modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
GS-23/plbart-algo2code | GS-23 | 2024-10-20T07:24:17Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"plbart",
"text2text-generation",
"arxiv:1910.09700",
"base_model:uclanlp/plbart-python-en_XX",
"base_model:finetune:uclanlp/plbart-python-en_XX",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-19T17:20:09Z | ---
library_name: transformers
metrics:
- chrf
- bleu
- exact_match
- rouge
base_model:
- uclanlp/plbart-python-en_XX
---
# Model Card for plbart-algo2code
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rewicks/baseline_en-de_16k_ep20 | rewicks | 2024-10-20T07:22:41Z | 265 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-15T18:32:32Z | ---
library_name: transformers
tags: []
---
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1MK26/BART-HydrogenGeneration | 1MK26 | 2024-10-20T07:22:11Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-20T07:21:20Z | ---
library_name: transformers
tags: []
---
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wooseok0303/roberta-base-klue-ynat-classification | wooseok0303 | 2024-10-20T07:17:03Z | 131 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-20T07:09:44Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_16k_ep17 | rewicks | 2024-10-20T07:16:09Z | 111 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-15T04:37:31Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_16k_ep15 | rewicks | 2024-10-20T07:11:19Z | 261 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T21:51:51Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_8k_ep23 | rewicks | 2024-10-20T07:11:07Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-16T01:43:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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yasyune/Kurage_Kikoto__Mahiro_Kikoto_so-vits-svc-4.0v1 | yasyune | 2024-10-20T07:08:15Z | 0 | 7 | null | [
"region:us"
] | null | 2023-03-28T00:31:43Z | ---
{}
---
黄琴海月さん・黄琴まひろさんのITAコーパスを学習させて作ったso-vits-svc4.0v1用のモデルデータです。
必ず利用規約を読んでから使用してください。
Please be sure to read the Terms of Use before use.
利用規約 Terms of Use https://kikyohiroto1227.wixsite.com/kikoto-utau/terms-of-service |
rewicks/baseline_en-de_16k_ep13 | rewicks | 2024-10-20T07:06:09Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T21:44:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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rewicks/baseline_en-de_16k_ep12 | rewicks | 2024-10-20T07:03:30Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T21:41:41Z | ---
library_name: transformers
tags: []
---
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Rhystz/rhstz2 | Rhystz | 2024-10-20T06:59:34Z | 8 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-20T06:59:27Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: rhstz11
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# rhstz
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `rhstz11` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
rewicks/baseline_en-de_16k_ep10 | rewicks | 2024-10-20T06:58:15Z | 277 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T21:34:57Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_16k_ep9 | rewicks | 2024-10-20T06:55:48Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T21:30:50Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_8k_ep17 | rewicks | 2024-10-20T06:55:31Z | 122 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-15T03:18:08Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_16k_ep8 | rewicks | 2024-10-20T06:53:40Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T21:27:54Z | ---
library_name: transformers
tags: []
---
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CarbonLover/krx_test_model | CarbonLover | 2024-10-20T06:52:15Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"krx",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T06:02:16Z | ---
library_name: transformers
tags:
- krx
---
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vocabtrimmer/bert-base-spanish-wwm-cased.xnli-es.1 | vocabtrimmer | 2024-10-20T06:49:01Z | 5 | 0 | null | [
"safetensors",
"bert",
"region:us"
] | null | 2024-10-20T06:48:48Z | # `vocabtrimmer/bert-base-spanish-wwm-cased.xnli-es.1`
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the
[xnli](https://huggingface.co/datasets/xnli) (es).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(es).
* Evaluation on test split
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 32.26 | 32.26 | 32.26 | 27.18 | 32.26 | 31.94 | 32.26 |
* Evaluation on validation split
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 32.01 | 32.01 | 32.01 | 26.56 | 32.01 | 30.82 | 32.01 |
Check the result file [here](https://huggingface.co/vocabtrimmer/bert-base-spanish-wwm-cased.xnli-es.1/raw/main/eval.json). |
rewicks/baseline_en-de_8k_ep14 | rewicks | 2024-10-20T06:47:57Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T20:49:12Z | ---
library_name: transformers
tags: []
---
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rewicks/baseline_en-de_8k_ep12 | rewicks | 2024-10-20T06:43:06Z | 119 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T20:45:18Z | ---
library_name: transformers
tags: []
---
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mav23/Theia-Llama-3.1-8B-v1-GGUF | mav23 | 2024-10-20T06:42:05Z | 81 | 0 | transformers | [
"transformers",
"gguf",
"text-generation",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-20T05:24:59Z | ---
license: llama3.1
base_model: Llama-3.1-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# Theia-Llama-3.1-8B-v1
**Theia-Llama-3.1-8B-v1 is an open-source crypto LLM, trained with carefully-designed dataset from the crypto field.**
## Technical Implementation
### Crypto-Oriented Dataset
The training dataset is curated from two primary sources to create a comprehensive representation of blockchain
projects. The first source is data collected from **CoinMarketCap**, focusing on the top **2000 projects** ranked by
market capitalization. This includes a wide range of project-specific documents such as whitepapers, official blog
posts, and news articles. The second core component of the dataset comprises detailed research reports on these projects
gathered from various credible sources on the internet, providing in-depth insights into project fundamentals,
development progress, and market impact. After constructing the dataset, both manual and algorithmic filtering are
applied to ensure data accuracy and eliminate redundancy.
### Model Fine-tuning and Quantization
The Theia-Llama-3.1-8B-v1 is fine-tuned from the base model (Llama-3.1-8B), specifically tailored for the cryptocurrency
domain. We employed LoRA (Low-Rank Adaptation) to fine-tune the model effectively, leveraging its ability to adapt large
pre-trained models to specific tasks with a smaller computational footprint. Our training methodology is further
enhanced through the use of LLaMA Factory, an open-source training framework. We integrate **DeepSpeed**, Microsoft's
distributed training engine, to optimize resource utilization and training efficiency. Techniques such as ZeRO (Zero
Redundancy Optimizer), offload, sparse attention, 1-bit Adam, and pipeline parallelism are employed to accelerate the
training process and reduce memory consumption. A fine-tuned model is also built using the
novel [D-DoRA](https://docs.chainbase.com/theia/Developers/Glossary/D2ORA), a decentralized training scheme, by our
Chainbase Labs. Since the LoRA version is much easier to deploy and play with for developers, we release the LoRA
version first for the Crypto AI community.
In addition to fine-tuning, we have quantized the model to optimize it for efficient deployment, specifically into the
Q8 GGUF format `Theia-Llama-3.1-8B-v1-Q8_0.gguf`. Model quantization is a process that reduces the precision of the
model's weights from floating-point (typically FP16 or FP32) to lower-bit representations, in this case, 8-bit
integers (Q8). The primary benefit of quantization is that it significantly reduces the model's memory footprint and
improves inference speed while maintaining an acceptable level of accuracy. This makes the model more accessible for use
in resource-constrained environments, such as on edge devices or lower-tier GPUs.
## Benchmark
To evaluate the current LLMs in the crypto domain, we have proposed a benchmark for evaluating Crypto AI Models, which
is the first AI model benchmark tailored specifically for the crypto domain. The models are evaluated across seven
dimensions, including crypto knowledge comprehension and generation, knowledge coverage, and reasoning capabilities,
etc. A detailed paper will follow to elaborate on this benchmark. Here we initially release the results of benchmarking
the understanding and generation capabilities in the crypto domain on 11 open-source and close-source LLMs from OpenAI,
Google, Meta, Qwen, and DeepSeek. For the open-source LLMs, we choose the models with the similar parameter size as
ours (~8b). For the close-source LLMs, we choose the popular models with most end-users.
| Model | Perplexity ↓ | BERT ↑ |
|---------------------------|--------------|-----------|
| **Theia-Llama-3.1-8B-v1** | **1.184** | **0.861** |
| ChatGPT-4o | 1.256 | 0.837 |
| ChatGPT-4o-mini | 1.257 | 0.794 |
| ChatGPT-3.5-turbo | 1.233 | 0.838 |
| Claude-3-sonnet (~70b) | N.A. | 0.848 |
| Gemini-1.5-Pro | N.A. | 0.830 |
| Gemini-1.5-Flash | N.A. | 0.828 |
| Llama-3.1-8B-Instruct | 1.270 | 0.835 |
| Mistral-7B-Instruct-v0.3 | 1.258 | 0.844 |
| Qwen2.5-7B-Instruct | 1.392 | 0.832 |
| Gemma-2-9b | 1.248 | 0.832 |
| Deepseek-llm-7b-chat | 1.348 | 0.846 |
## System Prompt
The system prompt used for training this model is:
```
You are a helpful assistant who will answer crypto related questions.
```
## Chat Format
As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
Today Date: 29 September 2024
You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>
What is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Tips for Performance
We are initially recommending a set of parameters.
```
sequence length = 256
temperature = 0
top-k-sampling = -1
top-p = 1
context window = 39680
```
|
rewicks/baseline_en-de_16k_ep1 | rewicks | 2024-10-20T06:38:43Z | 237 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T22:50:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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rewicks/baseline_en-de_8k_ep10 | rewicks | 2024-10-20T06:38:34Z | 405 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T20:39:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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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).
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rewicks/baseline_en-de_8k_ep9 | rewicks | 2024-10-20T06:36:35Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T20:35:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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ai1-test/trainer_wheel_model | ai1-test | 2024-10-20T06:32:42Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-09-28T20:10:41Z | ---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: trainer_wheel_model
results: []
---
<!-- 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. -->
# trainer_wheel_model
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8911
- Refined F1: 6.5281
- Meteor: 14.3360
- Bert Score F1: 80.5293
- Rouge1: 11.9272
- Rouge2: 1.9063
- Rougel: 8.2440
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Refined F1 | Meteor | Bert Score F1 | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:-----:|:---------------:|:----------:|:-------:|:-------------:|:-------:|:------:|:------:|
| 3.1114 | 1.0 | 3535 | 2.9473 | 6.3827 | 14.0121 | 80.5994 | 12.0441 | 1.9421 | 8.4024 |
| 3.021 | 2.0 | 7070 | 2.9119 | 6.4954 | 14.0416 | 80.4270 | 11.8023 | 1.9007 | 8.2623 |
| 2.9904 | 3.0 | 10605 | 2.8950 | 6.5251 | 14.2535 | 80.5717 | 12.0747 | 1.9382 | 8.3584 |
| 2.9729 | 4.0 | 14140 | 2.8911 | 6.5281 | 14.3360 | 80.5293 | 11.9272 | 1.9063 | 8.2440 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
rewicks/baseline_en-de_8k_ep7 | rewicks | 2024-10-20T06:32:13Z | 123 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T20:29:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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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.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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ai1-test/finance-chatbot-flan-t5-base | ai1-test | 2024-10-20T06:32:01Z | 124 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-20T06:31:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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deman539/snowflake-arctic-embed-m-finetuned-indeed-jobs | deman539 | 2024-10-20T06:30:54Z | 41 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-10-20T06:13:59Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("deman539/snowflake-arctic-embed-m-finetuned-indeed-jobs")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
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-->
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rewicks/baseline_en-de_8k_ep6 | rewicks | 2024-10-20T06:30:23Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-13T20:25:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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rewicks/baseline_en-de_8k_ep4 | rewicks | 2024-10-20T06:25:46Z | 312 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T19:59:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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rewicks/baseline_en-de_8k_ep3 | rewicks | 2024-10-20T06:23:30Z | 301 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T19:58:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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rewicks/baseline_en-de_8k_ep2 | rewicks | 2024-10-20T06:21:11Z | 318 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T19:57:19Z | ---
library_name: transformers
tags: []
---
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## Training Details
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### 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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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#### Summary
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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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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rewicks/baseline_en-de_8k_ep1 | rewicks | 2024-10-20T06:19:13Z | 215 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T19:56:00Z | ---
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]
- **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
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### Direct Use
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[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
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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#### Metrics
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[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]
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katopz/kbtg-kpoint-v2-16bit | katopz | 2024-10-20T06:11:02Z | 138 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T06:08:39Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** katopz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
mergekit-community/L3.1-Pneuma-8B-v1 | mergekit-community | 2024-10-20T05:50:52Z | 7 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:merge:ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2",
"base_model:Replete-AI/L3-Pneuma-8B",
"base_model:merge:Replete-AI/L3-Pneuma-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T05:45:44Z | ---
base_model:
- Replete-AI/L3-Pneuma-8B
- ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
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 della_linear merge method using [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2) as a base.
### Models Merged
The following models were included in the merge:
* [Replete-AI/L3-Pneuma-8B](https://huggingface.co/Replete-AI/L3-Pneuma-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
out_dtype: bfloat16
dtype: float32
tokenizer_source: base
merge_method: della_linear
parameters:
int8_mask: true
density: 0.5
epsilon: 0.04
lambda: 1.05
base_model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
models:
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
parameters:
weight:
- filter: v_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- filter: o_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- filter: up_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- filter: gate_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- filter: down_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- value: 1
- model: Replete-AI/L3-Pneuma-8B
parameters:
weight:
- filter: v_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: o_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: up_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: gate_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: down_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- value: 0
```
|
halbihn/NeuralPipe-7B-ties-GGUF | halbihn | 2024-10-20T05:49:03Z | 3 | 0 | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"halbihn/NeuralHermes-2.5-Mistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-24T03:12:33Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- halbihn/NeuralHermes-2.5-Mistral-7B
---
> Note: This repository contains the GGUF 4-bit quantized variant of `halbihn/NeuralPipe-7B-ties`. For the full version visit [the link](https://huggingface.co/halbihn/NeuralPipe-7B-ties).
# NeuralPipe-7B-ties
NeuralPipe-7B-ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [halbihn/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/halbihn/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: OpenPipe/mistral-ft-optimized-1218
parameters:
density: 0.5
weight: 0.5
- model: halbihn/NeuralHermes-2.5-Mistral-7B
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: true
dtype: float16
``` |
Nutanix/llama-30b_checkpoint-3500_20241020-053615-merged | Nutanix | 2024-10-20T05:46:53Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T05:36:59Z | ---
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]
- **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] |
halbihn/Qwen2.5-Sci | halbihn | 2024-10-20T05:46:19Z | 10 | 0 | null | [
"safetensors",
"qwen2",
"merge",
"mergekit",
"lazymergekit",
"unsloth/Qwen2.5-1.5B-Instruct",
"unsloth/Qwen2.5-Coder-1.5B-Instruct",
"unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:unsloth/Qwen2.5-1.5B-Instruct",
"base_model:merge:unsloth/Qwen2.5-1.5B-Instruct",
"base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"base_model:merge:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:merge:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-10-17T17:26:53Z | ---
base_model:
- unsloth/Qwen2.5-1.5B-Instruct
- unsloth/Qwen2.5-Coder-1.5B-Instruct
- unsloth/Qwen2.5-Math-1.5B-Instruct
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- unsloth/Qwen2.5-1.5B-Instruct
- unsloth/Qwen2.5-Coder-1.5B-Instruct
- unsloth/Qwen2.5-Math-1.5B-Instruct
---
> Note: This model is experimental and has not been tested for quality.
# Qwen2.5-Sci
Qwen2.5-Sci is a `mergekit` merge of the following models:
* [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct)
* [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct)
* [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct)
## 🧩 Configuration
```yaml
models:
- model: unsloth/Qwen2.5-1.5B-Instruct
parameters:
weight: 0.5
- model: unsloth/Qwen2.5-Coder-1.5B-Instruct
parameters:
weight: 0.3
- model: unsloth/Qwen2.5-Math-1.5B-Instruct
parameters:
weight: 0.2
merge_method: task_arithmetic
base_model: unsloth/Qwen2.5-1.5B-Instruct
parameters:
normalize: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "halbihn/Qwen2.5-Sci"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
BanglaLLM/BanglaLLama-3.1-8b-unolp-culturax-base-v0.0.1 | BanglaLLM | 2024-10-20T05:39:21Z | 15 | 0 | null | [
"safetensors",
"llama",
"bangla",
"banglaLLM",
"banglaNLP",
"LLM",
"LLama",
"Transformer",
"bn",
"en",
"dataset:uonlp/CulturaX",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | null | 2024-09-15T16:59:46Z | ---
language:
- bn
- en
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- uonlp/CulturaX
tags:
- bangla
- banglaLLM
- banglaNLP
- LLM
- LLama
- Transformer
---
# Bangla LLaMA-3.1 8B unolp/Culturax Base v0.1 [pre-trained]
Welcome to the inaugural release of the Bangla LLaMA-3.1 8B unolp-culturax base model – an important step in advancing LLMs for the Bangla language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks.
> **Please Note:** This model, labeled as a foundational Bangla Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes.
## Model description
- **Model type:** A 8B parameter model for Causal LM pre-trained on unolp/culturax (subset: bn) dataset.
- **Language(s):** Bangla and English
- **License:** GNU General Public License v3.0
- **Source Model:** [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
- **Training Precision:** `float16`
- **Code:** [GitHub](https://github.com/abhinand5/bangla-llama)
## Related Models
| Model | Type | Data | Base Model | # Params | Download Links |
|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
| Bangla LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) |
| Bangla LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) |
| Bangla LLaMA 7B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) |
| Bangla LLaMA 13B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 13B Base | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-instruct-v0.1) |
| Bangla LLaMA 3 8B Base | Base model | 12.4M | LLaMA 3 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3 8B Base | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.1 8B Base | Base model | 12.4M | LLaMA 3.1 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3.1 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.1 8B Base | 8b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.2 1B Base | Base model | 12.4M | LLaMA 3.2 1b | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3.2 1B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.2 1B Base | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.2 3B Instruct| Instruction following model | 172k instructions | Bangla LLaMA 3.2 3B Base | 3B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1)
## Usage Note
It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
## Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
- [Abdullah Khan Zehady](https://www.linkedin.com/in/abdullah-khan-zehady-915ba024/)
## Citation
We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Bangla language. |
BanglaLLM/BanglaLLama-3.2-3b-unlop-culturax-base-v0.0.3 | BanglaLLM | 2024-10-20T05:33:13Z | 16 | 0 | null | [
"safetensors",
"llama",
"bangla",
"banglaLLM",
"banglaNLP",
"LLM",
"LLama",
"Transformer",
"nlp",
"bengali",
"bn",
"en",
"dataset:uonlp/CulturaX",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"region:us"
] | null | 2024-10-13T09:21:38Z | ---
language:
- bn
- en
license: llama3.2
base_model:
- meta-llama/Llama-3.2-3B
datasets:
- uonlp/CulturaX
tags:
- bangla
- banglaLLM
- banglaNLP
- LLM
- LLama
- Transformer
- nlp
- bengali
---
# Bangla LLaMA-3.2 3B unolp/Culturax Base v0.3 [pre-trained]
Welcome to the inaugural release of the Bangla LLaMA-3.2 3B unolp-culturax base model – an important step in advancing LLMs for the Bangla language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks.
> **Please Note:** This model, labeled as a foundational Bangla Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes.
## Model description
- **Model type:** A 3B parameter model for Causal LM pre-trained on unolp/culturax (subset: bn) dataset.
- **Language(s):** Bangla and English
- **License:** GNU General Public License v3.0
- **Source Model:** [meta-llama/Meta-Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B)
- **Training Precision:** `float16`
- **Code:** [GitHub](https://github.com/abhinand5/bangla-llama)
## Related Models
| Model | Type | Data | Base Model | # Params | Download Links |
|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
| Bangla LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) |
| Bangla LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) |
| Bangla LLaMA 7B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) |
| Bangla LLaMA 13B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 13B Base | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-instruct-v0.1) |
| Bangla LLaMA 3 8B Base | Base model | 12.4M | LLaMA 3 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3 8B Base | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.1 8B Base | Base model | 12.4M | LLaMA 3.1 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3.1 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.1 8B Base | 8b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.2 1B Base | Base model | 12.4M | LLaMA 3.2 1b | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3.2 1B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.2 1B Base | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.2 3B Instruct| Instruction following model | 172k instructions | Bangla LLaMA 3.2 3B Base | 3B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1)
## Usage Note
It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
## Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
- [Abdullah Khan Zehady](https://www.linkedin.com/in/abdullah-khan-zehady-915ba024/)
## Citation
We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Bangla language. |
Qixuanliang/distilbert-base-uncased-finetuned-emotion | Qixuanliang | 2024-10-20T05:31:22Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-20T03:04:25Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2154
- Accuracy: 0.927
- F1: 0.9267
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8567 | 1.0 | 250 | 0.3202 | 0.9045 | 0.9031 |
| 0.2559 | 2.0 | 500 | 0.2154 | 0.927 | 0.9267 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1
|
BanglaLLM/BanglaLLama-3.2-1b-unolp-culturax-base-v0.0.1 | BanglaLLM | 2024-10-20T05:28:56Z | 113 | 0 | null | [
"safetensors",
"llama",
"bangla",
"banglaLLM",
"banglaNLP",
"LLM",
"LLama",
"Transformer",
"nlp",
"bengali",
"bn",
"en",
"dataset:uonlp/CulturaX",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2024-10-04T21:54:31Z | ---
language:
- bn
- en
license: llama3.2
base_model:
- meta-llama/Llama-3.2-1B
datasets:
- uonlp/CulturaX
tags:
- bangla
- banglaLLM
- banglaNLP
- LLM
- LLama
- Transformer
- nlp
- bengali
---
# Bangla LLaMA-3.2 1B unolp/Culturax Base v0.1 [pre-trained]
Welcome to the inaugural release of the Bangla LLaMA-3.2 1B unolp-culturax base model – an important step in advancing LLMs for the Bangla language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks.
