgement | 2|flexible-extract| 0|exact_match|0.5775|± |0.0362|
Average: 45.33%
</details>
Despite the fact that we archived great results on the Open LLM leaderboard benchmarks the model subjectively does not feel as smart as comparable mistral finetunes. Most of its answers are coherent but we observed that the model sometimes answers realy lazy or odd.
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our websites. We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startups, VAGO solutions and Hyperspace where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us at [VAGO solutions](https://vago-solutions.de/#Kontakt), [Hyperspace.computer](https://hyperspace.computer/)
## Acknowledgement
Many thanks to [google](https://huggingface.co/google) for providing such valuable model to the Open-Source community |
gavulsim/distilbert_finetuned_yahoo_answers_topics | gavulsim | 2023-10-27T09:20:49Z | 4,864 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:yahoo_answers_topics",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-10-27T08:19:50Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- yahoo_answers_topics
metrics:
- accuracy
model-index:
- name: deberta_finetuned_yahoo_answers_topics
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yahoo_answers_topics
type: yahoo_answers_topics
config: yahoo_answers_topics
split: test
args: yahoo_answers_topics
metrics:
- name: Accuracy
type: accuracy
value: 0.71195
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta_finetuned_yahoo_answers_topics
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yahoo_answers_topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9096
- Accuracy: 0.7119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1025 | 0.03 | 5000 | 1.0702 | 0.6717 |
| 1.0132 | 0.06 | 10000 | 0.9976 | 0.6834 |
| 0.8688 | 0.09 | 15000 | 0.9770 | 0.6961 |
| 0.9964 | 0.11 | 20000 | 0.9356 | 0.7020 |
| 0.9338 | 0.14 | 25000 | 0.9259 | 0.7090 |
| 0.9059 | 0.17 | 30000 | 0.9096 | 0.7119 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
w601sxs/b1ade-embed-kd | w601sxs | 2024-05-28T18:31:24Z | 4,864 | 1 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"mteb",
"sentence-similarity",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-05-24T20:58:10Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- mteb
model-index:
- name: b1ade_embed_kd
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification
config: default
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.81709145427287
- type: ap
value: 23.581082591688467
- type: f1
value: 62.54637626017967
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 80.300125
- type: ap
value: 74.26836190039964
- type: f1
value: 80.2158066692679
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification
config: default
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.084
- type: f1
value: 42.66774553366831
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 29.232000000000003
- type: map_at_10
value: 45.777
- type: map_at_100
value: 46.634
- type: map_at_1000
value: 46.64
- type: map_at_20
value: 46.489000000000004
- type: map_at_3
value: 40.861
- type: map_at_5
value: 43.659
- type: mrr_at_1
value: 30.156
- type: mrr_at_10
value: 46.141
- type: mrr_at_100
value: 46.983999999999995
- type: mrr_at_1000
value: 46.989999999999995
- type: mrr_at_20
value: 46.839
- type: mrr_at_3
value: 41.157
- type: mrr_at_5
value: 44.013000000000005
- type: ndcg_at_1
value: 29.232000000000003
- type: ndcg_at_10
value: 54.832
- type: ndcg_at_100
value: 58.303000000000004
- type: ndcg_at_1000
value: 58.451
- type: ndcg_at_20
value: 57.328
- type: ndcg_at_3
value: 44.685
- type: ndcg_at_5
value: 49.756
- type: precision_at_1
value: 29.232000000000003
- type: precision_at_10
value: 8.371
- type: precision_at_100
value: 0.985
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.6690000000000005
- type: precision_at_3
value: 18.587
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 29.232000000000003
- type: recall_at_10
value: 83.71300000000001
- type: recall_at_100
value: 98.506
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 93.38499999999999
- type: recall_at_3
value: 55.761
- type: recall_at_5
value: 68.137
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.801946024895756
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 37.639210206045206
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.589359041891576
- type: mrr
value: 70.88334872268389
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.63594177060354
- type: cos_sim_spearman
value: 84.75132870687939
- type: euclidean_pearson
value: 85.05646621990854
- type: euclidean_spearman
value: 84.68686940680522
- type: manhattan_pearson
value: 85.08705700579426
- type: manhattan_spearman
value: 84.83446313127413
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.1948051948052
- type: f1
value: 85.13229898343104
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.68616898014911
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.45376891835619
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 26.340000000000003
- type: map_at_10
value: 36.513
- type: map_at_100
value: 37.968
- type: map_at_1000
value: 38.107
- type: map_at_20
value: 37.355
- type: map_at_3
value: 33.153
- type: map_at_5
value: 34.899
- type: mrr_at_1
value: 33.763
- type: mrr_at_10
value: 42.778
- type: mrr_at_100
value: 43.667
- type: mrr_at_1000
value: 43.724000000000004
- type: mrr_at_20
value: 43.349
- type: mrr_at_3
value: 40.32
- type: mrr_at_5
value: 41.657
- type: ndcg_at_1
value: 33.763
- type: ndcg_at_10
value: 42.783
- type: ndcg_at_100
value: 48.209999999999994
- type: ndcg_at_1000
value: 50.678999999999995
- type: ndcg_at_20
value: 45.073
- type: ndcg_at_3
value: 37.841
- type: ndcg_at_5
value: 39.818999999999996
- type: precision_at_1
value: 33.763
- type: precision_at_10
value: 8.398
- type: precision_at_100
value: 1.396
- type: precision_at_1000
value: 0.188