> **Please Note:** This model, labeled as a foundational Bangla Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes.
## Model description
- **Model type:** A 1B parameter model for Causal LM pre-trained on unolp/culturax (subset: bn) dataset.
- **Language(s):** Bangla and English
- **License:** GNU General Public License v3.0
- **Source Model:** [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
- **Training Precision:** `float16`
- **Code:** [GitHub](https://github.com/abhinand5/bangla-llama)
## Related Models
| Model | Type | Data | Base Model | # Params | Download Links |
|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
| Bangla LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) |
| Bangla LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) |
| Bangla LLaMA 7B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) |
| Bangla LLaMA 13B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 13B Base | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-instruct-v0.1) |
| Bangla LLaMA 3 8B Base | Base model | 12.4M | LLaMA 3 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3 8B Base | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.1 8B Base | Base model | 12.4M | LLaMA 3.1 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-unolp-culturax-base-v0.0.1)
| Bangla LLaMA 3.1 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.1 8B Base | 8b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.2 3B Base | Base model | 12.4M | LLaMA 3.2 1b | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-3b-unolp-culturax-base-v0.0.3)
| Bangla LLaMA 3.2 1B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.2 1B Base | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-bangla-alpaca-orca-instruct-v0.0.1)
| Bangla LLaMA 3.2 3B Instruct| Instruction following model | 172k instructions | Bangla LLaMA 3.2 3B Base | 3B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1)
## Usage Note
It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
## Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
- [Abdullah Khan Zehady](https://www.linkedin.com/in/abdullah-khan-zehady-915ba024/)
## Citation
We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Bangla language. |
oksjjj/distilbert-base-uncased-distilled-clinc | oksjjj | 2024-10-20T05:26:14Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-20T05:15:53Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- 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. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0436
- Accuracy: 0.9306
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 0.4282 | 0.6787 |
| 0.6659 | 2.0 | 636 | 0.1524 | 0.8426 |
| 0.6659 | 3.0 | 954 | 0.0824 | 0.8952 |
| 0.1761 | 4.0 | 1272 | 0.0603 | 0.9184 |
| 0.091 | 5.0 | 1590 | 0.0513 | 0.9242 |
| 0.091 | 6.0 | 1908 | 0.0467 | 0.9287 |
| 0.0703 | 7.0 | 2226 | 0.0444 | 0.9316 |
| 0.0633 | 8.0 | 2544 | 0.0436 | 0.9306 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
|
mlx-community/CalmeRys-78B-Orpo-v0.1-4bit | mlx-community | 2024-10-20T05:19:11Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"orpo",
"sft",
"chatml",
"mlx",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"base_model:dfurman/CalmeRys-78B-Orpo-v0.1",
"base_model:quantized:dfurman/CalmeRys-78B-Orpo-v0.1",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | text-generation | 2024-10-20T05:04:19Z | ---
language:
- en
license: mit
library_name: transformers
tags:
- orpo
- qwen2
- sft
- chatml
- mlx
base_model: dfurman/CalmeRys-78B-Orpo-v0.1
datasets:
- mlabonne/orpo-dpo-mix-40k
pipeline_tag: text-generation
inference: false
model_creator: dfurman
quantized_by: dfurman
model-index:
- name: CalmeRys-78B-Orpo-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 81.63
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 61.92
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 37.92
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 20.02
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 36.37
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.8
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
---
# mlx-community/CalmeRys-78B-Orpo-v0.1-4bit
The Model [mlx-community/CalmeRys-78B-Orpo-v0.1-4bit](https://huggingface.co/mlx-community/CalmeRys-78B-Orpo-v0.1-4bit) was converted to MLX format from [dfurman/CalmeRys-78B-Orpo-v0.1](https://huggingface.co/dfurman/CalmeRys-78B-Orpo-v0.1) using mlx-lm version **0.19.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/CalmeRys-78B-Orpo-v0.1-4bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
prithivMLmods/Canopus-LoRA-Flux-Typography-ASCII | prithivMLmods | 2024-10-20T05:12:47Z | 63 | 9 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-10-17T09:07:36Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: 'Typography, ASCII Art : A black and white monochrome photograph of a mans face. The man head is covered in words, including "Portrait by Ralph Ueltzhoeffer" in the lower right corner of the frame. The background of the photograph is black, creating a stark contrast with the man silhouette. The text is written in a crayon type type, adding a touch of depth to the image.'
output:
url: >-
images/workspace_trainsamples_781100400605468413_62188401-f296-4d6d-9f0d-6979aa4282a5.png
- text: 'Typography, ASCII Art : A black and white monochrome photograph of a mans face. The man head is covered in words, including "Portrait by Ralph Ueltzhoeffer" in the lower right corner of the frame. The background of the photograph is black, creating a stark contrast with the man silhouette. The text is written in a crayon type type, adding a touch of depth to the image.'
output:
url: >-
images/MMM.png
- text: 'A black and white photograph of a young woman, her face rendered from bold, overlapping words. The words cover her face in a chaotic yet deliberate manner, spelling out "Fragments of Thought." The background is a deep charcoal gray, subtly blending into the edges of her silhouette. The font has a rough, textured brushstroke style, giving the image an edgy and dynamic quality.'
output:
url: >-
images/NNN.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Typography, ASCII Art
license: creativeml-openrail-m
---
# Canopus-LoRA-Flux-Typography-ASCII
<Gallery />
**The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.**
## Model description
**prithivMLmods/Canopus-LoRA-Flux-Typography-ASCII**
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 23 & 1K+ |
| Epoch | 15 | Save Every N Epochs | 1 |
Labeling: wd-v1-4-vit-tagger-v2
Total Images Used for Training : 30+ [ Hi-RES ] & More ...............
## Trigger prompts
Typography, ASCII Art : A black and white monochrome photograph of a man's face. The man's head is covered in words, including "Portrait by Ralph Ueltzhoeffer" in the lower right corner of the frame. The background of the photograph is black, creating a stark contrast with the man's silhouette. The text is written in a crayon type type, adding a touch of depth to the image.
Typography, ASCII Art: A grayscale, monochrome portrait of a woman’s face. The face is constructed entirely from delicate, hand-drawn lines of text. Phrases like "Echoes of Silence" appear throughout the image, with the text written in a rough, handwritten font style. The woman's expression is serene, while the background remains pitch black to amplify the contrast between the text and the face. The words fade in and out of her silhouette, blending seamlessly with the contours of her features, creating an almost ethereal, abstract effect.
Typography, ASCII Art: A black-and-white monochrome image of a man in profile, with his face outlined by intricately layered words. The words "Time Stands Still" and "Art by David Carson" are woven into the contours of his features. The text is styled in a distressed, stencil-like typeface, giving the piece a raw, edgy feel. The background is entirely black, making the white text stand out, with some words blending softly into the edges of the man's silhouette, creating a haunting, dreamlike effect. The typography adds texture, merging with the photograph to form a striking visual portrait.
| Parameter | Value |
|-----------------|---------------------------------------------------------------------------------------|
| Prompt | Typography, ASCII Art : A black and white monochrome photograph of a man's face. The man's head is covered in words, including "Portrait by Ralph Ueltzhoeffer" in the lower right corner of the frame. The background of the photograph is black, creating a stark contrast with the man's silhouette. The text is written in a crayon type type, adding a touch of depth to the image. |
| Sampler | euler |
## Setting Up
```
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Canopus-LoRA-Flux-Typography-ASCII"
trigger_word = "Realism" # Leave trigger_word blank if not used.
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## app.py
```
import gradio as gr
gr.load("models/prithivMLmods/Canopus-LoRA-Flux-Typography-ASCII").launch()
```
## pythonproject.py
```
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import gradio as gr
def image_generator(prompt):
pass
interface = gr.Interface(fn=image_generator, inputs="text", outputs="image")
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app = gr.mount_gradio_app(app, interface, path="/")
```
## Trigger words
You should use `Typography` to trigger the image generation.
You should use `ASCII Art` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/prithivMLmods/Canopus-LoRA-Flux-Typography-ASCII/tree/main) them in the Files & versions tab.
|
Mahe95/distilbert-base-uncased-finetuend-emotion | Mahe95 | 2024-10-20T05:05:04Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-20T04:09:51Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuend-emotion
results: []
---
<!-- 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. -->
# distilbert-base-uncased-finetuend-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1640
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0908 | 1.0 | 250 | 0.1789 |
| 0.0711 | 2.0 | 500 | 0.1640 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
utischoolnlp/Polyverse-1.3B-256-16-random | utischoolnlp | 2024-10-20T05:03:11Z | 49 | 0 | transformers | [
"transformers",
"safetensors",
"Polyverse",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-20T04:43:18Z | ---
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]
- **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] |
prithivMLmods/Kepler-452b-LoRA-Flux-Dev-3D-Bubbly | prithivMLmods | 2024-10-20T05:01:32Z | 67 | 11 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"Fluid texture",
"3D-Bubbly",
"Flux",
"Flux-Dev",
"LoRA",
"SDXL",
"SD",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-10-19T12:44:25Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- Fluid texture
- 3D-Bubbly
- Flux
- Flux-Dev
- LoRA
- SDXL
- SD
widget:
- text: >-
Create a stunning wallpaper featuring a 3D bubbly-style Batman character in
his iconic black and grey suit, prominently displaying the yellow bat symbol
on his chest. The character should have his eyes closed in a relaxed pose,
with one hand raised as if signaling a gesture. Surround him with a glossy,
distorted beige background that has fluid-like waves, enhancing the surreal
effect. Above the character, incorporate the name "Batman" in a bold, black
comic-style font that matches the overall theme. The image should have a
surreal, melted appearance, giving it a dynamic, fluid texture that makes it
ideal for a wallpaper.
output:
url: images/1111.png
- text: >-
Create a stunning wallpaper featuring a 3D bubbly-style Tom and Jerry scene, showcasing both characters in a playful interaction. Tom should be depicted in his classic blue-gray color, looking surprised with wide eyes and a playful expression, while Jerry, the small brown mouse, should be in a cheeky pose, holding a piece of cheese with a mischievous grin. Surround them with a glossy, distorted beige background that has fluid-like waves, enhancing the surreal effect. Above the characters, incorporate the name "TOM AND JERRY" in a bold, black comic-style font that complements the lively theme. The image should have a surreal, melted appearance, giving it a dynamic, fluid texture that makes it ideal for a fun wallpaper.'
output:
url: images/2222.png
- text: >-
Create a stunning wallpaper featuring a 3D bubbly-style Deadpool character in his iconic red and black suit, vividly showcasing his playful and irreverent personality. The character should be in a dynamic fighting pose, with his eyes narrowed and a cheeky grin, wielding dual katanas ready for action. Surround him with a glossy, distorted red and black background that has fluid-like waves, enhancing the chaotic and intense atmosphere. Above the character, incorporate the name "DEADPOOL" in a bold, white comic-style font with splatters of red, matching the overall theme. The image should have a surreal, melted appearance, giving it a dynamic, fluid texture that makes it ideal for a vibrant and action-packed wallpaper.'
output:
url: images/3333.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: 3d bubbly
license: creativeml-openrail-m
---
# Kepler-452b-LoRA-Flux-Dev-3D-Bubbly
<Gallery />
**The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.**
## Model description
**prithivMLmods/Kepler-452b-LoRA-Flux-Dev-3D-Bubbly**
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 25 & 1K+ |
| Epoch | 20 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 50+ [ Hi-RES ] & More ...............
& More ...
## Trigger prompts
Create a stunning wallpaper featuring a 3D bubbly-style Batman character in his iconic black and grey suit, prominently displaying the yellow bat symbol on his chest. The character should have his eyes closed in a relaxed pose, with one hand raised as if signaling a gesture. Surround him with a glossy, distorted beige background that has fluid-like waves, enhancing the surreal effect. Above the character, incorporate the name "Batman" in a bold, black comic-style font that matches the overall theme. The image should have a surreal, melted appearance, giving it a dynamic, fluid texture that makes it ideal for a wallpaper.
Create a stunning wallpaper featuring a 3D bubbly-style Tom and Jerry scene, showcasing both characters in a playful interaction. Tom should be depicted in his classic blue-gray color, looking surprised with wide eyes and a playful expression, while Jerry, the small brown mouse, should be in a cheeky pose, holding a piece of cheese with a mischievous grin. Surround them with a glossy, distorted beige background that has fluid-like waves, enhancing the surreal effect. Above the characters, incorporate the name "TOM AND JERRY" in a bold, black comic-style font that complements the lively theme. The image should have a surreal, melted appearance, giving it a dynamic, fluid texture that makes it ideal for a fun wallpaper.
Create a stunning wallpaper featuring a 3D bubbly-style Deadpool character in his iconic red and black suit, vividly showcasing his playful and irreverent personality. The character should be in a dynamic fighting pose, with his eyes narrowed and a cheeky grin, wielding dual katanas ready for action. Surround him with a glossy, distorted red and black background that has fluid-like waves, enhancing the chaotic and intense atmosphere. Above the character, incorporate the name "DEADPOOL" in a bold, white comic-style font with splatters of red, matching the overall theme. The image should have a surreal, melted appearance, giving it a dynamic, fluid texture that makes it ideal for a vibrant and action-packed wallpaper.
| Parameter | Value |
|-----------------|---------------------------------------------------------------------------------------|
| Prompt | Create a stunning wallpaper featuring a 3D bubbly-style Deadpool character in his iconic red and black suit, vividly showcasing his playful and irreverent personality. The character should be in a dynamic fighting pose, with his eyes narrowed and a cheeky grin, wielding dual katanas ready for action. Surround him with a glossy, distorted red and black background that has fluid-like waves, enhancing the chaotic and intense atmosphere. Above the character, incorporate the name "DEADPOOL" in a bold, white comic-style font with splatters of red, matching the overall theme. The image should have a surreal, melted appearance, giving it a dynamic, fluid texture that makes it ideal for a vibrant and action-packed wallpaper.' |
| Sampler | euler |
## Setting Up
```
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Kepler-452b-LoRA-Flux-Dev-3D-Bubbly"
trigger_word = "3d bubbly, Fluid texture " # Leave trigger_word blank if not used.
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## Simple Gradio Implementation
## app.py
```
import gradio as gr
gr.load("models/prithivMLmods/Kepler-452b-LoRA-Flux-Dev-3D-Bubbly").launch()
```
## pythonproject.py
```
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import gradio as gr
def image_generator(prompt):
pass
interface = gr.Interface(fn=image_generator, inputs="text", outputs="image")
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app = gr.mount_gradio_app(app, interface, path="/")
```
## App File Structure
/project-root/
│
├── .gitattributes
├── README.md
├── app.py
├── pythonproject.py
│
## Trigger words
You should use `3d bubbly`, `Fluid texture ` to trigger the image generation.
## Similar/Older Variants
[prithivMLmods/Canopus-Pixar-Art](https://huggingface.co/prithivMLmods/Canopus-Pixar-Art)
[prithivMLmods/Canopus-Pixar-3D-Flux-LoRA](https://huggingface.co/prithivMLmods/Canopus-Pixar-3D-Flux-LoRA)
## Download model based on Flux-Dev
Weights for this model are available in Safetensors format.
[Download](/prithivMLmods/Kepler-452b-LoRA-Flux-Dev-3D-Bubbly/tree/main) them in the Files & versions tab.
## Download LoRA based on SDXL
Weights for this model are available in Safetensors format.
[Download](/https://huggingface.co/prithivMLmods/Kepler-452b-LoRA-Flux-Dev-3D-Bubbly/tree/main/SDXL) them in the Files & versions tab. |
oksjjj/distilbert-base-uncased-finetuned-clinc | oksjjj | 2024-10-20T04:57:44Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-20T02:11:27Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7872
- Accuracy: 0.9206
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2931 | 0.7255 |
| 3.8009 | 2.0 | 636 | 1.8849 | 0.8526 |
| 3.8009 | 3.0 | 954 | 1.1702 | 0.8897 |
| 1.7128 | 4.0 | 1272 | 0.8717 | 0.9145 |
| 0.9206 | 5.0 | 1590 | 0.7872 | 0.9206 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
|
DivineNnamdi/jina-embeddings-v3 | DivineNnamdi | 2024-10-20T04:40:40Z | 122 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"feature-extraction",
"sentence-similarity",
"mteb",
"sentence-transformers",
"custom_code",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2409.10173",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | feature-extraction | 2024-10-20T02:41:09Z | ---
license: cc-by-nc-4.0
tags:
- feature-extraction
- sentence-similarity
- mteb
- sentence-transformers
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
inference: false
library_name: transformers
model-index:
- name: jina-embeddings-v3
results:
- dataset:
config: default
name: MTEB AFQMC (default)
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cosine_pearson
value: 41.74237700998808
- type: cosine_spearman
value: 43.4726782647566
- type: euclidean_pearson
value: 42.244585459479964
- type: euclidean_spearman
value: 43.525070045169606
- type: main_score
value: 43.4726782647566
- type: manhattan_pearson
value: 42.04616728224863
- type: manhattan_spearman
value: 43.308828270754645
- type: pearson
value: 41.74237700998808
- type: spearman
value: 43.4726782647566
task:
type: STS
- dataset:
config: default
name: MTEB ArguAna-PL (default)
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
metrics:
- type: main_score
value: 50.117999999999995
- type: map_at_1
value: 24.253
- type: map_at_10
value: 40.725
- type: map_at_100
value: 41.699999999999996
- type: map_at_1000
value: 41.707
- type: map_at_20
value: 41.467999999999996
- type: map_at_3
value: 35.467
- type: map_at_5
value: 38.291
- type: mrr_at_1
value: 24.751066856330013
- type: mrr_at_10
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- type: precision_at_20
value: 73.0
- type: precision_at_3
value: 79.333
- type: precision_at_5
value: 79.2
- type: recall_at_1
value: 0.214
- type: recall_at_10
value: 1.9980000000000002
- type: recall_at_100
value: 13.328999999999999
- type: recall_at_1000
value: 47.204
- type: recall_at_20
value: 3.7310000000000003
- type: recall_at_3
value: 0.628
- type: recall_at_5
value: 1.049
task:
type: Retrieval
- dataset:
config: default
name: MTEB CEDRClassification (default)
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
split: test
type: ai-forever/cedr-classification
metrics:
- type: accuracy
value: 47.30605738575983
- type: f1
value: 41.26091043925065
- type: lrap
value: 72.89452709883206
- type: main_score
value: 47.30605738575983
task:
type: MultilabelClassification
- dataset:
config: ru
name: MTEB MIRACLReranking (ru)
revision: 6d1962c527217f8927fca80f890f14f36b2802af
split: dev
type: miracl/mmteb-miracl-reranking
metrics:
- type: MAP@1(MIRACL)
value: 20.721999999999998
- type: MAP@10(MIRACL)
value: 33.900999999999996
- type: MAP@100(MIRACL)
value: 36.813
- type: MAP@1000(MIRACL)
value: 36.813
- type: MAP@20(MIRACL)
value: 35.684
- type: MAP@3(MIRACL)
value: 28.141
- type: MAP@5(MIRACL)
value: 31.075000000000003
- type: NDCG@1(MIRACL)
value: 32.799
- type: NDCG@10(MIRACL)
value: 42.065000000000005
- type: NDCG@100(MIRACL)
value: 49.730999999999995
- type: NDCG@1000(MIRACL)
value: 49.730999999999995
- type: NDCG@20(MIRACL)
value: 46.0
- type: NDCG@3(MIRACL)
value: 34.481
- type: NDCG@5(MIRACL)
value: 37.452999999999996
- type: P@1(MIRACL)
value: 32.799
- type: P@10(MIRACL)
value: 11.668000000000001
- type: P@100(MIRACL)
value: 1.9529999999999998
- type: P@1000(MIRACL)
value: 0.19499999999999998
- type: P@20(MIRACL)
value: 7.51
- type: P@3(MIRACL)
value: 20.823
- type: P@5(MIRACL)
value: 16.728
- type: Recall@1(MIRACL)
value: 20.721999999999998
- type: Recall@10(MIRACL)
value: 54.762
- type: Recall@100(MIRACL)
value: 79.952
- type: Recall@1000(MIRACL)
value: 79.952
- type: Recall@20(MIRACL)
value: 66.26100000000001
- type: Recall@3(MIRACL)
value: 34.410000000000004
- type: Recall@5(MIRACL)
value: 42.659000000000006
- type: main_score
value: 42.065000000000005
- type: nAUC_MAP@1000_diff1(MIRACL)
value: 14.33534992502818
- type: nAUC_MAP@1000_max(MIRACL)
value: 12.367998764646115
- type: nAUC_MAP@1000_std(MIRACL)
value: 4.569686002935006
- type: nAUC_MAP@100_diff1(MIRACL)
value: 14.33534992502818
- type: nAUC_MAP@100_max(MIRACL)
value: 12.367998764646115
- type: nAUC_MAP@100_std(MIRACL)
value: 4.569686002935006
- type: nAUC_MAP@10_diff1(MIRACL)
value: 16.920323975680027
- type: nAUC_MAP@10_max(MIRACL)
value: 9.327171297204082
- type: nAUC_MAP@10_std(MIRACL)
value: 3.2039133783079015
- type: nAUC_MAP@1_diff1(MIRACL)
value: 28.698973487482206
- type: nAUC_MAP@1_max(MIRACL)
value: 2.9217687660885034
- type: nAUC_MAP@1_std(MIRACL)
value: -1.1247408800976524
- type: nAUC_MAP@20_diff1(MIRACL)
value: 15.359083081640476
- type: nAUC_MAP@20_max(MIRACL)
value: 11.310494233946345
- type: nAUC_MAP@20_std(MIRACL)
value: 4.4171898386022885
- type: nAUC_MAP@3_diff1(MIRACL)
value: 22.27430591851617
- type: nAUC_MAP@3_max(MIRACL)
value: 6.407438291284658
- type: nAUC_MAP@3_std(MIRACL)
value: 0.9799184530397409
- type: nAUC_MAP@5_diff1(MIRACL)
value: 19.20571689941054
- type: nAUC_MAP@5_max(MIRACL)
value: 7.987468654026893
- type: nAUC_MAP@5_std(MIRACL)
value: 1.8324246565938962
- type: nAUC_NDCG@1000_diff1(MIRACL)
value: 3.7537669018914768
- type: nAUC_NDCG@1000_max(MIRACL)
value: 20.7944707840533
- type: nAUC_NDCG@1000_std(MIRACL)
value: 8.444837055303063
- type: nAUC_NDCG@100_diff1(MIRACL)
value: 3.7537669018914768
- type: nAUC_NDCG@100_max(MIRACL)
value: 20.7944707840533
- type: nAUC_NDCG@100_std(MIRACL)
value: 8.444837055303063
- type: nAUC_NDCG@10_diff1(MIRACL)
value: 10.829575656103888
- type: nAUC_NDCG@10_max(MIRACL)
value: 13.0445496498929
- type: nAUC_NDCG@10_std(MIRACL)
value: 6.050412212625362
- type: nAUC_NDCG@1_diff1(MIRACL)
value: 19.1388712233292
- type: nAUC_NDCG@1_max(MIRACL)
value: 10.871900994781642
- type: nAUC_NDCG@1_std(MIRACL)
value: 3.218568248751811
- type: nAUC_NDCG@20_diff1(MIRACL)
value: 7.093172181746442
- type: nAUC_NDCG@20_max(MIRACL)
value: 16.955238078958836
- type: nAUC_NDCG@20_std(MIRACL)
value: 8.325656379573035
- type: nAUC_NDCG@3_diff1(MIRACL)
value: 17.134437303330802
- type: nAUC_NDCG@3_max(MIRACL)
value: 10.235328822955793
- type: nAUC_NDCG@3_std(MIRACL)
value: 3.2341358691084814
- type: nAUC_NDCG@5_diff1(MIRACL)
value: 14.733664618337636
- type: nAUC_NDCG@5_max(MIRACL)
value: 11.181897412035282
- type: nAUC_NDCG@5_std(MIRACL)
value: 3.642277088791985
- type: nAUC_P@1000_diff1(MIRACL)
value: -26.330038284867573
- type: nAUC_P@1000_max(MIRACL)
value: 28.450694137240458
- type: nAUC_P@1000_std(MIRACL)
value: 9.892993775474912
- type: nAUC_P@100_diff1(MIRACL)
value: -26.330038284867552
- type: nAUC_P@100_max(MIRACL)
value: 28.45069413724051
- type: nAUC_P@100_std(MIRACL)
value: 9.892993775474928
- type: nAUC_P@10_diff1(MIRACL)
value: -17.436937353231112
- type: nAUC_P@10_max(MIRACL)
value: 24.327018012947857
- type: nAUC_P@10_std(MIRACL)
value: 11.78803527706634
- type: nAUC_P@1_diff1(MIRACL)
value: 19.1388712233292
- type: nAUC_P@1_max(MIRACL)
value: 10.871900994781642
- type: nAUC_P@1_std(MIRACL)
value: 3.218568248751811
- type: nAUC_P@20_diff1(MIRACL)
value: -22.947528755272426
- type: nAUC_P@20_max(MIRACL)
value: 27.773093471902538
- type: nAUC_P@20_std(MIRACL)
value: 14.898619107087221
- type: nAUC_P@3_diff1(MIRACL)
value: 1.4100426412400944
- type: nAUC_P@3_max(MIRACL)
value: 17.397472872058845
- type: nAUC_P@3_std(MIRACL)
value: 8.240008229861875
- type: nAUC_P@5_diff1(MIRACL)
value: -7.971349332207021
- type: nAUC_P@5_max(MIRACL)
value: 22.198441167940963
- type: nAUC_P@5_std(MIRACL)
value: 9.00265164460082
- type: nAUC_Recall@1000_diff1(MIRACL)
value: -38.69835271863148
- type: nAUC_Recall@1000_max(MIRACL)
value: 50.9545152809108
- type: nAUC_Recall@1000_std(MIRACL)
value: 20.44270887092116
- type: nAUC_Recall@100_diff1(MIRACL)
value: -38.69835271863148
- type: nAUC_Recall@100_max(MIRACL)
value: 50.9545152809108
- type: nAUC_Recall@100_std(MIRACL)
value: 20.44270887092116
- type: nAUC_Recall@10_diff1(MIRACL)
value: -0.08109036309433801
- type: nAUC_Recall@10_max(MIRACL)
value: 12.696619907773568
- type: nAUC_Recall@10_std(MIRACL)
value: 8.791982704261589
- type: nAUC_Recall@1_diff1(MIRACL)
value: 28.698973487482206
- type: nAUC_Recall@1_max(MIRACL)
value: 2.9217687660885034
- type: nAUC_Recall@1_std(MIRACL)
value: -1.1247408800976524
- type: nAUC_Recall@20_diff1(MIRACL)
value: -13.312171017942623
- type: nAUC_Recall@20_max(MIRACL)
value: 24.19847346821666
- type: nAUC_Recall@20_std(MIRACL)
value: 15.8157702609797
- type: nAUC_Recall@3_diff1(MIRACL)
value: 16.909128321353343
- type: nAUC_Recall@3_max(MIRACL)
value: 6.552122731902991
- type: nAUC_Recall@3_std(MIRACL)
value: 1.9963898223457228
- type: nAUC_Recall@5_diff1(MIRACL)
value: 9.990292655247721
- type: nAUC_Recall@5_max(MIRACL)
value: 9.361722273507574
- type: nAUC_Recall@5_std(MIRACL)
value: 3.270918827854495
task:
type: Reranking
- dataset:
config: default
name: MTEB SensitiveTopicsClassification (default)
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
split: test
type: ai-forever/sensitive-topics-classification
metrics:
- type: accuracy
value: 30.634765625
- type: f1
value: 32.647559808678665
- type: lrap
value: 45.94319661458259
- type: main_score
value: 30.634765625
task:
type: MultilabelClassification
- dataset:
config: default
name: MTEB ATEC (default)
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
metrics:
- type: cosine_pearson
value: 47.541497334563296
- type: cosine_spearman
value: 49.06268944206629
- type: euclidean_pearson
value: 51.838926748581635
- type: euclidean_spearman
value: 48.930697157135356
- type: main_score
value: 49.06268944206629
- type: manhattan_pearson
value: 51.835306769406365
- type: manhattan_spearman
value: 48.86135493444834
- type: pearson
value: 47.541497334563296
- type: spearman
value: 49.06268944206629
task:
type: STS
- dataset:
config: default
name: MTEB AllegroReviews (default)
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 49.51292246520874
- type: f1
value: 44.14350234332397
- type: f1_weighted
value: 51.65508998354552
- type: main_score
value: 49.51292246520874
task:
type: Classification
- dataset:
config: default
name: MTEB AlloProfClusteringP2P (default)
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: main_score
value: 63.883383458621665
- type: v_measure
value: 63.883383458621665
- type: v_measure_std
value: 2.693666879958465
task:
type: Clustering
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 43.657212124525546
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S (default)
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: main_score
value: 46.85924588755251
- type: v_measure
value: 46.85924588755251
- type: v_measure_std
value: 2.1918258880872377
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking (default)
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map
value: 66.39013753839347
- type: mrr
value: 67.68045617786551
- type: main_score
value: 66.39013753839347
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval (default)
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
split: test
type: lyon-nlp/alloprof
metrics:
- type: main_score
value: 54.284
- type: map_at_1
value: 37.047000000000004
- type: map_at_10
value: 48.53
- type: map_at_100
value: 49.357
- type: map_at_1000
value: 49.39
- type: map_at_20
value: 49.064
- type: map_at_3
value: 45.675
- type: map_at_5
value: 47.441
- type: mrr_at_1
value: 37.04663212435233
- type: mrr_at_10
value: 48.5300326232969
- type: mrr_at_100
value: 49.35708199037581
- type: mrr_at_1000
value: 49.39005824603193
- type: mrr_at_20
value: 49.06417416464799
- type: mrr_at_3
value: 45.67501439263105
- type: mrr_at_5
value: 47.44099021301103
- type: nauc_map_at_1000_diff1
value: 43.32474221868009
- type: nauc_map_at_1000_max
value: 39.407334029058575
- type: nauc_map_at_1000_std
value: -2.3728154448932606
- type: nauc_map_at_100_diff1
value: 43.32336300929909
- type: nauc_map_at_100_max
value: 39.432174777554835
- type: nauc_map_at_100_std
value: -2.356396922384349
- type: nauc_map_at_10_diff1
value: 43.1606520154482
- type: nauc_map_at_10_max
value: 39.33734650558226
- type: nauc_map_at_10_std
value: -2.5156222475075256
- type: nauc_map_at_1_diff1
value: 46.2178975214499
- type: nauc_map_at_1_max
value: 36.26173199049361
- type: nauc_map_at_1_std
value: -3.0897555582816443
- type: nauc_map_at_20_diff1
value: 43.272980702916456
- type: nauc_map_at_20_max
value: 39.4896977052276
- type: nauc_map_at_20_std
value: -2.3305501742917043
- type: nauc_map_at_3_diff1
value: 43.49525042967079
- type: nauc_map_at_3_max
value: 38.66352501824728
- type: nauc_map_at_3_std
value: -3.202794391620473
- type: nauc_map_at_5_diff1
value: 43.2266692546611
- type: nauc_map_at_5_max
value: 38.77368661115743
- type: nauc_map_at_5_std
value: -3.0897532130127954
- type: nauc_mrr_at_1000_diff1
value: 43.32474221868009
- type: nauc_mrr_at_1000_max
value: 39.407334029058575
- type: nauc_mrr_at_1000_std
value: -2.3728154448932606
- type: nauc_mrr_at_100_diff1
value: 43.32336300929909
- type: nauc_mrr_at_100_max
value: 39.432174777554835
- type: nauc_mrr_at_100_std
value: -2.356396922384349
- type: nauc_mrr_at_10_diff1
value: 43.1606520154482
- type: nauc_mrr_at_10_max
value: 39.33734650558226
- type: nauc_mrr_at_10_std
value: -2.5156222475075256
- type: nauc_mrr_at_1_diff1
value: 46.2178975214499
- type: nauc_mrr_at_1_max
value: 36.26173199049361
- type: nauc_mrr_at_1_std
value: -3.0897555582816443
- type: nauc_mrr_at_20_diff1
value: 43.272980702916456
- type: nauc_mrr_at_20_max
value: 39.4896977052276
- type: nauc_mrr_at_20_std
value: -2.3305501742917043
- type: nauc_mrr_at_3_diff1
value: 43.49525042967079
- type: nauc_mrr_at_3_max
value: 38.66352501824728
- type: nauc_mrr_at_3_std
value: -3.202794391620473
- type: nauc_mrr_at_5_diff1
value: 43.2266692546611
- type: nauc_mrr_at_5_max
value: 38.77368661115743
- type: nauc_mrr_at_5_std
value: -3.0897532130127954
- type: nauc_ndcg_at_1000_diff1
value: 43.01903168202974
- type: nauc_ndcg_at_1000_max
value: 40.75496622942232
- type: nauc_ndcg_at_1000_std
value: -1.3150412981845496
- type: nauc_ndcg_at_100_diff1
value: 42.98016493758145
- type: nauc_ndcg_at_100_max
value: 41.55869635162325
- type: nauc_ndcg_at_100_std
value: -0.5355252976886055
- type: nauc_ndcg_at_10_diff1
value: 42.218755211347506
- type: nauc_ndcg_at_10_max
value: 41.305042275175765
- type: nauc_ndcg_at_10_std
value: -1.4034484444573714
- type: nauc_ndcg_at_1_diff1
value: 46.2178975214499
- type: nauc_ndcg_at_1_max
value: 36.26173199049361
- type: nauc_ndcg_at_1_std
value: -3.0897555582816443
- type: nauc_ndcg_at_20_diff1
value: 42.66574440095576
- type: nauc_ndcg_at_20_max
value: 42.014620115124515
- type: nauc_ndcg_at_20_std
value: -0.5176162553751498
- type: nauc_ndcg_at_3_diff1
value: 42.837450505106055
- type: nauc_ndcg_at_3_max
value: 39.525369733082414
- type: nauc_ndcg_at_3_std
value: -3.1605948245795155
- type: nauc_ndcg_at_5_diff1
value: 42.37951815451173
- type: nauc_ndcg_at_5_max
value: 39.78840132935179
- type: nauc_ndcg_at_5_std
value: -2.936898430768135
- type: nauc_precision_at_1000_diff1
value: 49.69224988612385
- type: nauc_precision_at_1000_max
value: 79.57897547128005
- type: nauc_precision_at_1000_std
value: 45.040371354764645
- type: nauc_precision_at_100_diff1
value: 42.70597486048422
- type: nauc_precision_at_100_max
value: 65.74628759606188
- type: nauc_precision_at_100_std
value: 25.49157745244855
- type: nauc_precision_at_10_diff1
value: 38.565609931689345
- type: nauc_precision_at_10_max
value: 50.0239696180852
- type: nauc_precision_at_10_std
value: 3.976354829503967
- type: nauc_precision_at_1_diff1
value: 46.2178975214499
- type: nauc_precision_at_1_max
value: 36.26173199049361
- type: nauc_precision_at_1_std
value: -3.0897555582816443
- type: nauc_precision_at_20_diff1
value: 40.4134718566864
- type: nauc_precision_at_20_max
value: 57.121778108665374
- type: nauc_precision_at_20_std
value: 11.46021975428544
- type: nauc_precision_at_3_diff1
value: 40.90538379461529
- type: nauc_precision_at_3_max
value: 42.18393248057992
- type: nauc_precision_at_3_std
value: -3.005249943837297
- type: nauc_precision_at_5_diff1
value: 39.60162965860782
- type: nauc_precision_at_5_max
value: 43.28317158174058
- type: nauc_precision_at_5_std
value: -2.3469094487738054
- type: nauc_recall_at_1000_diff1
value: 49.69224988612252
- type: nauc_recall_at_1000_max
value: 79.57897547127862
- type: nauc_recall_at_1000_std
value: 45.04037135476256
- type: nauc_recall_at_100_diff1
value: 42.70597486048432
- type: nauc_recall_at_100_max
value: 65.74628759606213
- type: nauc_recall_at_100_std
value: 25.491577452448727
- type: nauc_recall_at_10_diff1
value: 38.56560993168935
- type: nauc_recall_at_10_max
value: 50.02396961808522
- type: nauc_recall_at_10_std
value: 3.9763548295040314
- type: nauc_recall_at_1_diff1
value: 46.2178975214499
- type: nauc_recall_at_1_max
value: 36.26173199049361
- type: nauc_recall_at_1_std
value: -3.0897555582816443
- type: nauc_recall_at_20_diff1
value: 40.41347185668637
- type: nauc_recall_at_20_max
value: 57.12177810866533
- type: nauc_recall_at_20_std
value: 11.460219754285431
- type: nauc_recall_at_3_diff1
value: 40.90538379461527
- type: nauc_recall_at_3_max
value: 42.18393248057989
- type: nauc_recall_at_3_std
value: -3.005249943837297
- type: nauc_recall_at_5_diff1
value: 39.601629658607784
- type: nauc_recall_at_5_max
value: 43.28317158174053
- type: nauc_recall_at_5_std
value: -2.3469094487738054
- type: ndcg_at_1
value: 37.047000000000004
- type: ndcg_at_10
value: 54.284
- type: ndcg_at_100
value: 58.34
- type: ndcg_at_1000
value: 59.303
- type: ndcg_at_20
value: 56.235
- type: ndcg_at_3
value: 48.503
- type: ndcg_at_5
value: 51.686
- type: precision_at_1
value: 37.047000000000004
- type: precision_at_10
value: 7.237
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 4.005
- type: precision_at_3
value: 18.898
- type: precision_at_5
value: 12.884
- type: recall_at_1
value: 37.047000000000004
- type: recall_at_10
value: 72.366
- type: recall_at_100
value: 91.408
- type: recall_at_1000
value: 99.136
- type: recall_at_20
value: 80.095
- type: recall_at_3
value: 56.693000000000005
- type: recall_at_5
value: 64.42099999999999
task:
type: Retrieval
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 89.49253731343283
- type: ap
value: 61.88098616359918
- type: ap_weighted
value: 61.88098616359918
- type: f1
value: 84.76516623679144
- type: f1_weighted
value: 89.92745276292968
- type: main_score
value: 89.49253731343283
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonCounterfactualClassification (de)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 89.61456102783727
- type: ap
value: 93.11816566733742
- type: ap_weighted
value: 93.11816566733742
- type: f1
value: 88.27635757733722
- type: f1_weighted
value: 89.82581568285453
- type: main_score
value: 89.61456102783727
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 95.3825
- type: ap
value: 93.393033869502
- type: ap_weighted
value: 93.393033869502
- type: f1
value: 95.38109007966307
- type: f1_weighted
value: 95.38109007966305
- type: main_score
value: 95.3825
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 49.768
- type: f1
value: 48.95084821944411
- type: f1_weighted
value: 48.9508482194441
- type: main_score
value: 49.768
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonReviewsClassification (de)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 48.071999999999996
- type: f1
value: 47.24171107487612
- type: f1_weighted
value: 47.24171107487612
- type: main_score
value: 48.071999999999996