- type: precision_at_20
value: 5.0569999999999995
- type: precision_at_3
value: 18.503
- type: precision_at_5
value: 13.219
- type: recall_at_1
value: 26.340000000000003
- type: recall_at_10
value: 54.603
- type: recall_at_100
value: 77.264
- type: recall_at_1000
value: 93.882
- type: recall_at_20
value: 63.101
- type: recall_at_3
value: 39.6
- type: recall_at_5
value: 45.651
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 24.313000000000002
- type: map_at_10
value: 33.225
- type: map_at_100
value: 34.293
- type: map_at_1000
value: 34.421
- type: map_at_20
value: 33.818
- type: map_at_3
value: 30.545
- type: map_at_5
value: 32.144
- type: mrr_at_1
value: 31.083
- type: mrr_at_10
value: 39.199
- type: mrr_at_100
value: 39.835
- type: mrr_at_1000
value: 39.892
- type: mrr_at_20
value: 39.57
- type: mrr_at_3
value: 36.879
- type: mrr_at_5
value: 38.245000000000005
- type: ndcg_at_1
value: 31.083
- type: ndcg_at_10
value: 38.553
- type: ndcg_at_100
value: 42.685
- type: ndcg_at_1000
value: 45.144
- type: ndcg_at_20
value: 40.116
- type: ndcg_at_3
value: 34.608
- type: ndcg_at_5
value: 36.551
- type: precision_at_1
value: 31.083
- type: precision_at_10
value: 7.28
- type: precision_at_100
value: 1.183
- type: precision_at_1000
value: 0.168
- type: precision_at_20
value: 4.322
- type: precision_at_3
value: 16.858
- type: precision_at_5
value: 12.127
- type: recall_at_1
value: 24.313000000000002
- type: recall_at_10
value: 48.117
- type: recall_at_100
value: 65.768
- type: recall_at_1000
value: 81.935
- type: recall_at_20
value: 53.689
- type: recall_at_3
value: 36.335
- type: recall_at_5
value: 41.803000000000004
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 33.013999999999996
- type: map_at_10
value: 44.567
- type: map_at_100
value: 45.664
- type: map_at_1000
value: 45.732
- type: map_at_20
value: 45.190000000000005
- type: map_at_3
value: 41.393
- type: map_at_5
value: 43.147000000000006
- type: mrr_at_1
value: 37.806
- type: mrr_at_10
value: 47.841
- type: mrr_at_100
value: 48.597
- type: mrr_at_1000
value: 48.638
- type: mrr_at_20
value: 48.262
- type: mrr_at_3
value: 45.361000000000004
- type: mrr_at_5
value: 46.803
- type: ndcg_at_1
value: 37.806
- type: ndcg_at_10
value: 50.412
- type: ndcg_at_100
value: 55.019
- type: ndcg_at_1000
value: 56.52
- type: ndcg_at_20
value: 52.23100000000001
- type: ndcg_at_3
value: 44.944
- type: ndcg_at_5
value: 47.535
- type: precision_at_1
value: 37.806
- type: precision_at_10
value: 8.351
- type: precision_at_100
value: 1.163
- type: precision_at_1000
value: 0.134
- type: precision_at_20
value: 4.727
- type: precision_at_3
value: 20.376
- type: precision_at_5
value: 14.056
- type: recall_at_1
value: 33.013999999999996
- type: recall_at_10
value: 64.35600000000001
- type: recall_at_100
value: 84.748
- type: recall_at_1000
value: 95.525
- type: recall_at_20
value: 71.137
- type: recall_at_3
value: 49.726
- type: recall_at_5
value: 56.054
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 18.476
- type: map_at_10
value: 24.715999999999998
- type: map_at_100
value: 25.72
- type: map_at_1000
value: 25.826999999999998
- type: map_at_20
value: 25.276
- type: map_at_3
value: 22.656000000000002
- type: map_at_5
value: 23.737
- type: mrr_at_1
value: 20.113
- type: mrr_at_10
value: 26.423999999999996
- type: mrr_at_100
value: 27.328000000000003
- type: mrr_at_1000
value: 27.418
- type: mrr_at_20
value: 26.936
- type: mrr_at_3
value: 24.275
- type: mrr_at_5
value: 25.501
- type: ndcg_at_1
value: 20.113
- type: ndcg_at_10
value: 28.626
- type: ndcg_at_100
value: 33.649
- type: ndcg_at_1000
value: 36.472
- type: ndcg_at_20
value: 30.581999999999997
- type: ndcg_at_3
value: 24.490000000000002
- type: ndcg_at_5
value: 26.394000000000002
- type: precision_at_1
value: 20.113
- type: precision_at_10
value: 4.52
- type: precision_at_100
value: 0.739
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_20
value: 2.706
- type: precision_at_3
value: 10.433
- type: precision_at_5
value: 7.48
- type: recall_at_1
value: 18.476
- type: recall_at_10
value: 39.129000000000005
- type: recall_at_100
value: 62.44
- type: recall_at_1000
value: 83.95700000000001
- type: recall_at_20
value: 46.611999999999995
- type: recall_at_3
value: 27.772000000000002
- type: recall_at_5
value: 32.312000000000005
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 10.126
- type: map_at_10
value: 15.916
- type: map_at_100
value: 17.049
- type: map_at_1000
value: 17.19
- type: map_at_20
value: 16.569
- type: map_at_3
value: 13.986
- type: map_at_5
value: 15.052999999999999
- type: mrr_at_1
value: 13.059999999999999
- type: mrr_at_10
value: 19.52
- type: mrr_at_100
value: 20.599999999999998
- type: mrr_at_1000
value: 20.693
- type: mrr_at_20
value: 20.177999999999997
- type: mrr_at_3
value: 17.496000000000002
- type: mrr_at_5
value: 18.541
- type: ndcg_at_1
value: 13.059999999999999
- type: ndcg_at_10
value: 19.987
- type: ndcg_at_100
value: 25.602000000000004
- type: ndcg_at_1000
value: 29.171999999999997
- type: ndcg_at_20
value: 22.31
- type: ndcg_at_3
value: 16.286
- type: ndcg_at_5
value: 17.931
- type: precision_at_1
value: 13.059999999999999
- type: precision_at_10
value: 3.9050000000000002
- type: precision_at_100
value: 0.771
- type: precision_at_1000
value: 0.123
- type: precision_at_20
value: 2.606
- type: precision_at_3
value: 8.167
- type: precision_at_5
value: 6.045
- type: recall_at_1
value: 10.126
- type: recall_at_10
value: 29.137
- type: recall_at_100
value: 53.824000000000005
- type: recall_at_1000
value: 79.373
- type: recall_at_20
value: 37.475
- type: recall_at_3
value: 18.791
- type: recall_at_5
value: 22.993
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 25.281
- type: map_at_10
value: 34.875
- type: map_at_100
value: 36.268
- type: map_at_1000
value: 36.385
- type: map_at_20
value: 35.711999999999996
- type: map_at_3
value: 31.808999999999997
- type: map_at_5
value: 33.550999999999995