task:
type: Classification
- dataset:
config: es
name: MTEB AmazonReviewsClassification (es)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 48.102000000000004
- type: f1
value: 47.27193805278696
- type: f1_weighted
value: 47.27193805278696
- type: main_score
value: 48.102000000000004
task:
type: Classification
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 47.30800000000001
- type: f1
value: 46.41683358017851
- type: f1_weighted
value: 46.41683358017851
- type: main_score
value: 47.30800000000001
task:
type: Classification
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 44.944
- type: f1
value: 44.223824487744395
- type: f1_weighted
value: 44.22382448774439
- type: main_score
value: 44.944
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: map_at_1
value: 29.232000000000003
- type: map_at_10
value: 45.117000000000004
- type: map_at_100
value: 45.977000000000004
- type: map_at_1000
value: 45.98
- type: map_at_20
value: 45.815
- type: map_at_3
value: 39.912
- type: map_at_5
value: 42.693
- type: mrr_at_1
value: 29.659000000000002
- type: mrr_at_10
value: 45.253
- type: mrr_at_100
value: 46.125
- type: mrr_at_1000
value: 46.129
- type: mrr_at_20
value: 45.964
- type: mrr_at_3
value: 40.043
- type: mrr_at_5
value: 42.870000000000005
- type: ndcg_at_1
value: 29.232000000000003
- type: ndcg_at_10
value: 54.327999999999996
- type: ndcg_at_100
value: 57.86
- type: ndcg_at_1000
value: 57.935
- type: ndcg_at_20
value: 56.794
- type: ndcg_at_3
value: 43.516
- type: ndcg_at_5
value: 48.512
- type: precision_at_1
value: 29.232000000000003
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.676
- type: precision_at_3
value: 17.994
- type: precision_at_5
value: 13.215
- type: recall_at_1
value: 29.232000000000003
- type: recall_at_10
value: 83.926
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 93.528
- type: recall_at_3
value: 53.983000000000004
- type: recall_at_5
value: 66.074
- type: main_score
value: 54.327999999999996
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 46.6636824632419
- type: v_measure
value: 46.6636824632419
- type: v_measure_std
value: 13.817129140714963
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 39.271141892800024
- type: v_measure
value: 39.271141892800024
- type: v_measure_std
value: 14.276782483454827
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 65.04363277324629
- type: mrr
value: 78.2372598162072
- type: main_score
value: 65.04363277324629
task:
type: Reranking
- dataset:
config: default
name: MTEB MindSmallReranking (default)
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 30.83
- type: main_score
value: 30.83
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 88.80382082011027
- type: cosine_spearman
value: 88.68876782169106
- type: euclidean_pearson
value: 87.00802890147176
- type: euclidean_spearman
value: 87.43211268192712
- type: main_score
value: 88.68876782169106
- type: manhattan_pearson
value: 87.14062537179474
- type: manhattan_spearman
value: 87.59115245033443
- type: pearson
value: 88.80382082011027
- type: spearman
value: 88.68876782169106
task:
type: STS
- dataset:
config: default
name: MTEB BQ (default)
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
metrics:
- type: cosine_pearson
value: 61.588006604878196
- type: cosine_spearman
value: 63.20615427154465
- type: euclidean_pearson
value: 61.818547092516496
- type: euclidean_spearman
value: 63.21558009151778
- type: main_score
value: 63.20615427154465
- type: manhattan_pearson
value: 61.665588158487616
- type: manhattan_spearman
value: 63.051544488238584
- type: pearson
value: 61.588006604878196
- type: spearman
value: 63.20615427154465
task:
type: STS
- dataset:
config: default
name: MTEB BSARDRetrieval (default)
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: main_score
value: 64.414
- type: map_at_1
value: 14.865
- type: map_at_10
value: 21.605
- type: map_at_100
value: 22.762
- type: map_at_1000
value: 22.854
- type: map_at_20
value: 22.259999999999998
- type: map_at_3
value: 20.119999999999997
- type: map_at_5
value: 20.931
- type: mrr_at_1
value: 14.864864864864865
- type: mrr_at_10
value: 21.605176605176606
- type: mrr_at_100
value: 22.7622306460065
- type: mrr_at_1000
value: 22.85383406410312
- type: mrr_at_20
value: 22.259528463088845
- type: mrr_at_3
value: 20.12012012012012
- type: mrr_at_5
value: 20.930930930930934
- type: nauc_map_at_1000_diff1
value: 17.486265968689338
- type: nauc_map_at_1000_max
value: 22.736799291688836
- type: nauc_map_at_1000_std
value: 9.831687441977147
- type: nauc_map_at_100_diff1
value: 17.50754492049086
- type: nauc_map_at_100_max
value: 22.77693662806787
- type: nauc_map_at_100_std
value: 9.853899509675395
- type: nauc_map_at_10_diff1
value: 17.42133968580952
- type: nauc_map_at_10_max
value: 22.45861793882279
- type: nauc_map_at_10_std
value: 8.964888472915938
- type: nauc_map_at_1_diff1
value: 19.433947086968093
- type: nauc_map_at_1_max
value: 24.75657047550517
- type: nauc_map_at_1_std
value: 15.122329157218505
- type: nauc_map_at_20_diff1
value: 17.429856756008785
- type: nauc_map_at_20_max
value: 22.438850987431017
- type: nauc_map_at_20_std
value: 9.172746012213558
- type: nauc_map_at_3_diff1
value: 18.218182689678475
- type: nauc_map_at_3_max
value: 23.57169444088667
- type: nauc_map_at_3_std
value: 10.464473559366356
- type: nauc_map_at_5_diff1
value: 18.6075342519133
- type: nauc_map_at_5_max
value: 23.308845973576673
- type: nauc_map_at_5_std
value: 9.364009996445652
- type: nauc_mrr_at_1000_diff1
value: 17.486265968689338
- type: nauc_mrr_at_1000_max
value: 22.736799291688836
- type: nauc_mrr_at_1000_std
value: 9.831687441977147
- type: nauc_mrr_at_100_diff1
value: 17.50754492049086
- type: nauc_mrr_at_100_max
value: 22.77693662806787
- type: nauc_mrr_at_100_std
value: 9.853899509675395
- type: nauc_mrr_at_10_diff1
value: 17.42133968580952
- type: nauc_mrr_at_10_max
value: 22.45861793882279
- type: nauc_mrr_at_10_std
value: 8.964888472915938
- type: nauc_mrr_at_1_diff1
value: 19.433947086968093
- type: nauc_mrr_at_1_max
value: 24.75657047550517
- type: nauc_mrr_at_1_std
value: 15.122329157218505
- type: nauc_mrr_at_20_diff1
value: 17.429856756008785
- type: nauc_mrr_at_20_max
value: 22.438850987431017
- type: nauc_mrr_at_20_std
value: 9.172746012213558
- type: nauc_mrr_at_3_diff1
value: 18.218182689678475
- type: nauc_mrr_at_3_max
value: 23.57169444088667
- type: nauc_mrr_at_3_std
value: 10.464473559366356
- type: nauc_mrr_at_5_diff1
value: 18.6075342519133
- type: nauc_mrr_at_5_max
value: 23.308845973576673
- type: nauc_mrr_at_5_std
value: 9.364009996445652
- type: nauc_ndcg_at_1000_diff1
value: 16.327871824135745
- type: nauc_ndcg_at_1000_max
value: 23.308241052911495
- type: nauc_ndcg_at_1000_std
value: 11.50905911184097
- type: nauc_ndcg_at_100_diff1
value: 16.676226744692773
- type: nauc_ndcg_at_100_max
value: 24.323253721240974
- type: nauc_ndcg_at_100_std
value: 11.952612443651557
- type: nauc_ndcg_at_10_diff1
value: 16.030325121764594
- type: nauc_ndcg_at_10_max
value: 21.306799242079542
- type: nauc_ndcg_at_10_std
value: 6.63359364302513
- type: nauc_ndcg_at_1_diff1
value: 19.433947086968093
- type: nauc_ndcg_at_1_max
value: 24.75657047550517
- type: nauc_ndcg_at_1_std
value: 15.122329157218505
- type: nauc_ndcg_at_20_diff1
value: 16.013173605999857
- type: nauc_ndcg_at_20_max
value: 21.607217260736576
- type: nauc_ndcg_at_20_std
value: 7.319482417138996
- type: nauc_ndcg_at_3_diff1
value: 17.97958548328493
- type: nauc_ndcg_at_3_max
value: 23.58346522810145
- type: nauc_ndcg_at_3_std
value: 9.392582854708314
- type: nauc_ndcg_at_5_diff1
value: 18.734733324685287
- type: nauc_ndcg_at_5_max
value: 23.273244317623742
- type: nauc_ndcg_at_5_std
value: 7.638611545253834
- type: nauc_precision_at_1000_diff1
value: 7.919843339380295
- type: nauc_precision_at_1000_max
value: 31.575386234270486
- type: nauc_precision_at_1000_std
value: 39.332224386769404
- type: nauc_precision_at_100_diff1
value: 15.018050960000052
- type: nauc_precision_at_100_max
value: 34.98209513759861
- type: nauc_precision_at_100_std
value: 26.970034484359022
- type: nauc_precision_at_10_diff1
value: 12.102191084210922
- type: nauc_precision_at_10_max
value: 18.112541150340675
- type: nauc_precision_at_10_std
value: 0.7358784689406018
- type: nauc_precision_at_1_diff1
value: 19.433947086968093
- type: nauc_precision_at_1_max
value: 24.75657047550517
- type: nauc_precision_at_1_std
value: 15.122329157218505
- type: nauc_precision_at_20_diff1
value: 12.018814361204328
- type: nauc_precision_at_20_max
value: 19.75123746049928
- type: nauc_precision_at_20_std
value: 3.012204650582264
- type: nauc_precision_at_3_diff1
value: 17.41375604940955
- type: nauc_precision_at_3_max
value: 23.699834627021037
- type: nauc_precision_at_3_std
value: 6.793486779050103
- type: nauc_precision_at_5_diff1
value: 19.194631963780257
- type: nauc_precision_at_5_max
value: 23.31708702442155
- type: nauc_precision_at_5_std
value: 3.4591358279667332
- type: nauc_recall_at_1000_diff1
value: 7.919843339380378
- type: nauc_recall_at_1000_max
value: 31.57538623427063
- type: nauc_recall_at_1000_std
value: 39.332224386769546
- type: nauc_recall_at_100_diff1
value: 15.018050960000085
- type: nauc_recall_at_100_max
value: 34.9820951375986
- type: nauc_recall_at_100_std
value: 26.97003448435901
- type: nauc_recall_at_10_diff1
value: 12.102191084210837
- type: nauc_recall_at_10_max
value: 18.112541150340594
- type: nauc_recall_at_10_std
value: 0.7358784689405188
- type: nauc_recall_at_1_diff1
value: 19.433947086968093
- type: nauc_recall_at_1_max
value: 24.75657047550517
- type: nauc_recall_at_1_std
value: 15.122329157218505
- type: nauc_recall_at_20_diff1
value: 12.01881436120429
- type: nauc_recall_at_20_max
value: 19.751237460499222
- type: nauc_recall_at_20_std
value: 3.0122046505822135
- type: nauc_recall_at_3_diff1
value: 17.413756049409503
- type: nauc_recall_at_3_max
value: 23.699834627020998
- type: nauc_recall_at_3_std
value: 6.793486779050083
- type: nauc_recall_at_5_diff1
value: 19.194631963780203
- type: nauc_recall_at_5_max
value: 23.3170870244215
- type: nauc_recall_at_5_std
value: 3.459135827966664
- type: ndcg_at_1
value: 14.865
- type: ndcg_at_10
value: 24.764
- type: ndcg_at_100
value: 30.861
- type: ndcg_at_1000
value: 33.628
- type: ndcg_at_20
value: 27.078000000000003
- type: ndcg_at_3
value: 21.675
- type: ndcg_at_5
value: 23.148
- type: precision_at_1
value: 14.865
- type: precision_at_10
value: 3.4680000000000004
- type: precision_at_100
value: 0.644
- type: precision_at_1000
value: 0.087
- type: precision_at_20
value: 2.185
- type: precision_at_3
value: 8.709
- type: precision_at_5
value: 5.946
- type: recall_at_1
value: 14.865
- type: recall_at_10
value: 34.685
- type: recall_at_100
value: 64.414
- type: recall_at_1000
value: 86.937
- type: recall_at_20
value: 43.694
- type: recall_at_3
value: 26.125999999999998
- type: recall_at_5
value: 29.73
task:
type: Retrieval
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 84.08116883116882
- type: f1
value: 84.05587055990273
- type: f1_weighted
value: 84.05587055990274
- type: main_score
value: 84.08116883116882
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: main_score
value: 38.1941007822277
- type: v_measure
value: 38.1941007822277
- type: v_measure_std
value: 0.7502113547288178
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: main_score
value: 34.42075599178318
- type: v_measure
value: 34.42075599178318
- type: v_measure_std
value: 0.600256720497283
task:
type: Clustering
- dataset:
config: default
name: MTEB BlurbsClusteringP2P (default)
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
split: test
type: slvnwhrl/blurbs-clustering-p2p
metrics:
- type: main_score
value: 41.634627363047265
- type: v_measure
value: 41.634627363047265
- type: v_measure_std
value: 9.726923191225307
task:
type: Clustering
- dataset:
config: default
name: MTEB BlurbsClusteringS2S (default)
revision: 22793b6a6465bf00120ad525e38c51210858132c
split: test
type: slvnwhrl/blurbs-clustering-s2s
metrics:
- type: main_score
value: 20.996468295584197
- type: v_measure
value: 20.996468295584197
- type: v_measure_std
value: 9.225766688272197
task:
type: Clustering
- dataset:
config: default
name: MTEB CBD (default)
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 69.99
- type: ap
value: 22.57826353116948
- type: ap_weighted
value: 22.57826353116948
- type: f1
value: 59.04574955548393
- type: f1_weighted
value: 74.36235022309789
- type: main_score
value: 69.99
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E (default)
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
- type: cosine_accuracy
value: 88.7
- type: cosine_accuracy_threshold
value: 97.37848043441772
- type: cosine_ap
value: 73.0405088928302
- type: cosine_f1
value: 63.52201257861635
- type: cosine_f1_threshold
value: 96.98888063430786
- type: cosine_precision
value: 78.90625
- type: cosine_recall
value: 53.1578947368421
- type: dot_accuracy
value: 84.89999999999999
- type: dot_accuracy_threshold
value: 43603.09753417969
- type: dot_ap
value: 56.98157569085279
- type: dot_f1
value: 57.606490872210955
- type: dot_f1_threshold
value: 40406.23779296875
- type: dot_precision
value: 46.864686468646866
- type: dot_recall
value: 74.73684210526315
- type: euclidean_accuracy
value: 88.5
- type: euclidean_accuracy_threshold
value: 498.0483055114746
- type: euclidean_ap
value: 72.97328234816734
- type: euclidean_f1
value: 63.722397476340696
- type: euclidean_f1_threshold
value: 508.6186408996582
- type: euclidean_precision
value: 79.52755905511812
- type: euclidean_recall
value: 53.1578947368421
- type: main_score
value: 73.0405088928302
- type: manhattan_accuracy
value: 88.6
- type: manhattan_accuracy_threshold
value: 12233.079528808594
- type: manhattan_ap
value: 72.92148503992615
- type: manhattan_f1
value: 63.69426751592356
- type: manhattan_f1_threshold
value: 12392.754364013672
- type: manhattan_precision
value: 80.64516129032258
- type: manhattan_recall
value: 52.63157894736842
- type: max_accuracy
value: 88.7
- type: max_ap
value: 73.0405088928302
- type: max_f1
value: 63.722397476340696
- type: max_precision
value: 80.64516129032258
- type: max_recall
value: 74.73684210526315
- type: similarity_accuracy
value: 88.7
- type: similarity_accuracy_threshold
value: 97.37848043441772
- type: similarity_ap
value: 73.0405088928302
- type: similarity_f1
value: 63.52201257861635
- type: similarity_f1_threshold
value: 96.98888063430786
- type: similarity_precision
value: 78.90625
- type: similarity_recall
value: 53.1578947368421
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R (default)
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics:
- type: cosine_pearson
value: 92.97492495289738
- type: cosine_spearman
value: 92.63248098608472
- type: euclidean_pearson
value: 92.04712487782031
- type: euclidean_spearman
value: 92.19679486755008
- type: main_score
value: 92.63248098608472
- type: manhattan_pearson
value: 92.0101187740438
- type: manhattan_spearman
value: 92.20926859332754
- type: pearson
value: 92.97492495289738
- type: spearman
value: 92.63248098608472
task:
type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P (default)
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: main_score
value: 39.96377851800628
- type: v_measure
value: 39.96377851800628
- type: v_measure_std
value: 0.9793033243093288
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S (default)
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: main_score
value: 38.788850224595784
- type: v_measure
value: 38.788850224595784
- type: v_measure_std
value: 1.0712604145916924
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 77.95952507806115
- type: mrr
value: 80.8643253968254
- type: main_score
value: 77.95952507806115
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 78.21522500165045
- type: mrr
value: 81.28194444444443
- type: main_score
value: 78.21522500165045
task:
type: Reranking
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
metrics:
- type: map_at_1
value: 33.377
- type: map_at_10
value: 46.371
- type: map_at_100
value: 47.829
- type: map_at_1000
value: 47.94
- type: map_at_20
value: 47.205000000000005
- type: map_at_3
value: 42.782
- type: map_at_5
value: 44.86
- type: mrr_at_1
value: 41.345
- type: mrr_at_10
value: 52.187
- type: mrr_at_100
value: 52.893
- type: mrr_at_1000
value: 52.929
- type: mrr_at_20
value: 52.637
- type: mrr_at_3
value: 49.714000000000006
- type: mrr_at_5
value: 51.373000000000005
- type: ndcg_at_1
value: 41.345
- type: ndcg_at_10
value: 52.946000000000005
- type: ndcg_at_100
value: 57.92699999999999
- type: ndcg_at_1000
value: 59.609
- type: ndcg_at_20
value: 54.900999999999996
- type: ndcg_at_3
value: 48.357
- type: ndcg_at_5
value: 50.739000000000004
- type: precision_at_1
value: 41.345
- type: precision_at_10
value: 10.186
- type: precision_at_100
value: 1.554
- type: precision_at_1000
value: 0.2
- type: precision_at_20
value: 5.959
- type: precision_at_3
value: 23.796
- type: precision_at_5
value: 17.024
- type: recall_at_1
value: 33.377
- type: recall_at_10
value: 65.067
- type: recall_at_100
value: 86.04899999999999
- type: recall_at_1000
value: 96.54899999999999
- type: recall_at_20
value: 72.071
- type: recall_at_3
value: 51.349999999999994
- type: recall_at_5
value: 58.41
- type: main_score
value: 52.946000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackEnglishRetrieval (default)
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: mteb/cqadupstack-english
metrics:
- type: map_at_1
value: 31.097
- type: map_at_10
value: 42.183
- type: map_at_100
value: 43.580999999999996
- type: map_at_1000
value: 43.718
- type: map_at_20
value: 42.921
- type: map_at_3
value: 38.963
- type: map_at_5
value: 40.815
- type: mrr_at_1
value: 39.745000000000005
- type: mrr_at_10
value: 48.736000000000004
- type: mrr_at_100
value: 49.405
- type: mrr_at_1000
value: 49.452
- type: mrr_at_20
value: 49.118
- type: mrr_at_3
value: 46.497
- type: mrr_at_5
value: 47.827999999999996
- type: ndcg_at_1
value: 39.745000000000005
- type: ndcg_at_10
value: 48.248000000000005
- type: ndcg_at_100
value: 52.956
- type: ndcg_at_1000
value: 54.99699999999999
- type: ndcg_at_20
value: 50.01
- type: ndcg_at_3
value: 43.946000000000005
- type: ndcg_at_5
value: 46.038000000000004
- type: precision_at_1
value: 39.745000000000005
- type: precision_at_10
value: 9.229
- type: precision_at_100
value: 1.5070000000000001
- type: precision_at_1000
value: 0.199
- type: precision_at_20
value: 5.489999999999999
- type: precision_at_3
value: 21.38
- type: precision_at_5
value: 15.274
- type: recall_at_1
value: 31.097
- type: recall_at_10
value: 58.617
- type: recall_at_100
value: 78.55199999999999
- type: recall_at_1000
value: 91.13900000000001
- type: recall_at_20
value: 64.92
- type: recall_at_3
value: 45.672000000000004
- type: recall_at_5
value: 51.669
- type: main_score
value: 48.248000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval (default)
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: mteb/cqadupstack-gaming
metrics:
- type: map_at_1
value: 39.745000000000005
- type: map_at_10
value: 52.063
- type: map_at_100
value: 53.077
- type: map_at_1000
value: 53.13
- type: map_at_20
value: 52.66
- type: map_at_3
value: 48.662
- type: map_at_5
value: 50.507000000000005
- type: mrr_at_1
value: 45.391999999999996
- type: mrr_at_10
value: 55.528
- type: mrr_at_100
value: 56.16100000000001
- type: mrr_at_1000
value: 56.192
- type: mrr_at_20
value: 55.923
- type: mrr_at_3
value: 52.93600000000001
- type: mrr_at_5
value: 54.435
- type: ndcg_at_1
value: 45.391999999999996
- type: ndcg_at_10
value: 58.019
- type: ndcg_at_100
value: 61.936
- type: ndcg_at_1000
value: 63.015
- type: ndcg_at_20
value: 59.691
- type: ndcg_at_3
value: 52.294
- type: ndcg_at_5
value: 55.017
- type: precision_at_1
value: 45.391999999999996
- type: precision_at_10
value: 9.386
- type: precision_at_100
value: 1.232
- type: precision_at_1000
value: 0.136
- type: precision_at_20
value: 5.223
- type: precision_at_3
value: 23.177
- type: precision_at_5
value: 15.9
- type: recall_at_1
value: 39.745000000000005
- type: recall_at_10
value: 72.08099999999999
- type: recall_at_100
value: 88.85300000000001