- type: mrr_at_1
value: 31.28
- type: mrr_at_10
value: 40.489000000000004
- type: mrr_at_100
value: 41.434
- type: mrr_at_1000
value: 41.491
- type: mrr_at_20
value: 41.088
- type: mrr_at_3
value: 38.033
- type: mrr_at_5
value: 39.621
- type: ndcg_at_1
value: 31.28
- type: ndcg_at_10
value: 40.716
- type: ndcg_at_100
value: 46.45
- type: ndcg_at_1000
value: 48.851
- type: ndcg_at_20
value: 43.216
- type: ndcg_at_3
value: 35.845
- type: ndcg_at_5
value: 38.251000000000005
- type: precision_at_1
value: 31.28
- type: precision_at_10
value: 7.623
- type: precision_at_100
value: 1.214
- type: precision_at_1000
value: 0.159
- type: precision_at_20
value: 4.625
- type: precision_at_3
value: 17.26
- type: precision_at_5
value: 12.435
- type: recall_at_1
value: 25.281
- type: recall_at_10
value: 52.476
- type: recall_at_100
value: 76.535
- type: recall_at_1000
value: 92.658
- type: recall_at_20
value: 61.211000000000006
- type: recall_at_3
value: 38.805
- type: recall_at_5
value: 45.053
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 20.092
- type: map_at_10
value: 27.805999999999997
- type: map_at_100
value: 29.137999999999998
- type: map_at_1000
value: 29.266
- type: map_at_20
value: 28.587
- type: map_at_3
value: 25.112000000000002
- type: map_at_5
value: 26.551000000000002
- type: mrr_at_1
value: 24.315
- type: mrr_at_10
value: 32.068000000000005
- type: mrr_at_100
value: 33.039
- type: mrr_at_1000
value: 33.114
- type: mrr_at_20
value: 32.66
- type: mrr_at_3
value: 29.49
- type: mrr_at_5
value: 30.906
- type: ndcg_at_1
value: 24.315
- type: ndcg_at_10
value: 32.9
- type: ndcg_at_100
value: 38.741
- type: ndcg_at_1000
value: 41.657
- type: ndcg_at_20
value: 35.338
- type: ndcg_at_3
value: 28.069
- type: ndcg_at_5
value: 30.169
- type: precision_at_1
value: 24.315
- type: precision_at_10
value: 6.2330000000000005
- type: precision_at_100
value: 1.072
- type: precision_at_1000
value: 0.15
- type: precision_at_20
value: 3.8580000000000005
- type: precision_at_3
value: 13.318
- type: precision_at_5
value: 9.748999999999999
- type: recall_at_1
value: 20.092
- type: recall_at_10
value: 43.832
- type: recall_at_100
value: 68.75099999999999
- type: recall_at_1000
value: 89.25
- type: recall_at_20
value: 52.445
- type: recall_at_3
value: 30.666
- type: recall_at_5
value: 35.873
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 19.317
- type: map_at_10
value: 26.653
- type: map_at_100
value: 28.011999999999997
- type: map_at_1000
value: 28.231
- type: map_at_20
value: 27.301
- type: map_at_3
value: 23.763
- type: map_at_5
value: 25.391000000000002
- type: mrr_at_1
value: 24.506
- type: mrr_at_10
value: 31.991999999999997
- type: mrr_at_100
value: 32.924
- type: mrr_at_1000
value: 32.993
- type: mrr_at_20
value: 32.521
- type: mrr_at_3
value: 29.48
- type: mrr_at_5
value: 30.982
- type: ndcg_at_1
value: 24.506
- type: ndcg_at_10
value: 32.202999999999996
- type: ndcg_at_100
value: 37.797
- type: ndcg_at_1000
value: 40.859
- type: ndcg_at_20
value: 34.098
- type: ndcg_at_3
value: 27.552
- type: ndcg_at_5
value: 29.781000000000002
- type: precision_at_1
value: 24.506
- type: precision_at_10
value: 6.462
- type: precision_at_100
value: 1.35
- type: precision_at_1000
value: 0.22499999999999998
- type: precision_at_20
value: 4.071000000000001
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 9.921000000000001
- type: recall_at_1
value: 19.317
- type: recall_at_10
value: 42.296
- type: recall_at_100
value: 68.2
- type: recall_at_1000
value: 88.565
- type: recall_at_20
value: 49.883
- type: recall_at_3
value: 28.608
- type: recall_at_5
value: 34.854
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 18.0
- type: map_at_10
value: 24.444
- type: map_at_100
value: 25.205
- type: map_at_1000
value: 25.291000000000004
- type: map_at_20
value: 24.834
- type: map_at_3
value: 22.311
- type: map_at_5
value: 23.442
- type: mrr_at_1
value: 20.552
- type: mrr_at_10
value: 27.028999999999996
- type: mrr_at_100
value: 27.706999999999997
- type: mrr_at_1000
value: 27.775
- type: mrr_at_20
value: 27.366
- type: mrr_at_3
value: 25.051000000000002
- type: mrr_at_5
value: 26.063
- type: ndcg_at_1
value: 20.552
- type: ndcg_at_10
value: 28.519
- type: ndcg_at_100
value: 32.580999999999996
- type: ndcg_at_1000
value: 34.99
- type: ndcg_at_20
value: 29.848000000000003
- type: ndcg_at_3
value: 24.46
- type: ndcg_at_5
value: 26.273000000000003
- type: precision_at_1
value: 20.552
- type: precision_at_10
value: 4.801
- type: precision_at_100
value: 0.729
- type: precision_at_1000
value: 0.101
- type: precision_at_20
value: 2.715
- type: precision_at_3
value: 10.940999999999999
- type: precision_at_5
value: 7.761
- type: recall_at_1
value: 18.0
- type: recall_at_10
value: 38.425
- type: recall_at_100
value: 57.885
- type: recall_at_1000
value: 75.945
- type: recall_at_20
value: 43.472
- type: recall_at_3
value: 27.483
- type: recall_at_5
value: 31.866
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 10.014000000000001
- type: map_at_10
value: 14.462
- type: map_at_100
value: 15.364
- type: map_at_1000
value: 15.482999999999999
- type: map_at_20
value: 14.931
- type: map_at_3
value: 12.842
- type: map_at_5
value: 13.697999999999999
- type: mrr_at_1
value: 12.526000000000002
- type: mrr_at_10
value: 17.433
- type: mrr_at_100
value: 18.296
- type: mrr_at_1000
value: 18.383
- type: mrr_at_20
value: 17.897
- type: mrr_at_3
value: 15.703
- type: mrr_at_5
value: 16.627
- type: ndcg_at_1
value: 12.526000000000002
- type: ndcg_at_10
value: 17.697
- type: ndcg_at_100
value: 22.33
- type: ndcg_at_1000
value: 25.587
- type: ndcg_at_20
value: 19.302
- type: ndcg_at_3
value: 14.606
- type: ndcg_at_5
value: 15.946
- type: precision_at_1
value: 12.526000000000002
- type: precision_at_10
value: 3.383
- type: precision_at_100
value: 0.6799999999999999
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 2.147
- type: precision_at_3
value: 7.02
- type: precision_at_5
value: 5.196
- type: recall_at_1
value: 10.014000000000001
- type: recall_at_10
value: 24.623
- type: recall_at_100
value: 45.795
- type: recall_at_1000
value: 69.904
- type: recall_at_20
value: 30.534
- type: recall_at_3
value: 15.955
- type: recall_at_5
value: 19.394
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 19.156000000000002
- type: map_at_10
value: 26.144000000000002
- type: map_at_100
value: 27.157999999999998
- type: map_at_1000
value: 27.288
- type: map_at_20
value: 26.689
- type: map_at_3
value: 24.125
- type: map_at_5
value: 25.369000000000003
- type: mrr_at_1
value: 22.854
- type: mrr_at_10
value: 29.874000000000002
- type: mrr_at_100
value: 30.738
- type: mrr_at_1000
value: 30.826999999999998
- type: mrr_at_20
value: 30.354
- type: mrr_at_3
value: 27.689999999999998
- type: mrr_at_5
value: 29.131
- type: ndcg_at_1
value: 22.854
- type: ndcg_at_10
value: 30.469
- type: ndcg_at_100
value: 35.475
- type: ndcg_at_1000
value: 38.59
- type: ndcg_at_20
value: 32.333
- type: ndcg_at_3
value: 26.674999999999997
- type: ndcg_at_5
value: 28.707
- type: precision_at_1
value: 22.854
- type: precision_at_10
value: 5.1209999999999996
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.123
- type: precision_at_20
value: 3.0460000000000003
- type: precision_at_3
value: 12.127
- type: precision_at_5
value: 8.75
- type: recall_at_1
value: 19.156000000000002
- type: recall_at_10
value: 40.009
- type: recall_at_100
value: 62.419999999999995
- type: recall_at_1000
value: 84.585
- type: recall_at_20
value: 46.912
- type: recall_at_3
value: 29.733999999999998
- type: recall_at_5
value: 34.741
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 19.317
- type: map_at_10
value: 26.653
- type: map_at_100
value: 28.011999999999997
- type: map_at_1000
value: 28.231
- type: map_at_20
value: 27.301
- type: map_at_3
value: 23.763
- type: map_at_5
value: 25.391000000000002
- type: mrr_at_1
value: 24.506
- type: mrr_at_10
value: 31.991999999999997
- type: mrr_at_100
value: 32.924
- type: mrr_at_1000
value: 32.993
- type: mrr_at_20
value: 32.521
- type: mrr_at_3
value: 29.48
- type: mrr_at_5
value: 30.982
- type: ndcg_at_1
value: 24.506
- type: ndcg_at_10
value: 32.202999999999996
- type: ndcg_at_100
value: 37.797
- type: ndcg_at_1000
value: 40.859
- type: ndcg_at_20
value: 34.098
- type: ndcg_at_3
value: 27.552
- type: ndcg_at_5
value: 29.781000000000002
- type: precision_at_1
value: 24.506
- type: precision_at_10
value: 6.462
- type: precision_at_100
value: 1.35
- type: precision_at_1000
value: 0.22499999999999998
- type: precision_at_20
value: 4.071000000000001
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 9.921000000000001
- type: recall_at_1
value: 19.317
- type: recall_at_10
value: 42.296
- type: recall_at_100
value: 68.2
- type: recall_at_1000
value: 88.565
- type: recall_at_20
value: 49.883
- type: recall_at_3
value: 28.608
- type: recall_at_5
value: 34.854
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 12.822
- type: map_at_10
value: 18.055
- type: map_at_100
value: 18.942
- type: map_at_1000
value: 19.057
- type: map_at_20
value: 18.544
- type: map_at_3
value: 15.964
- type: map_at_5
value: 16.833000000000002
- type: mrr_at_1
value: 14.048
- type: mrr_at_10
value: 19.489
- type: mrr_at_100
value: 20.392
- type: mrr_at_1000
value: 20.49
- type: mrr_at_20
value: 19.979
- type: mrr_at_3
value: 17.344
- type: mrr_at_5
value: 18.287
- type: ndcg_at_1
value: 14.048
- type: ndcg_at_10
value: 21.737000000000002
- type: ndcg_at_100
value: 26.383000000000003
- type: ndcg_at_1000
value: 29.555
- type: ndcg_at_20
value: 23.463
- type: ndcg_at_3
value: 17.29
- type: ndcg_at_5
value: 18.829
- type: precision_at_1
value: 14.048
- type: precision_at_10
value: 3.6229999999999998
- type: precision_at_100
value: 0.641
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 2.1999999999999997
- type: precision_at_3
value: 7.2090000000000005
- type: precision_at_5
value: 5.213
- type: recall_at_1
value: 12.822
- type: recall_at_10
value: 32.123000000000005
- type: recall_at_100
value: 53.657999999999994
- type: recall_at_1000
value: 77.72200000000001
- type: recall_at_20
value: 38.66
- type: recall_at_3
value: 19.814999999999998
- type: recall_at_5
value: 23.432
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 13.119
- type: map_at_10
value: 22.999
- type: map_at_100
value: 25.108000000000004
- type: map_at_1000
value: 25.306
- type: map_at_20
value: 24.141000000000002
- type: map_at_3
value: 19.223000000000003
- type: map_at_5
value: 21.181
- type: mrr_at_1
value: 30.554
- type: mrr_at_10
value: 42.553000000000004
- type: mrr_at_100
value: 43.498
- type: mrr_at_1000
value: 43.527
- type: mrr_at_20
value: 43.193
- type: mrr_at_3
value: 39.283
- type: mrr_at_5
value: 41.143
- type: ndcg_at_1
value: 30.554
- type: ndcg_at_10
value: 31.946
- type: ndcg_at_100
value: 39.934999999999995
- type: ndcg_at_1000
value: 43.256
- type: ndcg_at_20
value: 35.101
- type: ndcg_at_3
value: 26.489
- type: ndcg_at_5
value: 28.272000000000002
- type: precision_at_1
value: 30.554
- type: precision_at_10
value: 10.039
- type: precision_at_100
value: 1.864
- type: precision_at_1000
value: 0.248
- type: precision_at_20
value: 6.371
- type: precision_at_3
value: 20.174
- type: precision_at_5
value: 15.296000000000001
- type: recall_at_1
value: 13.119
- type: recall_at_10
value: 37.822
- type: recall_at_100
value: 65.312
- type: recall_at_1000
value: 83.817
- type: recall_at_20
value: 46.760000000000005
- type: recall_at_3
value: 23.858999999999998
- type: recall_at_5
value: 29.609999999999996
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 8.176
- type: map_at_10
value: 19.594
- type: map_at_100
value: 28.081
- type: map_at_1000
value: 29.864
- type: map_at_20
value: 22.983999999999998
- type: map_at_3
value: 13.923
- type: map_at_5
value: 16.597
- type: mrr_at_1
value: 66.75
- type: mrr_at_10
value: 75.82600000000001
- type: mrr_at_100
value: 76.145