- type: recall_at_1000
value: 96.569
- type: recall_at_20
value: 78.203
- type: recall_at_3
value: 56.957
- type: recall_at_5
value: 63.63100000000001
- type: main_score
value: 58.019
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval (default)
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: mteb/cqadupstack-gis
metrics:
- type: map_at_1
value: 26.651999999999997
- type: map_at_10
value: 35.799
- type: map_at_100
value: 36.846000000000004
- type: map_at_1000
value: 36.931000000000004
- type: map_at_20
value: 36.341
- type: map_at_3
value: 32.999
- type: map_at_5
value: 34.597
- type: mrr_at_1
value: 28.814
- type: mrr_at_10
value: 37.869
- type: mrr_at_100
value: 38.728
- type: mrr_at_1000
value: 38.795
- type: mrr_at_20
value: 38.317
- type: mrr_at_3
value: 35.235
- type: mrr_at_5
value: 36.738
- type: ndcg_at_1
value: 28.814
- type: ndcg_at_10
value: 41.028
- type: ndcg_at_100
value: 46.162
- type: ndcg_at_1000
value: 48.15
- type: ndcg_at_20
value: 42.824
- type: ndcg_at_3
value: 35.621
- type: ndcg_at_5
value: 38.277
- type: precision_at_1
value: 28.814
- type: precision_at_10
value: 6.361999999999999
- type: precision_at_100
value: 0.9450000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 3.6159999999999997
- type: precision_at_3
value: 15.140999999999998
- type: precision_at_5
value: 10.712000000000002
- type: recall_at_1
value: 26.651999999999997
- type: recall_at_10
value: 55.038
- type: recall_at_100
value: 78.806
- type: recall_at_1000
value: 93.485
- type: recall_at_20
value: 61.742
- type: recall_at_3
value: 40.682
- type: recall_at_5
value: 46.855000000000004
- type: main_score
value: 41.028
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval (default)
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: mteb/cqadupstack-mathematica
metrics:
- type: map_at_1
value: 17.627000000000002
- type: map_at_10
value: 26.436999999999998
- type: map_at_100
value: 27.85
- type: map_at_1000
value: 27.955999999999996
- type: map_at_20
value: 27.233
- type: map_at_3
value: 23.777
- type: map_at_5
value: 25.122
- type: mrr_at_1
value: 22.387999999999998
- type: mrr_at_10
value: 31.589
- type: mrr_at_100
value: 32.641999999999996
- type: mrr_at_1000
value: 32.696999999999996
- type: mrr_at_20
value: 32.201
- type: mrr_at_3
value: 28.98
- type: mrr_at_5
value: 30.342000000000002
- type: ndcg_at_1
value: 22.387999999999998
- type: ndcg_at_10
value: 32.129999999999995
- type: ndcg_at_100
value: 38.562999999999995
- type: ndcg_at_1000
value: 40.903
- type: ndcg_at_20
value: 34.652
- type: ndcg_at_3
value: 27.26
- type: ndcg_at_5
value: 29.235
- type: precision_at_1
value: 22.387999999999998
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 1.068
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_20
value: 3.6999999999999997
- type: precision_at_3
value: 13.267000000000001
- type: precision_at_5
value: 9.403
- type: recall_at_1
value: 17.627000000000002
- type: recall_at_10
value: 44.71
- type: recall_at_100
value: 72.426
- type: recall_at_1000
value: 88.64699999999999
- type: recall_at_20
value: 53.65
- type: recall_at_3
value: 30.989
- type: recall_at_5
value: 36.237
- type: main_score
value: 32.129999999999995
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackPhysicsRetrieval (default)
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
split: test
type: mteb/cqadupstack-physics
metrics:
- type: map_at_1
value: 30.891000000000002
- type: map_at_10
value: 41.519
- type: map_at_100
value: 42.896
- type: map_at_1000
value: 42.992999999999995
- type: map_at_20
value: 42.287
- type: map_at_3
value: 37.822
- type: map_at_5
value: 39.976
- type: mrr_at_1
value: 37.921
- type: mrr_at_10
value: 47.260999999999996
- type: mrr_at_100
value: 48.044
- type: mrr_at_1000
value: 48.08
- type: mrr_at_20
value: 47.699999999999996
- type: mrr_at_3
value: 44.513999999999996
- type: mrr_at_5
value: 46.064
- type: ndcg_at_1
value: 37.921
- type: ndcg_at_10
value: 47.806
- type: ndcg_at_100
value: 53.274
- type: ndcg_at_1000
value: 55.021
- type: ndcg_at_20
value: 49.973
- type: ndcg_at_3
value: 42.046
- type: ndcg_at_5
value: 44.835
- type: precision_at_1
value: 37.921
- type: precision_at_10
value: 8.767999999999999
- type: precision_at_100
value: 1.353
- type: precision_at_1000
value: 0.168
- type: precision_at_20
value: 5.135
- type: precision_at_3
value: 20.051
- type: precision_at_5
value: 14.398
- type: recall_at_1
value: 30.891000000000002
- type: recall_at_10
value: 60.897999999999996
- type: recall_at_100
value: 83.541
- type: recall_at_1000
value: 94.825
- type: recall_at_20
value: 68.356
- type: recall_at_3
value: 44.65
- type: recall_at_5
value: 51.919000000000004
- type: main_score
value: 47.806
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackProgrammersRetrieval (default)
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
split: test
type: mteb/cqadupstack-programmers
metrics:
- type: map_at_1
value: 27.654
- type: map_at_10
value: 38.025999999999996
- type: map_at_100
value: 39.425
- type: map_at_1000
value: 39.528
- type: map_at_20
value: 38.838
- type: map_at_3
value: 34.745
- type: map_at_5
value: 36.537
- type: mrr_at_1
value: 34.018
- type: mrr_at_10
value: 43.314
- type: mrr_at_100
value: 44.283
- type: mrr_at_1000
value: 44.327
- type: mrr_at_20
value: 43.929
- type: mrr_at_3
value: 40.868
- type: mrr_at_5
value: 42.317
- type: ndcg_at_1
value: 34.018
- type: ndcg_at_10
value: 43.887
- type: ndcg_at_100
value: 49.791000000000004
- type: ndcg_at_1000
value: 51.834
- type: ndcg_at_20
value: 46.376
- type: ndcg_at_3
value: 38.769999999999996
- type: ndcg_at_5
value: 41.144
- type: precision_at_1
value: 34.018
- type: precision_at_10
value: 8.001999999999999
- type: precision_at_100
value: 1.2630000000000001
- type: precision_at_1000
value: 0.16
- type: precision_at_20
value: 4.737
- type: precision_at_3
value: 18.417
- type: precision_at_5
value: 13.150999999999998
- type: recall_at_1
value: 27.654
- type: recall_at_10
value: 56.111
- type: recall_at_100
value: 81.136
- type: recall_at_1000
value: 94.788
- type: recall_at_20
value: 65.068
- type: recall_at_3
value: 41.713
- type: recall_at_5
value: 48.106
- type: main_score
value: 43.887
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackRetrieval (default)
revision: CQADupstackRetrieval_is_a_combined_dataset
split: test
type: CQADupstackRetrieval_is_a_combined_dataset
metrics:
- type: main_score
value: 42.58858333333333
- type: ndcg_at_10
value: 42.58858333333333
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval (default)
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
split: test
type: mteb/cqadupstack-stats
metrics:
- type: map_at_1
value: 24.501
- type: map_at_10
value: 32.814
- type: map_at_100
value: 33.754
- type: map_at_1000
value: 33.859
- type: map_at_20
value: 33.324
- type: map_at_3
value: 30.758000000000003
- type: map_at_5
value: 31.936999999999998
- type: mrr_at_1
value: 27.761000000000003
- type: mrr_at_10
value: 35.662
- type: mrr_at_100
value: 36.443999999999996
- type: mrr_at_1000
value: 36.516999999999996
- type: mrr_at_20
value: 36.085
- type: mrr_at_3
value: 33.742
- type: mrr_at_5
value: 34.931
- type: ndcg_at_1
value: 27.761000000000003
- type: ndcg_at_10
value: 37.208000000000006
- type: ndcg_at_100
value: 41.839
- type: ndcg_at_1000
value: 44.421
- type: ndcg_at_20
value: 38.917
- type: ndcg_at_3
value: 33.544000000000004
- type: ndcg_at_5
value: 35.374
- type: precision_at_1
value: 27.761000000000003
- type: precision_at_10
value: 5.92
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 3.4130000000000003
- type: precision_at_3
value: 15.031
- type: precision_at_5
value: 10.306999999999999
- type: recall_at_1
value: 24.501
- type: recall_at_10
value: 47.579
- type: recall_at_100
value: 69.045
- type: recall_at_1000
value: 88.032
- type: recall_at_20
value: 54.125
- type: recall_at_3
value: 37.202
- type: recall_at_5
value: 41.927
- type: main_score
value: 37.208000000000006
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackTexRetrieval (default)
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: mteb/cqadupstack-tex
metrics:
- type: map_at_1
value: 18.29
- type: map_at_10
value: 26.183
- type: map_at_100
value: 27.351999999999997
- type: map_at_1000
value: 27.483999999999998
- type: map_at_20
value: 26.798
- type: map_at_3
value: 23.629
- type: map_at_5
value: 24.937
- type: mrr_at_1
value: 22.299
- type: mrr_at_10
value: 30.189
- type: mrr_at_100
value: 31.098
- type: mrr_at_1000
value: 31.177
- type: mrr_at_20
value: 30.697000000000003
- type: mrr_at_3
value: 27.862
- type: mrr_at_5
value: 29.066
- type: ndcg_at_1
value: 22.299
- type: ndcg_at_10
value: 31.202
- type: ndcg_at_100
value: 36.617
- type: ndcg_at_1000
value: 39.544000000000004
- type: ndcg_at_20
value: 33.177
- type: ndcg_at_3
value: 26.639000000000003
- type: ndcg_at_5
value: 28.526
- type: precision_at_1
value: 22.299
- type: precision_at_10
value: 5.8020000000000005
- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_20
value: 3.505
- type: precision_at_3
value: 12.698
- type: precision_at_5
value: 9.174
- type: recall_at_1
value: 18.29
- type: recall_at_10
value: 42.254999999999995
- type: recall_at_100
value: 66.60000000000001
- type: recall_at_1000
value: 87.31400000000001
- type: recall_at_20
value: 49.572
- type: recall_at_3
value: 29.342000000000002
- type: recall_at_5
value: 34.221000000000004
- type: main_score
value: 31.202
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackUnixRetrieval (default)
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: mteb/cqadupstack-unix
metrics:
- type: map_at_1
value: 27.722
- type: map_at_10
value: 37.698
- type: map_at_100
value: 38.899
- type: map_at_1000
value: 38.998
- type: map_at_20
value: 38.381
- type: map_at_3
value: 34.244
- type: map_at_5
value: 36.295
- type: mrr_at_1
value: 32.183
- type: mrr_at_10
value: 41.429
- type: mrr_at_100
value: 42.308
- type: mrr_at_1000
value: 42.358000000000004
- type: mrr_at_20
value: 41.957
- type: mrr_at_3
value: 38.401999999999994
- type: mrr_at_5
value: 40.294999999999995
- type: ndcg_at_1
value: 32.183
- type: ndcg_at_10
value: 43.519000000000005
- type: ndcg_at_100
value: 48.786
- type: ndcg_at_1000
value: 50.861999999999995
- type: ndcg_at_20
value: 45.654
- type: ndcg_at_3
value: 37.521
- type: ndcg_at_5
value: 40.615
- type: precision_at_1
value: 32.183
- type: precision_at_10
value: 7.603
- type: precision_at_100
value: 1.135
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 4.408
- type: precision_at_3
value: 17.071
- type: precision_at_5
value: 12.668
- type: recall_at_1
value: 27.722
- type: recall_at_10
value: 57.230000000000004
- type: recall_at_100
value: 79.97999999999999
- type: recall_at_1000
value: 94.217
- type: recall_at_20
value: 64.864
- type: recall_at_3
value: 41.215
- type: recall_at_5
value: 48.774
- type: main_score
value: 43.519000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWebmastersRetrieval (default)
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: mteb/cqadupstack-webmasters
metrics:
- type: map_at_1
value: 25.852999999999998
- type: map_at_10
value: 35.394999999999996
- type: map_at_100
value: 37.291999999999994
- type: map_at_1000
value: 37.495
- type: map_at_20
value: 36.372
- type: map_at_3
value: 32.336
- type: map_at_5
value: 34.159
- type: mrr_at_1
value: 31.818
- type: mrr_at_10
value: 40.677
- type: mrr_at_100
value: 41.728
- type: mrr_at_1000
value: 41.778
- type: mrr_at_20
value: 41.301
- type: mrr_at_3
value: 38.208
- type: mrr_at_5
value: 39.592
- type: ndcg_at_1
value: 31.818
- type: ndcg_at_10
value: 41.559000000000005
- type: ndcg_at_100
value: 48.012
- type: ndcg_at_1000
value: 50.234
- type: ndcg_at_20
value: 44.15
- type: ndcg_at_3
value: 36.918
- type: ndcg_at_5
value: 39.227000000000004
- type: precision_at_1
value: 31.818
- type: precision_at_10
value: 8.043
- type: precision_at_100
value: 1.625
- type: precision_at_1000
value: 0.245
- type: precision_at_20
value: 5.2170000000000005
- type: precision_at_3
value: 17.655
- type: precision_at_5
value: 12.845999999999998
- type: recall_at_1
value: 25.852999999999998
- type: recall_at_10
value: 53.093
- type: recall_at_100
value: 81.05799999999999
- type: recall_at_1000
value: 94.657
- type: recall_at_20
value: 62.748000000000005
- type: recall_at_3
value: 39.300000000000004
- type: recall_at_5
value: 45.754
- type: main_score
value: 41.559000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWordpressRetrieval (default)
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: mteb/cqadupstack-wordpress
metrics:
- type: map_at_1
value: 19.23
- type: map_at_10
value: 28.128999999999998
- type: map_at_100
value: 29.195
- type: map_at_1000
value: 29.310000000000002
- type: map_at_20
value: 28.713
- type: map_at_3
value: 25.191000000000003
- type: map_at_5
value: 26.69
- type: mrr_at_1
value: 21.257
- type: mrr_at_10
value: 30.253999999999998
- type: mrr_at_100
value: 31.195
- type: mrr_at_1000
value: 31.270999999999997
- type: mrr_at_20
value: 30.747999999999998
- type: mrr_at_3
value: 27.633999999999997
- type: mrr_at_5
value: 28.937
- type: ndcg_at_1
value: 21.257
- type: ndcg_at_10
value: 33.511
- type: ndcg_at_100
value: 38.733000000000004
- type: ndcg_at_1000
value: 41.489
- type: ndcg_at_20
value: 35.476
- type: ndcg_at_3
value: 27.845
- type: ndcg_at_5
value: 30.264999999999997
- type: precision_at_1
value: 21.257
- type: precision_at_10
value: 5.619
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.124
- type: precision_at_20
value: 3.29
- type: precision_at_3
value: 12.508
- type: precision_at_5
value: 8.946
- type: recall_at_1
value: 19.23
- type: recall_at_10
value: 48.185
- type: recall_at_100
value: 71.932
- type: recall_at_1000
value: 92.587
- type: recall_at_20
value: 55.533
- type: recall_at_3
value: 32.865
- type: recall_at_5
value: 38.577
- type: main_score
value: 33.511
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER (default)
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: map_at_1
value: 19.594
- type: map_at_10
value: 32.519
- type: map_at_100
value: 34.1
- type: map_at_1000
value: 34.263
- type: map_at_20
value: 33.353
- type: map_at_3
value: 27.898
- type: map_at_5
value: 30.524
- type: mrr_at_1
value: 46.515
- type: mrr_at_10
value: 56.958
- type: mrr_at_100
value: 57.54899999999999
- type: mrr_at_1000
value: 57.574999999999996
- type: mrr_at_20
value: 57.315000000000005
- type: mrr_at_3
value: 54.852999999999994
- type: mrr_at_5
value: 56.153
- type: ndcg_at_1
value: 46.515
- type: ndcg_at_10
value: 42.363
- type: ndcg_at_100
value: 48.233
- type: ndcg_at_1000
value: 50.993
- type: ndcg_at_20
value: 44.533
- type: ndcg_at_3
value: 37.297000000000004
- type: ndcg_at_5
value: 38.911
- type: precision_at_1
value: 46.515
- type: precision_at_10
value: 12.520999999999999
- type: precision_at_100
value: 1.8980000000000001
- type: precision_at_1000
value: 0.242
- type: precision_at_20
value: 7.212000000000001
- type: precision_at_3
value: 27.752
- type: precision_at_5
value: 20.391000000000002
- type: recall_at_1
value: 19.594
- type: recall_at_10
value: 46.539
- type: recall_at_100
value: 66.782
- type: recall_at_1000
value: 82.049
- type: recall_at_20
value: 52.611
- type: recall_at_3
value: 32.528
- type: recall_at_5
value: 38.933
- type: main_score
value: 42.363
task:
type: Retrieval
- dataset:
config: default
name: MTEB CmedqaRetrieval (default)
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
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- type: map_at_10
value: 29.94
- type: map_at_100
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- type: map_at_1000
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- type: map_at_20
value: 30.798
- type: map_at_3
value: 26.534999999999997
- type: map_at_5
value: 28.33
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- type: recall_at_1
value: 20.144000000000002
- type: recall_at_10
value: 44.985
- type: recall_at_100
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- type: recall_at_1000
value: 94.477
- type: recall_at_20
value: 53.37
- type: recall_at_3
value: 31.141000000000002
- type: recall_at_5
value: 36.721
task:
type: Retrieval
- dataset:
config: default
name: MTEB Cmnli (default)
revision: None
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_accuracy
value: 71.25676488274203
- type: cos_sim_accuracy_threshold
value: 78.11152935028076
- type: cos_sim_ap
value: 79.10444825556077
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value: 74.10750923266312
- type: cos_sim_f1_threshold
value: 75.2312421798706
- type: cos_sim_precision
value: 66.02083714129044
- type: cos_sim_recall
value: 84.45171849427169
- type: dot_accuracy
value: 68.11785929043896
- type: dot_accuracy_threshold
value: 34783.23974609375
- type: dot_ap
value: 75.80201827987712
- type: dot_f1
value: 72.31670990679349
- type: dot_f1_threshold
value: 31978.036499023438
- type: dot_precision
value: 61.386623164763456
- type: dot_recall
value: 87.98223053542202
- type: euclidean_accuracy
value: 71.41310883944678
- type: euclidean_accuracy_threshold
value: 1374.9353408813477
- type: euclidean_ap
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task:
type: Retrieval
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config: default
name: MTEB EcomRetrieval (default)
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
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task:
type: Retrieval
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config: default
name: MTEB EmotionClassification (default)
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
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task:
type: Classification
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config: default
name: MTEB FEVER (default)
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
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value: 85.762
- type: map_at_100
value: 85.954
- type: map_at_1000
value: 85.966
- type: map_at_20
value: 85.887
- type: map_at_3
value: 84.854
- type: map_at_5
value: 85.408
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value: 84.443
- type: mrr_at_10
value: 90.432
- type: mrr_at_100
value: 90.483
- type: mrr_at_1000
value: 90.484
- type: mrr_at_20
value: 90.473
- type: mrr_at_3
value: 89.89399999999999
- type: mrr_at_5
value: 90.244
- type: ndcg_at_1
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- type: ndcg_at_1000
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value: 78.371
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value: 94.594
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- type: recall_at_1000
value: 98.18
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value: 95.707
- type: recall_at_3
value: 90.853
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task:
type: Retrieval
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config: default
name: MTEB FiQA2018 (default)
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
split: test
type: mteb/fiqa
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task:
type: Retrieval
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config: default
name: MTEB GeoreviewClassification (default)
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
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type: ai-forever/georeview-classification
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task:
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config: default
name: MTEB GeoreviewClusteringP2P (default)
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
split: test
type: ai-forever/georeview-clustering-p2p
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task:
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name: MTEB GerDaLIR (default)
revision: 0bb47f1d73827e96964edb84dfe552f62f4fd5eb
split: test
type: jinaai/ger_da_lir
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- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: 86.93074003793416
- type: nauc_recall_at_100_max
value: 100.0
- type: nauc_recall_at_100_std
value: -174.07785375175723
- type: nauc_recall_at_10_diff1
value: 87.43064119411991
- type: nauc_recall_at_10_max
value: 90.60785783417579
- type: nauc_recall_at_10_std