- type: mrr_at_1000
value: 76.14999999999999
- type: mrr_at_20
value: 76.074
- type: mrr_at_3
value: 74.333
- type: mrr_at_5
value: 75.25800000000001
- type: ndcg_at_1
value: 54.50000000000001
- type: ndcg_at_10
value: 41.806
- type: ndcg_at_100
value: 47.067
- type: ndcg_at_1000
value: 54.397
- type: ndcg_at_20
value: 41.727
- type: ndcg_at_3
value: 46.92
- type: ndcg_at_5
value: 44.381
- type: precision_at_1
value: 66.75
- type: precision_at_10
value: 33.35
- type: precision_at_100
value: 10.92
- type: precision_at_1000
value: 2.222
- type: precision_at_20
value: 25.862000000000002
- type: precision_at_3
value: 51.417
- type: precision_at_5
value: 43.65
- type: recall_at_1
value: 8.176
- type: recall_at_10
value: 26.029000000000003
- type: recall_at_100
value: 53.872
- type: recall_at_1000
value: 76.895
- type: recall_at_20
value: 34.192
- type: recall_at_3
value: 15.789
- type: recall_at_5
value: 20.255000000000003
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.22
- type: f1
value: 43.59074485488622
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 40.872
- type: map_at_10
value: 55.178000000000004
- type: map_at_100
value: 55.859
- type: map_at_1000
value: 55.881
- type: map_at_20
value: 55.66
- type: map_at_3
value: 51.4
- type: map_at_5
value: 53.754000000000005
- type: mrr_at_1
value: 43.744
- type: mrr_at_10
value: 58.36900000000001
- type: mrr_at_100
value: 58.911
- type: mrr_at_1000
value: 58.916999999999994
- type: mrr_at_20
value: 58.779
- type: mrr_at_3
value: 54.653
- type: mrr_at_5
value: 56.987
- type: ndcg_at_1
value: 43.744
- type: ndcg_at_10
value: 62.936
- type: ndcg_at_100
value: 65.666
- type: ndcg_at_1000
value: 66.08699999999999
- type: ndcg_at_20
value: 64.548
- type: ndcg_at_3
value: 55.543
- type: ndcg_at_5
value: 59.646
- type: precision_at_1
value: 43.744
- type: precision_at_10
value: 9.191
- type: precision_at_100
value: 1.072
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 4.967
- type: precision_at_3
value: 23.157
- type: precision_at_5
value: 16.115
- type: recall_at_1
value: 40.872
- type: recall_at_10
value: 83.818
- type: recall_at_100
value: 95.14200000000001
- type: recall_at_1000
value: 97.897
- type: recall_at_20
value: 89.864
- type: recall_at_3
value: 64.19200000000001
- type: recall_at_5
value: 74.029
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 14.804999999999998
- type: map_at_10
value: 22.86
- type: map_at_100
value: 24.823999999999998
- type: map_at_1000
value: 25.041000000000004
- type: map_at_20
value: 23.881
- type: map_at_3
value: 20.09
- type: map_at_5
value: 21.39
- type: mrr_at_1
value: 29.938
- type: mrr_at_10
value: 37.041000000000004
- type: mrr_at_100
value: 38.196000000000005
- type: mrr_at_1000
value: 38.256
- type: mrr_at_20
value: 37.693
- type: mrr_at_3
value: 34.721999999999994
- type: mrr_at_5
value: 35.787
- type: ndcg_at_1
value: 29.938
- type: ndcg_at_10
value: 29.358
- type: ndcg_at_100
value: 37.544
- type: ndcg_at_1000
value: 41.499
- type: ndcg_at_20
value: 32.354
- type: ndcg_at_3
value: 26.434
- type: ndcg_at_5
value: 26.93
- type: precision_at_1
value: 29.938
- type: precision_at_10
value: 8.117
- type: precision_at_100
value: 1.611
- type: precision_at_1000
value: 0.232
- type: precision_at_20
value: 5.255
- type: precision_at_3
value: 17.49
- type: precision_at_5
value: 12.747
- type: recall_at_1
value: 14.804999999999998
- type: recall_at_10
value: 34.776
- type: recall_at_100
value: 66.279
- type: recall_at_1000
value: 89.96600000000001
- type: recall_at_20
value: 44.31
- type: recall_at_3
value: 23.623
- type: recall_at_5
value: 27.194000000000003
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 38.555
- type: map_at_10
value: 54.20700000000001
- type: map_at_100
value: 55.177
- type: map_at_1000
value: 55.254999999999995
- type: map_at_20
value: 54.788000000000004
- type: map_at_3
value: 51.034
- type: map_at_5
value: 52.998
- type: mrr_at_1
value: 77.11
- type: mrr_at_10
value: 82.93199999999999
- type: mrr_at_100
value: 83.14200000000001
- type: mrr_at_1000
value: 83.15
- type: mrr_at_20
value: 83.062
- type: mrr_at_3
value: 81.95599999999999
- type: mrr_at_5
value: 82.586
- type: ndcg_at_1
value: 77.11
- type: ndcg_at_10
value: 63.853
- type: ndcg_at_100
value: 67.18499999999999
- type: ndcg_at_1000
value: 68.676
- type: ndcg_at_20
value: 65.279
- type: ndcg_at_3
value: 59.301
- type: ndcg_at_5
value: 61.822
- type: precision_at_1
value: 77.11
- type: precision_at_10
value: 13.044
- type: precision_at_100
value: 1.5630000000000002
- type: precision_at_1000
value: 0.17600000000000002
- type: precision_at_20
value: 6.979
- type: precision_at_3
value: 36.759
- type: precision_at_5
value: 24.054000000000002
- type: recall_at_1
value: 38.555
- type: recall_at_10
value: 65.21900000000001
- type: recall_at_100
value: 78.16300000000001
- type: recall_at_1000
value: 88.02799999999999
- type: recall_at_20
value: 69.791
- type: recall_at_3
value: 55.138
- type: recall_at_5
value: 60.135000000000005
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 69.8728
- type: ap
value: 63.98214492125858
- type: f1
value: 69.59975497754624
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification
config: default
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.76288189694483
- type: f1
value: 94.52150972672682
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification
config: default
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.83994528043777
- type: f1
value: 57.95571154189732
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification
config: default
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 46.1163416274378
- type: f1
value: 45.425692244093064
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification
config: default
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 45.57834566240753
- type: f1
value: 43.84840097785479
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.86396397182615