value: 15.378710059643607
- type: nauc_recall_at_1_diff1
value: 89.35436544117584
- type: nauc_recall_at_1_max
value: 70.35936815057701
- type: nauc_recall_at_1_std
value: -13.598996360976903
- type: nauc_recall_at_20_diff1
value: 78.78206037685645
- type: nauc_recall_at_20_max
value: 82.52264166455791
- type: nauc_recall_at_20_std
value: -5.958065992168697
- type: nauc_recall_at_3_diff1
value: 90.12709256456463
- type: nauc_recall_at_3_max
value: 90.7267880583832
- type: nauc_recall_at_3_std
value: -11.047599315631881
- type: nauc_recall_at_5_diff1
value: 89.90668735665676
- type: nauc_recall_at_5_max
value: 93.51571626543753
- type: nauc_recall_at_5_std
value: 22.632403279126112
- type: ndcg_at_1
value: 90.789
- type: ndcg_at_10
value: 95.46
- type: ndcg_at_100
value: 95.652
- type: ndcg_at_1000
value: 95.659
- type: ndcg_at_20
value: 95.575
- type: ndcg_at_3
value: 94.82000000000001
- type: ndcg_at_5
value: 95.26400000000001
- type: precision_at_1
value: 90.789
- type: precision_at_10
value: 9.908999999999999
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.977
- type: precision_at_3
value: 32.471
- type: precision_at_5
value: 19.701
- type: recall_at_1
value: 90.789
- type: recall_at_10
value: 99.093
- type: recall_at_100
value: 99.955
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.546
- type: recall_at_3
value: 97.414
- type: recall_at_5
value: 98.503
task:
type: Retrieval
- dataset:
config: default
name: MTEB GermanSTSBenchmark (default)
revision: e36907544d44c3a247898ed81540310442329e20
split: test
type: jinaai/german-STSbenchmark
metrics:
- type: cosine_pearson
value: 86.55319003300265
- type: cosine_spearman
value: 87.50267373081324
- type: euclidean_pearson
value: 87.41630636501863
- type: euclidean_spearman
value: 88.02170803409365
- type: main_score
value: 87.50267373081324
- type: manhattan_pearson
value: 87.33703179056744
- type: manhattan_spearman
value: 87.99192826922514
- type: pearson
value: 86.55319003300265
- type: spearman
value: 87.50267373081324
task:
type: STS
- dataset:
config: default
name: MTEB HALClusteringS2S (default)
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: main_score
value: 27.477557517301303
- type: v_measure
value: 27.477557517301303
- type: v_measure_std
value: 3.3525736581861336
task:
type: Clustering
- dataset:
config: default
name: MTEB HeadlineClassification (default)
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
split: test
type: ai-forever/headline-classification
metrics:
- type: accuracy
value: 75.0830078125
- type: f1
value: 75.08863209267814
- type: f1_weighted
value: 75.08895979060917
- type: main_score
value: 75.0830078125
task:
type: Classification
- dataset:
config: default
name: MTEB HotpotQA (default)
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
metrics:
- type: map_at_1
value: 38.143
- type: map_at_10
value: 55.916999999999994
- type: map_at_100
value: 56.706
- type: map_at_1000
value: 56.77100000000001
- type: map_at_20
value: 56.367
- type: map_at_3
value: 53.111
- type: map_at_5
value: 54.839000000000006
- type: mrr_at_1
value: 76.286
- type: mrr_at_10
value: 81.879
- type: mrr_at_100
value: 82.09100000000001
- type: mrr_at_1000
value: 82.101
- type: mrr_at_20
value: 82.01
- type: mrr_at_3
value: 80.972
- type: mrr_at_5
value: 81.537
- type: ndcg_at_1
value: 76.286
- type: ndcg_at_10
value: 64.673
- type: ndcg_at_100
value: 67.527
- type: ndcg_at_1000
value: 68.857
- type: ndcg_at_20
value: 65.822
- type: ndcg_at_3
value: 60.616
- type: ndcg_at_5
value: 62.827999999999996
- type: precision_at_1
value: 76.286
- type: precision_at_10
value: 13.196
- type: precision_at_100
value: 1.544
- type: precision_at_1000
value: 0.172
- type: precision_at_20
value: 6.968000000000001
- type: precision_at_3
value: 37.992
- type: precision_at_5
value: 24.54
- type: recall_at_1
value: 38.143
- type: recall_at_10
value: 65.982
- type: recall_at_100
value: 77.225
- type: recall_at_1000
value: 86.077
- type: recall_at_20
value: 69.68299999999999
- type: recall_at_3
value: 56.989000000000004
- type: recall_at_5
value: 61.35
- type: main_score
value: 64.673
task:
type: Retrieval
- dataset:
config: default
name: MTEB IFlyTek (default)
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy
value: 41.67756829549827
- type: f1
value: 33.929325579581636
- type: f1_weighted
value: 43.03952025643197
- type: main_score
value: 41.67756829549827
task:
type: Classification
- dataset:
config: default
name: MTEB ImdbClassification (default)
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 91.90440000000001
- type: ap
value: 88.78663714603425
- type: ap_weighted
value: 88.78663714603425
- type: f1
value: 91.89564361975891
- type: f1_weighted
value: 91.89564361975891
- type: main_score
value: 91.90440000000001
task:
type: Classification
- dataset:
config: default
name: MTEB InappropriatenessClassification (default)
revision: 601651fdc45ef243751676e62dd7a19f491c0285
split: test
type: ai-forever/inappropriateness-classification
metrics:
- type: accuracy
value: 61.0498046875
- type: ap
value: 57.04240566648215
- type: ap_weighted
value: 57.04240566648215
- type: f1
value: 60.867630038606954
- type: f1_weighted
value: 60.867630038606954
- type: main_score
value: 61.0498046875
task:
type: Classification
- dataset:
config: default
name: MTEB JDReview (default)
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy
value: 83.50844277673546
- type: ap
value: 48.46732380712268
- type: ap_weighted
value: 48.46732380712268
- type: f1
value: 77.43967451387445
- type: f1_weighted
value: 84.78462929014114
- type: main_score
value: 83.50844277673546
task:
type: Classification
- dataset:
config: default
name: MTEB KinopoiskClassification (default)
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
split: test
type: ai-forever/kinopoisk-sentiment-classification
metrics:
- type: accuracy
value: 62.393333333333324
- type: f1
value: 61.35940129568015
- type: f1_weighted
value: 61.35940129568015
- type: main_score
value: 62.393333333333324
task:
type: Classification
- dataset:
config: default
name: MTEB LCQMC (default)
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
split: test
type: C-MTEB/LCQMC
metrics:
- type: cosine_pearson
value: 67.74375505907872
- type: cosine_spearman
value: 75.94582231399434
- type: euclidean_pearson
value: 74.52501692443582
- type: euclidean_spearman
value: 75.88428434746646
- type: main_score
value: 75.94582231399434
- type: manhattan_pearson
value: 74.55015441749529
- type: manhattan_spearman
value: 75.83288262176175
- type: pearson
value: 67.74375505907872
- type: spearman
value: 75.94582231399434
task:
type: STS
- dataset:
config: default
name: MTEB LEMBNarrativeQARetrieval (default)
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
split: test
type: dwzhu/LongEmbed
metrics:
- type: map_at_1
value: 23.093
- type: map_at_10
value: 30.227999999999998
- type: map_at_100
value: 31.423000000000002
- type: map_at_1000
value: 31.533
- type: map_at_20
value: 30.835
- type: map_at_3
value: 27.983999999999998
- type: map_at_5
value: 29.253
- type: mrr_at_1
value: 23.093
- type: mrr_at_10
value: 30.227999999999998
- type: mrr_at_100
value: 31.423000000000002
- type: mrr_at_1000
value: 31.533
- type: mrr_at_20
value: 30.835
- type: mrr_at_3
value: 27.983999999999998
- type: mrr_at_5
value: 29.253
- type: ndcg_at_1
value: 23.093
- type: ndcg_at_10
value: 34.297
- type: ndcg_at_100
value: 41.049
- type: ndcg_at_1000
value: 43.566
- type: ndcg_at_20
value: 36.52
- type: ndcg_at_3
value: 29.629
- type: ndcg_at_5
value: 31.926
- type: precision_at_1
value: 23.093
- type: precision_at_10
value: 4.735
- type: precision_at_100
value: 0.8109999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 2.8080000000000003
- type: precision_at_3
value: 11.468
- type: precision_at_5
value: 8.001
- type: recall_at_1
value: 23.093
- type: recall_at_10
value: 47.354
- type: recall_at_100
value: 81.147
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 56.16799999999999
- type: recall_at_3
value: 34.405
- type: recall_at_5
value: 40.004
- type: main_score
value: 34.297
task:
type: Retrieval
- dataset:
config: default
name: MTEB LEMBNeedleRetrieval (default)
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
split: test_256
type: dwzhu/LongEmbed
metrics:
- type: map_at_1
value: 64.0
- type: map_at_10
value: 77.083
- type: map_at_100
value: 77.265
- type: map_at_1000
value: 77.265
- type: map_at_20
value: 77.265
- type: map_at_3
value: 76.333
- type: map_at_5
value: 76.833
- type: mrr_at_1
value: 64.0
- type: mrr_at_10
value: 77.083
- type: mrr_at_100
value: 77.265
- type: mrr_at_1000
value: 77.265
- type: mrr_at_20
value: 77.265
- type: mrr_at_3
value: 76.333
- type: mrr_at_5
value: 76.833
- type: ndcg_at_1
value: 64.0
- type: ndcg_at_10
value: 82.325
- type: ndcg_at_100
value: 82.883
- type: ndcg_at_1000
value: 82.883
- type: ndcg_at_20
value: 82.883
- type: ndcg_at_3
value: 80.833
- type: ndcg_at_5
value: 81.694
- type: precision_at_1
value: 64.0
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 5.0
- type: precision_at_3
value: 31.333
- type: precision_at_5
value: 19.2
- type: recall_at_1
value: 64.0
- type: recall_at_10
value: 98.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 100.0
- type: recall_at_3
value: 94.0
- type: recall_at_5
value: 96.0
- type: main_score
value: 64.0
task:
type: Retrieval
- dataset:
config: default
name: MTEB LEMBPasskeyRetrieval (default)
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
split: test_256
type: dwzhu/LongEmbed
metrics:
- type: map_at_1
value: 100.0
- type: map_at_10
value: 100.0
- type: map_at_100
value: 100.0
- type: map_at_1000
value: 100.0
- type: map_at_20
value: 100.0
- type: map_at_3
value: 100.0
- type: map_at_5
value: 100.0
- type: mrr_at_1
value: 100.0
- type: mrr_at_10
value: 100.0
- type: mrr_at_100
value: 100.0
- type: mrr_at_1000
value: 100.0
- type: mrr_at_20
value: 100.0
- type: mrr_at_3
value: 100.0
- type: mrr_at_5
value: 100.0
- type: ndcg_at_1
value: 100.0
- type: ndcg_at_10
value: 100.0
- type: ndcg_at_100
value: 100.0
- type: ndcg_at_1000
value: 100.0
- type: ndcg_at_20
value: 100.0
- type: ndcg_at_3
value: 100.0
- type: ndcg_at_5
value: 100.0
- type: precision_at_1
value: 100.0
- type: precision_at_10
value: 10.0
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 5.0
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 20.0
- type: recall_at_1
value: 100.0
- type: recall_at_10
value: 100.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 100.0
- type: recall_at_3
value: 100.0
- type: recall_at_5
value: 100.0
- type: main_score
value: 100.0
task:
type: Retrieval
- dataset:
config: default
name: MTEB LEMBQMSumRetrieval (default)
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
split: test
type: dwzhu/LongEmbed
metrics:
- type: map_at_1
value: 24.361
- type: map_at_10
value: 33.641
- type: map_at_100
value: 35.104
- type: map_at_1000
value: 35.127
- type: map_at_20
value: 34.388999999999996
- type: map_at_3
value: 30.255
- type: map_at_5
value: 32.079
- type: mrr_at_1
value: 24.361
- type: mrr_at_10
value: 33.641
- type: mrr_at_100
value: 35.104
- type: mrr_at_1000
value: 35.127
- type: mrr_at_20
value: 34.388999999999996
- type: mrr_at_3
value: 30.255
- type: mrr_at_5
value: 32.079
- type: ndcg_at_1
value: 24.361
- type: ndcg_at_10
value: 39.337
- type: ndcg_at_100
value: 47.384
- type: ndcg_at_1000
value: 47.75
- type: ndcg_at_20
value: 42.077999999999996
- type: ndcg_at_3
value: 32.235
- type: ndcg_at_5
value: 35.524
- type: precision_at_1
value: 24.361
- type: precision_at_10
value: 5.783
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 3.435
- type: precision_at_3
value: 12.661
- type: precision_at_5
value: 9.193999999999999
- type: recall_at_1
value: 24.361
- type: recall_at_10
value: 57.826
- type: recall_at_100
value: 97.51100000000001
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 68.697
- type: recall_at_3
value: 37.983
- type: recall_at_5
value: 45.972
- type: main_score
value: 39.337
task:
type: Retrieval
- dataset:
config: default
name: MTEB LEMBSummScreenFDRetrieval (default)
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
split: validation
type: dwzhu/LongEmbed
metrics:
- type: map_at_1
value: 84.821
- type: map_at_10
value: 90.11200000000001
- type: map_at_100
value: 90.158
- type: map_at_1000
value: 90.158
- type: map_at_20
value: 90.137
- type: map_at_3
value: 89.385
- type: map_at_5
value: 89.876
- type: mrr_at_1
value: 84.821
- type: mrr_at_10
value: 90.11200000000001
- type: mrr_at_100
value: 90.158
- type: mrr_at_1000
value: 90.158
- type: mrr_at_20
value: 90.137
- type: mrr_at_3
value: 89.385
- type: mrr_at_5
value: 89.876
- type: ndcg_at_1
value: 84.821
- type: ndcg_at_10
value: 92.334
- type: ndcg_at_100
value: 92.535
- type: ndcg_at_1000
value: 92.535
- type: ndcg_at_20
value: 92.414
- type: ndcg_at_3
value: 90.887
- type: ndcg_at_5
value: 91.758
- type: precision_at_1
value: 84.821
- type: precision_at_10
value: 9.911
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.97
- type: precision_at_3
value: 31.746000000000002
- type: precision_at_5
value: 19.464000000000002
- type: recall_at_1
value: 84.821
- type: recall_at_10
value: 99.107
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.405
- type: recall_at_3
value: 95.238
- type: recall_at_5
value: 97.321
- type: main_score
value: 92.334
task:
type: Retrieval
- dataset:
config: default
name: MTEB LEMBWikimQARetrieval (default)
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
split: test
type: dwzhu/LongEmbed
metrics:
- type: map_at_1
value: 53.667
- type: map_at_10
value: 61.719
- type: map_at_100
value: 62.471
- type: map_at_1000
value: 62.492000000000004
- type: map_at_20
value: 62.153000000000006
- type: map_at_3
value: 59.167
- type: map_at_5
value: 60.95
- type: mrr_at_1
value: 53.667
- type: mrr_at_10
value: 61.719
- type: mrr_at_100
value: 62.471
- type: mrr_at_1000
value: 62.492000000000004
- type: mrr_at_20
value: 62.153000000000006
- type: mrr_at_3
value: 59.167
- type: mrr_at_5
value: 60.95
- type: ndcg_at_1
value: 53.667
- type: ndcg_at_10
value: 66.018
- type: ndcg_at_100
value: 69.726
- type: ndcg_at_1000
value: 70.143
- type: ndcg_at_20
value: 67.61399999999999
- type: ndcg_at_3
value: 60.924
- type: ndcg_at_5
value: 64.10900000000001
- type: precision_at_1
value: 53.667
- type: precision_at_10
value: 7.9670000000000005
- type: precision_at_100
value: 0.97
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.3
- type: precision_at_3
value: 22.0
- type: precision_at_5
value: 14.732999999999999
- type: recall_at_1
value: 53.667
- type: recall_at_10
value: 79.667
- type: recall_at_100
value: 97.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 86.0
- type: recall_at_3
value: 66.0
- type: recall_at_5
value: 73.667
- type: main_score
value: 66.018
task:
type: Retrieval
- dataset:
config: deu-deu
name: MTEB MLQARetrieval (deu-deu)
revision: 397ed406c1a7902140303e7faf60fff35b58d285
split: test
type: facebook/mlqa
metrics:
- type: main_score
value: 67.548
- type: map_at_1
value: 56.559000000000005
- type: map_at_10
value: 63.867
- type: map_at_100
value: 64.429
- type: map_at_1000
value: 64.457
- type: map_at_20
value: 64.215
- type: map_at_3
value: 62.109
- type: map_at_5
value: 63.101
- type: mrr_at_1
value: 56.56990915134057
- type: mrr_at_10
value: 63.86820789324668
- type: mrr_at_100
value: 64.42973602152581
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value: 77.69378738778958
- type: nauc_recall_at_1_max
value: 68.64652310701173
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value: -4.667071946448379
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value: 64.42290081731899
- type: nauc_recall_at_20_max
value: 73.3358289439033
- type: nauc_recall_at_20_std
value: 9.846598361586073
- type: nauc_recall_at_3_diff1
value: 70.41211290964785
- type: nauc_recall_at_3_max
value: 72.64451776775402
- type: nauc_recall_at_3_std
value: -1.916280959835826
- type: nauc_recall_at_5_diff1
value: 68.20695272727916
- type: nauc_recall_at_5_max
value: 72.66404224006101
- type: nauc_recall_at_5_std
value: -0.431125323007886
- type: ndcg_at_1
value: 54.31700000000001
- type: ndcg_at_10
value: 64.723
- type: ndcg_at_100
value: 67.648
- type: ndcg_at_1000
value: 68.619
- type: ndcg_at_20
value: 65.85499999999999
- type: ndcg_at_3
value: 61.244
- type: ndcg_at_5
value: 63.038000000000004
- type: precision_at_1
value: 54.31700000000001
- type: precision_at_10
value: 7.564
- type: precision_at_100
value: 0.898
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 4.005
- type: precision_at_3
value: 22.034000000000002
- type: precision_at_5
value: 14.093
- type: recall_at_1
value: 54.308
- type: recall_at_10
value: 75.622
- type: recall_at_100
value: 89.744
- type: recall_at_1000
value: 97.539
- type: recall_at_20
value: 80.085
- type: recall_at_3
value: 66.09
- type: recall_at_5
value: 70.446
task:
type: Retrieval
- dataset:
config: de
name: MTEB MLSUMClusteringP2P (de)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 41.267647761702854
- type: v_measure
value: 41.267647761702854
- type: v_measure_std
value: 10.93390895077248
task:
type: Clustering
- dataset:
config: fr
name: MTEB MLSUMClusteringP2P (fr)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 44.68714862333979
- type: v_measure
value: 44.68714862333979
- type: v_measure_std
value: 1.811036989797814
task:
type: Clustering
- dataset:
config: ru
name: MTEB MLSUMClusteringP2P (ru)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 41.92518785753813
- type: v_measure
value: 41.92518785753813
- type: v_measure_std
value: 5.9356661900220775
task:
type: Clustering
- dataset:
config: es
name: MTEB MLSUMClusteringP2P (es)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 48.69875719812033
- type: v_measure
value: 48.69875719812033
- type: v_measure_std
value: 1.204253881950113
task:
type: Clustering
- dataset:
config: de
name: MTEB MLSUMClusteringS2S (de)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 40.07927325071353
- type: v_measure
value: 40.07927325071353
- type: v_measure_std
value: 9.296680835266145
task:
type: Clustering
- dataset:
config: fr
name: MTEB MLSUMClusteringS2S (fr)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 44.88484854069901
- type: v_measure
value: 44.88484854069901
- type: v_measure_std
value: 2.3704247819781843
task:
type: Clustering
- dataset:
config: ru
name: MTEB MLSUMClusteringS2S (ru)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 43.97657450929179
- type: v_measure
value: 43.97657450929179
- type: v_measure_std
value: 6.087547931333613
task:
type: Clustering
- dataset:
config: es
name: MTEB MLSUMClusteringS2S (es)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 48.41108671948728
- type: v_measure
value: 48.41108671948728
- type: v_measure_std
value: 1.3848320630151243
task:
type: Clustering
- dataset:
config: default
name: MTEB MMarcoReranking (default)
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map
value: 21.050447576170395
- type: mrr
value: 20.201984126984126
- type: main_score
value: 21.050447576170395
task:
type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval (default)
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
split: dev
type: C-MTEB/MMarcoRetrieval
metrics:
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value: 79.687
- type: map_at_1
value: 66.872
- type: map_at_10
value: 75.949
- type: map_at_100
value: 76.25
- type: map_at_1000
value: 76.259
- type: map_at_20
value: 76.145
- type: map_at_3
value: 74.01299999999999
- type: map_at_5
value: 75.232
- type: mrr_at_1
value: 69.18338108882521
- type: mrr_at_10
value: 76.5424227952881
- type: mrr_at_100
value: 76.8019342792628
- type: mrr_at_1000
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- type: mrr_at_20
value: 76.7115234815896
- type: mrr_at_3
value: 74.83046800382044
- type: mrr_at_5
value: 75.88490926456515
- type: nauc_map_at_1000_diff1
value: 78.06933310424179
- type: nauc_map_at_1000_max
value: 49.392948209665896
- type: nauc_map_at_1000_std
value: -15.126109322591166
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value: 78.06612779298378
- type: nauc_map_at_100_max
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value: -15.099282408159349