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 34.018965727588565
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
- type: map
value: 31.286618059824573
- type: mrr
value: 32.481830769278965
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 4.236
- type: map_at_10
value: 9.352
- type: map_at_100
value: 12.382
- type: map_at_1000
value: 13.828999999999999
- type: map_at_20
value: 10.619
- type: map_at_3
value: 6.814000000000001
- type: map_at_5
value: 7.887
- type: mrr_at_1
value: 37.152
- type: mrr_at_10
value: 47.055
- type: mrr_at_100
value: 47.82
- type: mrr_at_1000
value: 47.86
- type: mrr_at_20
value: 47.605
- type: mrr_at_3
value: 44.118
- type: mrr_at_5
value: 46.115
- type: ndcg_at_1
value: 34.365
- type: ndcg_at_10
value: 28.473
- type: ndcg_at_100
value: 27.311999999999998
- type: ndcg_at_1000
value: 36.671
- type: ndcg_at_20
value: 27.137
- type: ndcg_at_3
value: 31.939
- type: ndcg_at_5
value: 30.428
- type: precision_at_1
value: 36.223
- type: precision_at_10
value: 21.858
- type: precision_at_100
value: 7.417999999999999
- type: precision_at_1000
value: 2.0709999999999997
- type: precision_at_20
value: 16.502
- type: precision_at_3
value: 30.857
- type: precision_at_5
value: 26.997
- type: recall_at_1
value: 4.236
- type: recall_at_10
value: 13.489
- type: recall_at_100
value: 29.580000000000002
- type: recall_at_1000
value: 62.726000000000006
- type: recall_at_20
value: 18.346999999999998
- type: recall_at_3
value: 7.811
- type: recall_at_5
value: 10.086
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 21.123
- type: map_at_10
value: 34.429
- type: map_at_100
value: 35.803000000000004
- type: map_at_1000
value: 35.853
- type: map_at_20
value: 35.308
- type: map_at_3
value: 30.095
- type: map_at_5
value: 32.435
- type: mrr_at_1
value: 23.841
- type: mrr_at_10
value: 36.864999999999995
- type: mrr_at_100
value: 37.935
- type: mrr_at_1000
value: 37.97
- type: mrr_at_20
value: 37.566
- type: mrr_at_3
value: 32.918
- type: mrr_at_5
value: 35.11
- type: ndcg_at_1
value: 23.841
- type: ndcg_at_10
value: 42.043
- type: ndcg_at_100
value: 48.015
- type: ndcg_at_1000
value: 49.152
- type: ndcg_at_20
value: 44.936
- type: ndcg_at_3
value: 33.513999999999996
- type: ndcg_at_5
value: 37.541999999999994
- type: precision_at_1
value: 23.841
- type: precision_at_10
value: 7.454
- type: precision_at_100
value: 1.081
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_20
value: 4.413
- type: precision_at_3
value: 15.672
- type: precision_at_5
value: 11.657
- type: recall_at_1
value: 21.123
- type: recall_at_10
value: 63.096
- type: recall_at_100
value: 89.27199999999999
- type: recall_at_1000
value: 97.69
- type: recall_at_20
value: 73.873
- type: recall_at_3
value: 40.588
- type: recall_at_5
value: 49.928
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 70.255
- type: map_at_10
value: 84.387
- type: map_at_100
value: 85.027
- type: map_at_1000
value: 85.043
- type: map_at_20
value: 84.809
- type: map_at_3
value: 81.5
- type: map_at_5
value: 83.286
- type: mrr_at_1
value: 80.85
- type: mrr_at_10
value: 87.25699999999999
- type: mrr_at_100
value: 87.363
- type: mrr_at_1000
value: 87.363
- type: mrr_at_20
value: 87.336
- type: mrr_at_3
value: 86.357
- type: mrr_at_5
value: 86.939
- type: ndcg_at_1
value: 80.86
- type: ndcg_at_10
value: 88.151
- type: ndcg_at_100
value: 89.381
- type: ndcg_at_1000
value: 89.47800000000001
- type: ndcg_at_20
value: 88.82100000000001
- type: ndcg_at_3
value: 85.394
- type: ndcg_at_5
value: 86.855
- type: precision_at_1
value: 80.86
- type: precision_at_10
value: 13.397
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.106999999999999
- type: precision_at_3
value: 37.46
- type: precision_at_5
value: 24.568
- type: recall_at_1
value: 70.255
- type: recall_at_10
value: 95.405
- type: recall_at_100
value: 99.56
- type: recall_at_1000
value: 99.98599999999999
- type: recall_at_20
value: 97.544
- type: recall_at_3
value: 87.414
- type: recall_at_5
value: 91.598
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 54.7557403999403
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 56.2773308957202
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.123
- type: map_at_10
value: 9.940999999999999
- type: map_at_100
value: 11.928999999999998
- type: map_at_1000
value: 12.257
- type: map_at_20
value: 10.866000000000001
- type: map_at_3
value: 7.091
- type: map_at_5
value: 8.393
- type: mrr_at_1
value: 20.3
- type: mrr_at_10
value: 30.068
- type: mrr_at_100
value: 31.296000000000003
- type: mrr_at_1000
value: 31.36
- type: mrr_at_20
value: 30.756
- type: mrr_at_3
value: 26.667
- type: mrr_at_5
value: 28.616999999999997
- type: ndcg_at_1
value: 20.3
- type: ndcg_at_10
value: 17.305
- type: ndcg_at_100
value: 25.529000000000003
- type: ndcg_at_1000
value: 31.41
- type: ndcg_at_20
value: 19.967
- type: ndcg_at_3
value: 16.022
- type: ndcg_at_5
value: 14.12
- type: precision_at_1
value: 20.3
- type: precision_at_10
value: 9.06
- type: precision_at_100
value: 2.103
- type: precision_at_1000
value: 0.35200000000000004
- type: precision_at_20
value: 6.075
- type: precision_at_3
value: 14.832999999999998
- type: precision_at_5
value: 12.36
- type: recall_at_1
value: 4.123
- type: recall_at_10
value: 18.383
- type: recall_at_100
value: 42.67
- type: recall_at_1000
value: 71.44800000000001
- type: recall_at_20
value: 24.64
- type: recall_at_3
value: 9.043
- type: recall_at_5
value: 12.543000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 84.37101718384514
- type: cos_sim_spearman
value: 80.73657031880697
- type: euclidean_pearson
value: 81.42351850520845
- type: euclidean_spearman
value: 80.81452496851979
- type: manhattan_pearson
value: 81.47676252115669
- type: manhattan_spearman
value: 80.87566944708885
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.79559176971591
- type: cos_sim_spearman
value: 75.41866597445552
- type: euclidean_pearson
value: 83.20287101163838
- type: euclidean_spearman