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value: 77.94565685470538
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value: -15.182130695916355
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value: 79.84814509858211
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- type: nauc_map_at_20_diff1
value: 78.03597839981245
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value: 78.0637014655507
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value: -17.093950563306596
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value: 77.94068229240348
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value: 49.38930719689204
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value: -15.9919454201954
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value: 78.34582398092816
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value: 78.3429966714221
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value: -14.354914066301236
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value: 78.2208070219624
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value: 49.77720536573364
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value: -14.316233764741812
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value: 80.22305496572142
- type: nauc_mrr_at_1_max
value: 44.30231210192536
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value: -18.942549914934492
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value: 78.31006724240147
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value: 78.2406507247798
- type: nauc_mrr_at_5_max
value: 49.71276359754787
- type: nauc_mrr_at_5_std
value: -14.979526226149698
- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_1000_max
value: 51.11543344053061
- type: nauc_ndcg_at_1000_std
value: -12.208878737005096
- type: nauc_ndcg_at_100_diff1
value: 77.67462502211228
- type: nauc_ndcg_at_100_max
value: 51.593977338939034
- type: nauc_ndcg_at_100_std
value: -11.312126179513802
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value: 52.35435572808972
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- type: nauc_precision_at_100_std
value: 29.085768971612342
- type: nauc_precision_at_10_diff1
value: 21.67045907336595
- type: nauc_precision_at_10_max
value: 41.68948432407223
- type: nauc_precision_at_10_std
value: 17.837055074458092
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- type: nauc_precision_at_1_max
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- type: nauc_precision_at_20_diff1
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
value: 23.635897665206087
- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_3_max
value: 47.0458691263379
- type: nauc_precision_at_3_std
value: -3.3181861146890217
- type: nauc_precision_at_5_diff1
value: 35.406205343514806
- type: nauc_precision_at_5_max
value: 45.56549449285401
- type: nauc_precision_at_5_std
value: 5.612378074562386
- type: nauc_recall_at_1000_diff1
value: 72.32762520815842
- type: nauc_recall_at_1000_max
value: 85.64979256307343
- type: nauc_recall_at_1000_std
value: 73.61925297037476
- type: nauc_recall_at_100_diff1
value: 72.31946328709962
- type: nauc_recall_at_100_max
value: 83.76576070068353
- type: nauc_recall_at_100_std
value: 57.39376538662535
- type: nauc_recall_at_10_diff1
value: 69.51307788072499
- type: nauc_recall_at_10_max
value: 69.60124733654142
- type: nauc_recall_at_10_std
value: 13.483540424716892
- type: nauc_recall_at_1_diff1
value: 79.84814509858211
- type: nauc_recall_at_1_max
value: 40.78978466656547
- type: nauc_recall_at_1_std
value: -19.96189264026715
- type: nauc_recall_at_20_diff1
value: 70.92168324710599
- type: nauc_recall_at_20_max
value: 76.09106252420084
- type: nauc_recall_at_20_std
value: 25.406842300761447
- type: nauc_recall_at_3_diff1
value: 74.1212680517145
- type: nauc_recall_at_3_max
value: 56.24921832879403
- type: nauc_recall_at_3_std
value: -11.55542913578436
- type: nauc_recall_at_5_diff1
value: 72.31262959872993
- type: nauc_recall_at_5_max
value: 62.761214896697915
- type: nauc_recall_at_5_std
value: -3.280167584070396
- type: ndcg_at_1
value: 69.18299999999999
- type: ndcg_at_10
value: 79.687
- type: ndcg_at_100
value: 81.062
- type: ndcg_at_1000
value: 81.312
- type: ndcg_at_20
value: 80.34599999999999
- type: ndcg_at_3
value: 75.98700000000001
- type: ndcg_at_5
value: 78.039
- type: precision_at_1
value: 69.18299999999999
- type: precision_at_10
value: 9.636
- type: precision_at_100
value: 1.0330000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 4.958
- type: precision_at_3
value: 28.515
- type: precision_at_5
value: 18.201
- type: recall_at_1
value: 66.872
- type: recall_at_10
value: 90.688
- type: recall_at_100
value: 96.99
- type: recall_at_1000
value: 98.958
- type: recall_at_20
value: 93.21199999999999
- type: recall_at_3
value: 80.84599999999999
- type: recall_at_5
value: 85.732
task:
type: Retrieval
- dataset:
config: default
name: MTEB MSMARCO (default)
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
- type: map_at_1
value: 21.861
- type: map_at_10
value: 34.008
- type: map_at_100
value: 35.174
- type: map_at_1000
value: 35.224
- type: map_at_20
value: 34.705999999999996
- type: map_at_3
value: 30.209000000000003
- type: map_at_5
value: 32.351
- type: mrr_at_1
value: 22.493
- type: mrr_at_10
value: 34.583999999999996
- type: mrr_at_100
value: 35.691
- type: mrr_at_1000
value: 35.736000000000004
- type: mrr_at_20
value: 35.257
- type: mrr_at_3
value: 30.85
- type: mrr_at_5
value: 32.962
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.815
- type: ndcg_at_100
value: 46.483999999999995
- type: ndcg_at_1000
value: 47.73
- type: ndcg_at_20
value: 43.302
- type: ndcg_at_3
value: 33.056000000000004
- type: ndcg_at_5
value: 36.879
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.465999999999999
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.752
- type: precision_at_3
value: 14.069
- type: precision_at_5
value: 10.384
- type: recall_at_1
value: 21.861
- type: recall_at_10
value: 61.781
- type: recall_at_100
value: 88.095
- type: recall_at_1000
value: 97.625
- type: recall_at_20
value: 71.44500000000001
- type: recall_at_3
value: 40.653
- type: recall_at_5
value: 49.841
- type: main_score
value: 40.815
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 97.4874601003192
- type: f1
value: 97.19067544931094
- type: f1_weighted
value: 97.49331776181019
- type: main_score
value: 97.4874601003192
task:
type: Classification
- dataset:
config: de
name: MTEB MTOPDomainClassification (de)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 96.89489997182305
- type: f1
value: 96.51138586512977
- type: f1_weighted
value: 96.89723065967186
- type: main_score
value: 96.89489997182305
task:
type: Classification
- dataset:
config: es
name: MTEB MTOPDomainClassification (es)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 97.17144763175452
- type: f1
value: 96.81785681878274
- type: f1_weighted
value: 97.1778974586874
- type: main_score
value: 97.17144763175452
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 96.30128405887879
- type: f1
value: 95.94555923088487
- type: f1_weighted
value: 96.30399416794926
- type: main_score
value: 96.30128405887879
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 84.53488372093022
- type: f1
value: 61.77995074251401
- type: f1_weighted
value: 86.8005170485101
- type: main_score
value: 84.53488372093022
task:
type: Classification
- dataset:
config: de
name: MTEB MTOPIntentClassification (de)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 80.79459002535924
- type: f1
value: 56.08938302001448
- type: f1_weighted
value: 83.66582131948252
- type: main_score
value: 80.79459002535924
task:
type: Classification
- dataset:
config: es
name: MTEB MTOPIntentClassification (es)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 84.7765176784523
- type: f1
value: 61.39860057885528
- type: f1_weighted
value: 86.94881745670745
- type: main_score
value: 84.7765176784523
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 82.2079549013467
- type: f1
value: 59.90260478749016
- type: f1_weighted
value: 84.36861708593257
- type: main_score
value: 82.2079549013467
task:
type: Classification
- dataset:
config: eng
name: MTEB MasakhaNEWSClassification (eng)
revision: 18193f187b92da67168c655c9973a165ed9593dd
split: test
type: mteb/masakhanews
metrics:
- type: accuracy
value: 74.98945147679325
- type: f1
value: 74.3157483560261
- type: f1_weighted
value: 75.01179008904884
- type: main_score
value: 74.98945147679325
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 18193f187b92da67168c655c9973a165ed9593dd
split: test
type: mteb/masakhanews
metrics:
- type: accuracy
value: 74.02843601895735
- type: f1
value: 70.40326349620732
- type: f1_weighted
value: 74.6596277063484
- type: main_score
value: 74.02843601895735
task:
type: Classification
- dataset:
config: amh
name: MTEB MasakhaNEWSClusteringP2P (amh)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 69.45780291725053
- type: v_measure
value: 69.45780291725053
- type: v_measure_std
value: 36.54340055904091
task:
type: Clustering
- dataset:
config: eng
name: MTEB MasakhaNEWSClusteringP2P (eng)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 64.88996119332239
- type: v_measure
value: 64.88996119332239
- type: v_measure_std
value: 30.017223408197268
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 42.362383958691666
- type: v_measure
value: 42.362383958691666
- type: v_measure_std
value: 37.61076788039063
task:
type: Clustering
- dataset:
config: hau
name: MTEB MasakhaNEWSClusteringP2P (hau)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 43.29201252405562
- type: v_measure
value: 43.29201252405562
- type: v_measure_std
value: 34.31987945146255
task:
type: Clustering
- dataset:
config: ibo
name: MTEB MasakhaNEWSClusteringP2P (ibo)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 33.59926542995238
- type: v_measure
value: 33.59926542995238
- type: v_measure_std
value: 35.70048601084112
task:
type: Clustering
- dataset:
config: lin
name: MTEB MasakhaNEWSClusteringP2P (lin)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 67.58487601893106
- type: v_measure
value: 67.58487601893106
- type: v_measure_std
value: 35.16784970777931
task:
type: Clustering
- dataset:
config: lug
name: MTEB MasakhaNEWSClusteringP2P (lug)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 50.01220872023533
- type: v_measure
value: 50.01220872023533
- type: v_measure_std
value: 41.87411574676182
task:
type: Clustering
- dataset:
config: orm
name: MTEB MasakhaNEWSClusteringP2P (orm)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 29.007847502598317
- type: v_measure
value: 29.007847502598317
- type: v_measure_std
value: 38.374997395079994
task:
type: Clustering
- dataset:
config: pcm
name: MTEB MasakhaNEWSClusteringP2P (pcm)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 79.13520228554611
- type: v_measure
value: 79.13520228554611
- type: v_measure_std
value: 18.501843848275183
task:
type: Clustering
- dataset:
config: run
name: MTEB MasakhaNEWSClusteringP2P (run)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 60.317213909746656
- type: v_measure
value: 60.317213909746656
- type: v_measure_std
value: 36.500281823747386
task:
type: Clustering
- dataset:
config: sna
name: MTEB MasakhaNEWSClusteringP2P (sna)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 59.395277358240946
- type: v_measure
value: 59.395277358240946
- type: v_measure_std
value: 37.500916816164654
task:
type: Clustering
- dataset:
config: som
name: MTEB MasakhaNEWSClusteringP2P (som)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 38.18638688704302
- type: v_measure
value: 38.18638688704302
- type: v_measure_std
value: 35.453681137564466
task:
type: Clustering
- dataset:
config: swa
name: MTEB MasakhaNEWSClusteringP2P (swa)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 29.49230755729658
- type: v_measure
value: 29.49230755729658
- type: v_measure_std
value: 28.284313285264645
task:
type: Clustering
- dataset:
config: tir
name: MTEB MasakhaNEWSClusteringP2P (tir)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 60.632258622750115
- type: v_measure
value: 60.632258622750115
- type: v_measure_std
value: 34.429711214740564
task:
type: Clustering
- dataset:
config: xho
name: MTEB MasakhaNEWSClusteringP2P (xho)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 41.76322918806381
- type: v_measure
value: 41.76322918806381
- type: v_measure_std
value: 36.43245296200775
task:
type: Clustering
- dataset:
config: yor
name: MTEB MasakhaNEWSClusteringP2P (yor)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 33.17083910808645
- type: v_measure
value: 33.17083910808645
- type: v_measure_std
value: 34.87547994284835
task:
type: Clustering
- dataset:
config: amh
name: MTEB MasakhaNEWSClusteringS2S (amh)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 60.95132147787602
- type: v_measure
value: 60.95132147787602
- type: v_measure_std
value: 37.330148394033365
task:
type: Clustering
- dataset:
config: eng
name: MTEB MasakhaNEWSClusteringS2S (eng)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 60.974810831426595
- type: v_measure
value: 60.974810831426595
- type: v_measure_std
value: 24.934675467507827
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 44.479206673553335
- type: v_measure
value: 44.479206673553335
- type: v_measure_std
value: 32.58254804499339
task:
type: Clustering
- dataset:
config: hau
name: MTEB MasakhaNEWSClusteringS2S (hau)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 26.4742082741682
- type: v_measure
value: 26.4742082741682
- type: v_measure_std
value: 22.344929192323097
task:
type: Clustering
- dataset:
config: ibo
name: MTEB MasakhaNEWSClusteringS2S (ibo)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 38.906129911741985
- type: v_measure
value: 38.906129911741985
- type: v_measure_std
value: 34.785601792668444
task:
type: Clustering
- dataset:
config: lin
name: MTEB MasakhaNEWSClusteringS2S (lin)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 62.60982020876592
- type: v_measure
value: 62.60982020876592
- type: v_measure_std
value: 40.7368955715045
task:
type: Clustering
- dataset:
config: lug
name: MTEB MasakhaNEWSClusteringS2S (lug)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 42.70424106365967
- type: v_measure
value: 42.70424106365967
- type: v_measure_std
value: 46.80946241135087
task:
type: Clustering
- dataset:
config: orm
name: MTEB MasakhaNEWSClusteringS2S (orm)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 28.609942199922322
- type: v_measure
value: 28.609942199922322
- type: v_measure_std
value: 38.46685040191088
task:
type: Clustering
- dataset:
config: pcm
name: MTEB MasakhaNEWSClusteringS2S (pcm)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 76.83901348810822
- type: v_measure
value: 76.83901348810822
- type: v_measure_std
value: 17.57617141269189
task:
type: Clustering
- dataset:
config: run
name: MTEB MasakhaNEWSClusteringS2S (run)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 46.89757547846193
- type: v_measure
value: 46.89757547846193
- type: v_measure_std
value: 44.58903590203438
task:
type: Clustering
- dataset:
config: sna
name: MTEB MasakhaNEWSClusteringS2S (sna)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 55.37185207068829
- type: v_measure
value: 55.37185207068829
- type: v_measure_std
value: 36.944574863543004
task:
type: Clustering
- dataset:
config: som
name: MTEB MasakhaNEWSClusteringS2S (som)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 37.44211021681754
- type: v_measure
value: 37.44211021681754
- type: v_measure_std
value: 33.41469994463241
task:
type: Clustering
- dataset:
config: swa
name: MTEB MasakhaNEWSClusteringS2S (swa)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 26.020680621216062
- type: v_measure
value: 26.020680621216062
- type: v_measure_std
value: 25.480037522570413
task:
type: Clustering
- dataset:
config: tir
name: MTEB MasakhaNEWSClusteringS2S (tir)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 63.74306846771303
- type: v_measure
value: 63.74306846771303
- type: v_measure_std
value: 32.19119631078685
task:
type: Clustering
- dataset:
config: xho
name: MTEB MasakhaNEWSClusteringS2S (xho)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 24.580890519243777
- type: v_measure
value: 24.580890519243777
- type: v_measure_std
value: 37.941836363967106
task:
type: Clustering
- dataset:
config: yor
name: MTEB MasakhaNEWSClusteringS2S (yor)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: main_score
value: 43.63458888828314
- type: v_measure
value: 43.63458888828314
- type: v_measure_std
value: 31.28169350649098
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 75.37323470073974
- type: f1
value: 71.1836877753734
- type: f1_weighted
value: 75.72073213955457
- type: main_score
value: 75.37323470073974
task:
type: Classification
- dataset:
config: de
name: MTEB MassiveIntentClassification (de)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 74.83523873570948
- type: f1
value: 70.72375821116886
- type: f1_weighted
value: 75.20800490010755
- type: main_score
value: 74.83523873570948
task:
type: Classification
- dataset:
config: es
name: MTEB MassiveIntentClassification (es)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 75.31607262945528
- type: f1
value: 72.06063554897662
- type: f1_weighted
value: 75.72438161355252
- type: main_score
value: 75.31607262945528
task:
type: Classification
- dataset:
config: ru
name: MTEB MassiveIntentClassification (ru)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.7955615332885
- type: f1
value: 73.08099648499756
- type: f1_weighted
value: 77.18482068239668
- type: main_score
value: 76.7955615332885
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 77.60591795561534
- type: f1
value: 74.46676705370395
- type: f1_weighted
value: 77.69888062336614
- type: main_score
value: 77.60591795561534
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.32145258910558
- type: f1
value: 72.89824154178328
- type: f1_weighted
value: 76.6539327979472
- type: main_score
value: 76.32145258910558
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 73.21788836583724
- type: f1
value: 70.45594512246377
- type: f1_weighted
value: 73.67862536499393
- type: main_score
value: 73.21788836583724
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 80.82044384667114
- type: f1
value: 80.53217664465089
- type: f1_weighted
value: 80.94535087010512
- type: main_score
value: 80.82044384667114
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 82.1049092131809
- type: f1
value: 81.55343463694733
- type: f1_weighted
value: 82.33509098770782
- type: main_score
value: 82.1049092131809
task:
type: Classification
- dataset:
config: es
name: MTEB MassiveScenarioClassification (es)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 82.58238063214526
- type: f1
value: 82.27974449333072
- type: f1_weighted
value: 82.81337569618209
- type: main_score
value: 82.58238063214526
task:
type: Classification
- dataset:
config: de
name: MTEB MassiveScenarioClassification (de)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 83.97108271687962
- type: f1
value: 83.56285606936076
- type: f1_weighted
value: 84.10198745390771
- type: main_score
value: 83.97108271687962
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 84.71082716879623
- type: f1
value: 84.09447062371402
- type: f1_weighted
value: 84.73765765551342
- type: main_score
value: 84.71082716879623
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 83.093476798924
- type: f1
value: 82.72656900752943
- type: f1_weighted
value: 83.26606516503364
- type: main_score
value: 83.093476798924
task:
type: Classification
- dataset:
config: ru
name: MTEB MassiveScenarioClassification (ru)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 84.05850706119705
- type: f1
value: 83.64234048881222
- type: f1_weighted
value: 84.17315768381876
- type: main_score
value: 84.05850706119705
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval (default)
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: main_score
value: 56.635999999999996
- type: map_at_1
value: 48.699999999999996
- type: map_at_10
value: 53.991