value: 75.54564777571143
- type: manhattan_pearson
value: 83.24622548900163
- type: manhattan_spearman
value: 75.63826258190343
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.63322096299294
- type: cos_sim_spearman
value: 85.48272638914783
- type: euclidean_pearson
value: 85.57327707819331
- type: euclidean_spearman
value: 85.90735298172922
- type: manhattan_pearson
value: 85.5744191274933
- type: manhattan_spearman
value: 85.90828008488766
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.05530140566407
- type: cos_sim_spearman
value: 78.85454907951474
- type: euclidean_pearson
value: 81.4307311680376
- type: euclidean_spearman
value: 78.99131623529348
- type: manhattan_pearson
value: 81.46870892683134
- type: manhattan_spearman
value: 79.05473823658481
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.66620817683379
- type: cos_sim_spearman
value: 85.23347998035328
- type: euclidean_pearson
value: 84.59001637865366
- type: euclidean_spearman
value: 85.0081410316597
- type: manhattan_pearson
value: 84.59742325369818
- type: manhattan_spearman
value: 85.01721329704324
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 79.86344730144208
- type: cos_sim_spearman
value: 82.15966778685441
- type: euclidean_pearson
value: 81.85580574642779
- type: euclidean_spearman
value: 82.06482873417123
- type: manhattan_pearson
value: 81.82971046102377
- type: manhattan_spearman
value: 82.04185436355144
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17
config: default
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cos_sim_pearson
value: 31.440481026661672
- type: cos_sim_spearman
value: 31.592743544965913
- type: euclidean_pearson
value: 31.15111049327518
- type: euclidean_spearman
value: 30.555124184361464
- type: manhattan_pearson
value: 31.724139249295654
- type: manhattan_spearman
value: 30.483389245793504
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22
config: default
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cos_sim_pearson
value: 34.51489724275415
- type: cos_sim_spearman
value: 47.06532141601629
- type: euclidean_pearson
value: 33.28904737503036
- type: euclidean_spearman
value: 45.111172981641865
- type: manhattan_pearson
value: 33.36374172942392
- type: manhattan_spearman
value: 45.100940945158534
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.09996292950329
- type: cos_sim_spearman
value: 82.69376206796092
- type: euclidean_pearson
value: 82.83254956369134
- type: euclidean_spearman
value: 82.34202999843637
- type: manhattan_pearson
value: 82.8048494319632
- type: manhattan_spearman
value: 82.34713123336984
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.1402269601644
- type: mrr
value: 94.84447197682492
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 49.138999999999996
- type: map_at_10
value: 60.288
- type: map_at_100
value: 61.082
- type: map_at_1000
value: 61.11
- type: map_at_20
value: 60.831999999999994
- type: map_at_3
value: 57.106
- type: map_at_5
value: 58.857000000000006
- type: mrr_at_1
value: 51.333
- type: mrr_at_10
value: 61.364
- type: mrr_at_100
value: 62.029999999999994
- type: mrr_at_1000
value: 62.056
- type: mrr_at_20
value: 61.85000000000001
- type: mrr_at_3
value: 58.721999999999994
- type: mrr_at_5
value: 60.221999999999994
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_10
value: 65.71900000000001
- type: ndcg_at_100
value: 69.036
- type: ndcg_at_1000
value: 69.626
- type: ndcg_at_20
value: 67.571
- type: ndcg_at_3
value: 60.019
- type: ndcg_at_5
value: 62.733000000000004
- type: precision_at_1
value: 51.333
- type: precision_at_10
value: 9.067
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 4.95
- type: precision_at_3
value: 23.889
- type: precision_at_5
value: 16.0
- type: recall_at_1
value: 49.138999999999996
- type: recall_at_10
value: 81.256
- type: recall_at_100
value: 95.6
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 88.289
- type: recall_at_3
value: 66.078
- type: recall_at_5
value: 72.661
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.73762376237623
- type: cos_sim_ap
value: 93.02149432690442
- type: cos_sim_f1
value: 86.59079663532904
- type: cos_sim_precision
value: 85.70029382957884
- type: cos_sim_recall
value: 87.5
- type: dot_accuracy
value: 99.73267326732673
- type: dot_ap
value: 92.38661051842968
- type: dot_f1
value: 85.92283628779978
- type: dot_precision
value: 89.76034858387798
- type: dot_recall
value: 82.39999999999999
- type: euclidean_accuracy
value: 99.73960396039604
- type: euclidean_ap
value: 92.99557708360517
- type: euclidean_f1
value: 86.49183572488866
- type: euclidean_precision
value: 85.60235063663075
- type: euclidean_recall
value: 87.4
- type: manhattan_accuracy
value: 99.74059405940594
- type: manhattan_ap
value: 93.24237279644005
- type: manhattan_f1
value: 86.77727501256913
- type: manhattan_precision
value: 87.25985844287159
- type: manhattan_recall
value: 86.3
- type: max_accuracy
value: 99.74059405940594
- type: max_ap
value: 93.24237279644005
- type: max_f1
value: 86.77727501256913
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 63.94924261127149
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.22297034902405
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 46.12948438780115
- type: mrr
value: 46.77186783804431
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.02235612863601
- type: cos_sim_spearman
value: 30.567504287706598
- type: dot_pearson
value: 28.943978981614897
- type: dot_spearman
value: 29.905635915797358
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.173
- type: map_at_10
value: 1.124
- type: map_at_100
value: 5.645
- type: map_at_1000
value: 14.965
- type: map_at_20
value: 1.876
- type: map_at_3
value: 0.45599999999999996
- type: map_at_5
value: 0.699
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 81.786
- type: mrr_at_100
value: 81.786
- type: mrr_at_1000
value: 81.786
- type: mrr_at_20
value: 81.786
- type: mrr_at_3
value: 80.0
- type: mrr_at_5
value: 81.5
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 53.88699999999999
- type: ndcg_at_100
value: 38.028
- type: ndcg_at_1000
value: 37.183
- type: ndcg_at_20
value: 49.286
- type: ndcg_at_3
value: 63.05
- type: ndcg_at_5
value: 59.49100000000001
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 55.400000000000006
- type: precision_at_100
value: 38.800000000000004
- type: precision_at_1000
value: 17.082
- type: precision_at_20
value: 50.7
- type: precision_at_3
value: 66.667
- type: precision_at_5
value: 62.4
- type: recall_at_1
value: 0.173
- type: recall_at_10
value: 1.353
- type: recall_at_100
value: 8.887
- type: recall_at_1000
value: 36.012
- type: recall_at_20
value: 2.476
- type: recall_at_3
value: 0.508
- type: recall_at_5
value: 0.795
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.614
- type: map_at_10
value: 6.651999999999999
- type: map_at_100
value: 11.59
- type: map_at_1000
value: 13.044
- type: map_at_20
value: 8.702
- type: map_at_3
value: 4.159
- type: map_at_5
value: 5.327
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 42.664
- type: mrr_at_100
value: 43.957
- type: mrr_at_1000
value: 43.957
- type: mrr_at_20
value: 43.193
- type: mrr_at_3
value: 40.476
- type: mrr_at_5
value: 42.007
- type: ndcg_at_1
value: 27.551
- type: ndcg_at_10
value: 18.098
- type: ndcg_at_100
value: 30.019000000000002
- type: ndcg_at_1000
value: 42.179
- type: ndcg_at_20
value: 19.552
- type: ndcg_at_3
value: 21.22
- type: ndcg_at_5
value: 19.774
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 15.101999999999999
- type: precision_at_100
value: 6.510000000000001
- type: precision_at_1000
value: 1.4569999999999999
- type: precision_at_20
value: 12.449
- type: precision_at_3
value: 22.448999999999998
- type: precision_at_5
value: 19.592000000000002
- type: recall_at_1
value: 2.614
- type: recall_at_10
value: 11.068
- type: recall_at_100
value: 42.317
- type: recall_at_1000
value: 79.063
- type: recall_at_20
value: 18.589
- type: recall_at_3
value: 5.06
- type: recall_at_5
value: 7.356
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 75.0146484375
- type: ap
value: 16.80191476928431
- type: f1
value: 58.08037205204817
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.80249009620826
- type: f1
value: 62.24155926661914
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 47.074846780747094
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.21785778148656
- type: cos_sim_ap
value: 71.06584074764645
- type: cos_sim_f1
value: 65.81720166625826
- type: cos_sim_precision
value: 61.43641354071363
- type: cos_sim_recall
value: 70.87071240105541
- type: dot_accuracy
value: 84.30589497526375
- type: dot_ap
value: 68.85872202019365
- type: dot_f1
value: 64.20295157946092
- type: dot_precision
value: 59.69607620775687
- type: dot_recall
value: 69.44591029023746
- type: euclidean_accuracy
value: 85.21189724026942
- type: euclidean_ap
value: 71.18847194129523
- type: euclidean_f1
value: 66.00049962528105
- type: euclidean_precision
value: 62.66603415559773
- type: euclidean_recall
value: 69.70976253298153
- type: manhattan_accuracy
value: 85.25958157000656
- type: manhattan_ap
value: 71.12967638566641
- type: manhattan_f1
value: 65.77477594492791
- type: manhattan_precision
value: 64.77359938603223
- type: manhattan_recall
value: 66.80738786279683
- type: max_accuracy
value: 85.25958157000656
- type: max_ap
value: 71.18847194129523
- type: max_f1
value: 66.00049962528105
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.22330888345559
- type: cos_sim_ap
value: 84.40304506741951
- type: cos_sim_f1
value: 76.46823520855303
- type: cos_sim_precision
value: 72.45537867824409
- type: cos_sim_recall
value: 80.95164767477672
- type: dot_accuracy
value: 87.9400007761866
- type: dot_ap
value: 83.63499141834609
- type: dot_f1
value: 75.98620939938304
- type: dot_precision
value: 71.86792064254823
- type: dot_recall
value: 80.60517400677548
- type: euclidean_accuracy
value: 88.21166608452671
- type: euclidean_ap
value: 84.40463988450605
- type: euclidean_f1
value: 76.52312831312177
- type: euclidean_precision
value: 72.40621135083138
- type: euclidean_recall
value: 81.13643363104404
- type: manhattan_accuracy
value: 88.24659448131331
- type: manhattan_ap
value: 84.42287495905447
- type: manhattan_f1
value: 76.54849595413475
- type: manhattan_precision
value: 72.39036442248302
- type: manhattan_recall
value: 81.21342777948875
- type: max_accuracy
value: 88.24659448131331
- type: max_ap
value: 84.42287495905447
- type: max_f1
value: 76.54849595413475
---
# b1ade-embed-kd
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was distilled with teacher model as
and student model as b1ade-embed
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 275105 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 5000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 5e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Results:
Good agreement with teacher model, at least on STS:
Teacher:
```
2024-05-20 16:29:07 - Teacher Performance:
2024-05-20 16:29:07 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
2024-05-20 16:29:12 - Cosine-Similarity : Pearson: 0.8561 Spearman: 0.8597
2024-05-20 16:29:12 - Manhattan-Distance: Pearson: 0.8569 Spearman: 0.8567
2024-05-20 16:29:12 - Euclidean-Distance: Pearson: 0.8575 Spearman: 0.8571
2024-05-20 16:29:12 - Dot-Product-Similarity: Pearson: 0.8624 Spearman: 0.8662
```
Student:
```
2024-05-20 16:29:12 - Student Performance:
2024-05-20 16:29:12 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
2024-05-20 16:29:17 - Cosine-Similarity : Pearson: 0.8561 Spearman: 0.8597
2024-05-20 16:29:17 - Manhattan-Distance: Pearson: 0.8569 Spearman: 0.8567
2024-05-20 16:29:17 - Euclidean-Distance: Pearson: 0.8575 Spearman: 0.8571
2024-05-20 16:29:17 - Dot-Product-Similarity: Pearson: 0.8624 Spearman: 0.8662
``` |
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