- type: map_at_100
value: 54.449999999999996
- type: map_at_1000
value: 54.515
- type: map_at_20
value: 54.212
- type: map_at_3
value: 52.833
- type: map_at_5
value: 53.503
- type: mrr_at_1
value: 48.699999999999996
- type: mrr_at_10
value: 53.991309523809505
- type: mrr_at_100
value: 54.45008993448266
- type: mrr_at_1000
value: 54.515253990549795
- type: mrr_at_20
value: 54.21201762247036
- type: mrr_at_3
value: 52.8333333333333
- type: mrr_at_5
value: 53.50333333333328
- type: nauc_map_at_1000_diff1
value: 79.96867989401643
- type: nauc_map_at_1000_max
value: 69.75230895599029
- type: nauc_map_at_1000_std
value: 2.6418738289740213
- type: nauc_map_at_100_diff1
value: 79.95343709599133
- type: nauc_map_at_100_max
value: 69.751282671507
- type: nauc_map_at_100_std
value: 2.621719966106279
- type: nauc_map_at_10_diff1
value: 80.02875864565634
- type: nauc_map_at_10_max
value: 69.80948662290187
- type: nauc_map_at_10_std
value: 2.329151604733765
- type: nauc_map_at_1_diff1
value: 83.616940281383
- type: nauc_map_at_1_max
value: 69.08142651929452
- type: nauc_map_at_1_std
value: 1.9687791394035643
- type: nauc_map_at_20_diff1
value: 79.95555601275339
- type: nauc_map_at_20_max
value: 69.76604695002925
- type: nauc_map_at_20_std
value: 2.556184141901367
- type: nauc_map_at_3_diff1
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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task:
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name: MTEB MintakaRetrieval (es)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
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task:
type: Retrieval
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
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task:
type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment (default)
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: test
type: C-MTEB/MultilingualSentiment-classification
metrics:
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task:
type: Classification
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config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
metrics:
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task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
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value: 88.34
- type: recall_at_3
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- type: recall_at_5
value: 73.894
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task:
type: Retrieval
- dataset:
config: default
name: MTEB Ocnli (default)
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
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value: 36627.362060546875
- type: dot_ap
value: 63.696303449293204
- type: dot_f1
value: 68.3986041101202
- type: dot_f1_threshold
value: 30452.72216796875
- type: dot_precision
value: 54.04411764705882
- type: dot_recall
value: 93.13621964097149
- type: euclidean_accuracy
value: 63.02111532214402
- type: euclidean_accuracy_threshold
value: 1392.76762008667
- type: euclidean_ap
value: 66.65907089443218
- type: euclidean_f1
value: 69.05036524413688
- type: euclidean_f1_threshold
value: 1711.5310668945312
- type: euclidean_precision
value: 54.29262394195889
- type: euclidean_recall
value: 94.82576557550159
- type: main_score
value: 63.02111532214402
- type: manhattan_accuracy
value: 62.75040606388739
- type: manhattan_accuracy_threshold
value: 32475.347900390625
- type: manhattan_ap
value: 66.50943585125434
- type: manhattan_f1
value: 69.08382066276802
- type: manhattan_f1_threshold
value: 41238.470458984375
- type: manhattan_precision
value: 54.75896168108776
- type: manhattan_recall
value: 93.55860612460401
- type: max_accuracy
value: 63.02111532214402
- type: max_ap
value: 66.65907089443218
- type: max_f1
value: 69.08382066276802
- type: max_precision
value: 57.485875706214685
- type: max_recall
value: 94.82576557550159
- type: similarity_accuracy
value: 62.3714131023281
- type: similarity_accuracy_threshold
value: 79.70921993255615
- type: similarity_ap
value: 66.41380155495659
- type: similarity_f1
value: 68.89547185780786
- type: similarity_f1_threshold
value: 72.91591167449951
- type: similarity_precision
value: 57.485875706214685
- type: similarity_recall
value: 85.95564941921859
task:
type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping (default)
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 91.88000000000001
- type: ap
value: 89.52463684448476
- type: ap_weighted
value: 89.52463684448476
- type: f1
value: 91.86313022306673
- type: f1_weighted
value: 91.87806318146912
- type: main_score
value: 91.88000000000001
task:
type: Classification
- dataset:
config: en
name: MTEB OpusparcusPC (en)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test.full
type: GEM/opusparcus
metrics:
- type: cosine_accuracy
value: 92.65578635014838
- type: cosine_accuracy_threshold
value: 74.02530312538147
- type: cosine_ap
value: 98.3834226153613
- type: cosine_f1
value: 94.92567913890312
- type: cosine_f1_threshold
value: 74.02530312538147
- type: cosine_precision
value: 95.562435500516
- type: cosine_recall
value: 94.29735234215886
- type: dot_accuracy
value: 91.54302670623146
- type: dot_accuracy_threshold
value: 34452.29187011719
- type: dot_ap
value: 98.1237257754439
- type: dot_f1
value: 94.22400803616273
- type: dot_f1_threshold
value: 33670.41931152344
- type: dot_precision
value: 92.9633300297324
- type: dot_recall
value: 95.5193482688391
- type: euclidean_accuracy
value: 92.28486646884274
- type: euclidean_accuracy_threshold
value: 1602.8022766113281
- type: euclidean_ap
value: 98.3099021504706
- type: euclidean_f1
value: 94.75277497477296
- type: euclidean_f1_threshold
value: 1604.7462463378906
- type: euclidean_precision
value: 93.89999999999999
- type: euclidean_recall
value: 95.62118126272912
- type: main_score
value: 98.3834226153613
- type: manhattan_accuracy
value: 92.2106824925816
- type: manhattan_accuracy_threshold
value: 38872.90954589844
- type: manhattan_ap
value: 98.28694101230218
- type: manhattan_f1
value: 94.67815509376584
- type: manhattan_f1_threshold
value: 38872.90954589844
- type: manhattan_precision
value: 94.24823410696267
- type: manhattan_recall
value: 95.11201629327903
- type: max_accuracy
value: 92.65578635014838
- type: max_ap
value: 98.3834226153613
- type: max_f1
value: 94.92567913890312
- type: max_precision
value: 95.562435500516
- type: max_recall
value: 95.62118126272912
- type: similarity_accuracy
value: 92.65578635014838
- type: similarity_accuracy_threshold
value: 74.02530312538147
- type: similarity_ap
value: 98.3834226153613
- type: similarity_f1
value: 94.92567913890312
- type: similarity_f1_threshold
value: 74.02530312538147
- type: similarity_precision
value: 95.562435500516
- type: similarity_recall
value: 94.29735234215886
task:
type: PairClassification
- dataset:
config: de
name: MTEB OpusparcusPC (de)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test.full
type: GEM/opusparcus
metrics:
- type: cosine_accuracy
value: 87.72178850248403
- type: cosine_accuracy_threshold
value: 73.33863377571106
- type: cosine_ap
value: 96.98901408834976
- type: cosine_f1
value: 91.89944134078212
- type: cosine_f1_threshold
value: 71.45810127258301
- type: cosine_precision
value: 89.64577656675749
- type: cosine_recall
value: 94.26934097421203
- type: dot_accuracy
value: 86.30234208658624
- type: dot_accuracy_threshold
value: 32027.130126953125
- type: dot_ap
value: 96.12260574893256
- type: dot_f1
value: 91.31602506714414
- type: dot_f1_threshold
value: 30804.376220703125
- type: dot_precision
value: 85.93091828138164
- type: dot_recall
value: 97.42120343839542
- type: euclidean_accuracy
value: 87.9347054648687
- type: euclidean_accuracy_threshold
value: 1609.6670150756836
- type: euclidean_ap
value: 97.00238860358252
- type: euclidean_f1
value: 92.1089063221043
- type: euclidean_f1_threshold
value: 1641.8487548828125
- type: euclidean_precision
value: 89.10714285714286
- type: euclidean_recall
value: 95.31996179560649
- type: main_score
value: 97.00238860358252
- type: manhattan_accuracy
value: 87.72178850248403
- type: manhattan_accuracy_threshold
value: 40137.060546875
- type: manhattan_ap
value: 96.98653728159941
- type: manhattan_f1
value: 92.03865623561896
- type: manhattan_f1_threshold
value: 40137.060546875
- type: manhattan_precision
value: 88.80994671403198
- type: manhattan_recall
value: 95.51098376313276
- type: max_accuracy
value: 87.9347054648687
- type: max_ap
value: 97.00238860358252
- type: max_f1
value: 92.1089063221043
- type: max_precision
value: 89.64577656675749
- type: max_recall
value: 97.42120343839542
- type: similarity_accuracy
value: 87.72178850248403
- type: similarity_accuracy_threshold
value: 73.33863377571106
- type: similarity_ap
value: 96.98901408834976
- type: similarity_f1
value: 91.89944134078212
- type: similarity_f1_threshold
value: 71.45810127258301
- type: similarity_precision
value: 89.64577656675749
- type: similarity_recall
value: 94.26934097421203
task:
type: PairClassification
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test.full
type: GEM/opusparcus
metrics:
- type: cosine_accuracy
value: 80.92643051771117
- type: cosine_accuracy_threshold
value: 76.68856382369995
- type: cosine_ap
value: 93.74622381534307
- type: cosine_f1
value: 87.12328767123287
- type: cosine_f1_threshold
value: 71.64022922515869
- type: cosine_precision
value: 80.64243448858834
- type: cosine_recall
value: 94.73684210526315
- type: dot_accuracy
value: 80.858310626703
- type: dot_accuracy_threshold
value: 34028.3935546875
- type: dot_ap
value: 91.18448457633308
- type: dot_f1
value: 86.82606657290202
- type: dot_f1_threshold
value: 34028.3935546875
- type: dot_precision
value: 82.2380106571936
- type: dot_recall
value: 91.9563058589871
- type: euclidean_accuracy
value: 80.858310626703
- type: euclidean_accuracy_threshold
value: 1595.7651138305664
- type: euclidean_ap
value: 93.8182717829648
- type: euclidean_f1
value: 87.04044117647058
- type: euclidean_f1_threshold
value: 1609.2475891113281
- type: euclidean_precision
value: 81.00940975192472
- type: euclidean_recall
value: 94.04170804369414
- type: main_score
value: 93.8182717829648
- type: manhattan_accuracy
value: 80.99455040871935
- type: manhattan_accuracy_threshold
value: 38092.132568359375
- type: manhattan_ap
value: 93.77563401151711
- type: manhattan_f1
value: 86.91983122362869
- type: manhattan_f1_threshold
value: 38092.132568359375
- type: manhattan_precision
value: 82.32682060390763
- type: manhattan_recall
value: 92.05561072492551
- type: max_accuracy
value: 80.99455040871935
- type: max_ap
value: 93.8182717829648
- type: max_f1
value: 87.12328767123287
- type: max_precision
value: 82.32682060390763
- type: max_recall
value: 94.73684210526315
- type: similarity_accuracy
value: 80.92643051771117
- type: similarity_accuracy_threshold
value: 76.68856382369995
- type: similarity_ap
value: 93.74622381534307
- type: similarity_f1
value: 87.12328767123287
- type: similarity_f1_threshold
value: 71.64022922515869
- type: similarity_precision
value: 80.64243448858834
- type: similarity_recall
value: 94.73684210526315
task:
type: PairClassification
- dataset:
config: ru
name: MTEB OpusparcusPC (ru)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test.full
type: GEM/opusparcus
metrics:
- type: cosine_accuracy
value: 76.83823529411765
- type: cosine_accuracy_threshold
value: 72.70769476890564
- type: cosine_ap
value: 89.56692049908222
- type: cosine_f1
value: 83.99832003359934
- type: cosine_f1_threshold
value: 70.9052324295044
- type: cosine_precision
value: 76.16146230007617
- type: cosine_recall
value: 93.63295880149812
- type: dot_accuracy
value: 76.28676470588235
- type: dot_accuracy_threshold
value: 33740.68908691406
- type: dot_ap
value: 87.77185177141567
- type: dot_f1
value: 83.62251375370292
- type: dot_f1_threshold
value: 32726.611328125
- type: dot_precision
value: 76.29343629343629
- type: dot_recall
value: 92.50936329588015
- type: euclidean_accuracy
value: 77.32843137254902
- type: euclidean_accuracy_threshold
value: 1566.510009765625
- type: euclidean_ap
value: 89.60605626791111
- type: euclidean_f1
value: 84.06546080964686
- type: euclidean_f1_threshold
value: 1576.4202117919922
- type: euclidean_precision
value: 77.83094098883574
- type: euclidean_recall
value: 91.38576779026218
- type: main_score
value: 89.60605626791111
- type: manhattan_accuracy
value: 76.89950980392157
- type: manhattan_accuracy_threshold
value: 38202.215576171875
- type: manhattan_ap
value: 89.55766894104868
- type: manhattan_f1
value: 83.80462724935732
- type: manhattan_f1_threshold
value: 38934.375
- type: manhattan_precision
value: 77.25118483412322
- type: manhattan_recall
value: 91.57303370786516
- type: max_accuracy
value: 77.32843137254902
- type: max_ap
value: 89.60605626791111
- type: max_f1
value: 84.06546080964686
- type: max_precision
value: 77.83094098883574
- type: max_recall
value: 93.63295880149812
- type: similarity_accuracy
value: 76.83823529411765
- type: similarity_accuracy_threshold
value: 72.70769476890564
- type: similarity_ap
value: 89.56692049908222
- type: similarity_f1
value: 83.99832003359934
- type: similarity_f1_threshold
value: 70.9052324295044
- type: similarity_precision
value: 76.16146230007617
- type: similarity_recall
value: 93.63295880149812
task:
type: PairClassification
- dataset:
config: default
name: MTEB PAC (default)
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 68.39559803069794
- type: ap
value: 77.68074206719457
- type: ap_weighted
value: 77.68074206719457
- type: f1
value: 66.23485605467732
- type: f1_weighted
value: 69.03201442129347
- type: main_score
value: 68.39559803069794
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX (default)
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
metrics:
- type: cosine_pearson
value: 13.161523266433587
- type: cosine_spearman
value: 15.557333873773386
- type: euclidean_pearson
value: 17.147508431907525
- type: euclidean_spearman
value: 15.664112857732146
- type: main_score
value: 15.557333873773386
- type: manhattan_pearson
value: 17.130875906264386
- type: manhattan_spearman
value: 15.624397342229637
- type: pearson
value: 13.161523266433587
- type: spearman
value: 15.557333873773386
task:
type: STS
- dataset:
config: default
name: MTEB PSC (default)
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cosine_accuracy
value: 97.86641929499072
- type: cosine_accuracy_threshold
value: 79.0391206741333
- type: cosine_ap
value: 99.19403807771533
- type: cosine_f1
value: 96.45608628659475
- type: cosine_f1_threshold
value: 79.0391206741333
- type: cosine_precision
value: 97.50778816199377
- type: cosine_recall
value: 95.42682926829268
- type: dot_accuracy
value: 98.14471243042672
- type: dot_accuracy_threshold
value: 29808.1787109375
- type: dot_ap
value: 99.331999859971
- type: dot_f1
value: 97.01492537313433
- type: dot_f1_threshold
value: 29808.1787109375
- type: dot_precision
value: 95.02923976608187
- type: dot_recall
value: 99.08536585365853
- type: euclidean_accuracy
value: 97.49536178107606
- type: euclidean_accuracy_threshold
value: 1276.227855682373
- type: euclidean_ap
value: 98.91056467717377
- type: euclidean_f1
value: 95.83975346687212
- type: euclidean_f1_threshold
value: 1276.227855682373
- type: euclidean_precision
value: 96.88473520249221
- type: euclidean_recall
value: 94.8170731707317
- type: main_score
value: 99.331999859971
- type: manhattan_accuracy
value: 97.49536178107606
- type: manhattan_accuracy_threshold
value: 31097.674560546875
- type: manhattan_ap
value: 98.95694691792707
- type: manhattan_f1
value: 95.83975346687212
- type: manhattan_f1_threshold
value: 31097.674560546875
- type: manhattan_precision
value: 96.88473520249221
- type: manhattan_recall
value: 94.8170731707317
- type: max_accuracy
value: 98.14471243042672
- type: max_ap
value: 99.331999859971
- type: max_f1
value: 97.01492537313433
- type: max_precision
value: 97.50778816199377
- type: max_recall
value: 99.08536585365853
- type: similarity_accuracy
value: 97.86641929499072
- type: similarity_accuracy_threshold
value: 79.0391206741333
- type: similarity_ap
value: 99.19403807771533
- type: similarity_f1
value: 96.45608628659475
- type: similarity_f1_threshold
value: 79.0391206741333
- type: similarity_precision
value: 97.50778816199377
- type: similarity_recall
value: 95.42682926829268
task:
type: PairClassification
- dataset:
config: en
name: MTEB PawsXPairClassification (en)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: google-research-datasets/paws-x
metrics:
- type: cosine_accuracy
value: 61.8
- type: cosine_accuracy_threshold
value: 99.5664119720459
- type: cosine_ap
value: 60.679317786040585
- type: cosine_f1
value: 63.17354143441101
- type: cosine_f1_threshold
value: 97.22164869308472
- type: cosine_precision
value: 47.6457399103139
- type: cosine_recall
value: 93.71554575523705
- type: dot_accuracy
value: 55.7
- type: dot_accuracy_threshold
value: 48353.62548828125
- type: dot_ap
value: 48.53805970536875
- type: dot_f1
value: 62.42214532871972
- type: dot_f1_threshold
value: 38215.53955078125
- type: dot_precision
value: 45.48663640948058
- type: dot_recall
value: 99.44873208379272
- type: euclidean_accuracy
value: 61.75000000000001
- type: euclidean_accuracy_threshold
value: 189.0761137008667
- type: euclidean_ap
value: 60.55517418691518
- type: euclidean_f1
value: 63.07977736549165
- type: euclidean_f1_threshold
value: 504.3168067932129
- type: euclidean_precision
value: 47.53914988814318
- type: euclidean_recall
value: 93.71554575523705
- type: main_score
value: 60.679317786040585
- type: manhattan_accuracy
value: 61.9
- type: manhattan_accuracy_threshold
value: 4695.778274536133
- type: manhattan_ap
value: 60.48686620413608
- type: manhattan_f1
value: 62.92880855772778
- type: manhattan_f1_threshold
value: 12542.36831665039
- type: manhattan_precision
value: 47.28381374722838
- type: manhattan_recall
value: 94.04630650496141
- type: max_accuracy
value: 61.9
- type: max_ap
value: 60.679317786040585
- type: max_f1
value: 63.17354143441101
- type: max_precision
value: 47.6457399103139
- type: max_recall
value: 99.44873208379272
- type: similarity_accuracy
value: 61.8
- type: similarity_accuracy_threshold
value: 99.5664119720459
- type: similarity_ap
value: 60.679317786040585
- type: similarity_f1
value: 63.17354143441101
- type: similarity_f1_threshold
value: 97.22164869308472
- type: similarity_precision
value: 47.6457399103139
- type: similarity_recall
value: 93.71554575523705
task:
type: PairClassification
- dataset:
config: de
name: MTEB PawsXPairClassification (de)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: google-research-datasets/paws-x
metrics:
- type: cosine_accuracy
value: 60.25
- type: cosine_accuracy_threshold
value: 99.54338073730469
- type: cosine_ap
value: 56.7863613689054
- type: cosine_f1
value: 62.23499820337766
- type: cosine_f1_threshold
value: 89.95014429092407
- type: cosine_precision
value: 45.86864406779661
- type: cosine_recall
value: 96.75977653631284
- type: dot_accuracy
value: 56.8
- type: dot_accuracy_threshold
value: 47349.78332519531
- type: dot_ap
value: 49.7857806061729
- type: dot_f1
value: 62.31225986727209
- type: dot_f1_threshold
value: 30143.206787109375
- type: dot_precision
value: 45.32520325203252
- type: dot_recall
value: 99.66480446927373
- type: euclidean_accuracy
value: 60.3
- type: euclidean_accuracy_threshold
value: 219.78106498718262
- type: euclidean_ap
value: 56.731544327179606
- type: euclidean_f1
value: 62.19895287958115
- type: euclidean_f1_threshold
value: 1792.1623229980469
- type: euclidean_precision
value: 45.22842639593909
- type: euclidean_recall
value: 99.55307262569832
- type: main_score
value: 56.7863613689054
- type: manhattan_accuracy
value: 60.150000000000006
- type: manhattan_accuracy_threshold
value: 5104.503631591797
- type: manhattan_ap
value: 56.70304479768734
- type: manhattan_f1
value: 62.22067039106145
- type: manhattan_f1_threshold
value: 42839.471435546875
- type: manhattan_precision
value: 45.2513966480447
- type: manhattan_recall
value: 99.55307262569832
- type: max_accuracy
value: 60.3
- type: max_ap
value: 56.7863613689054
- type: max_f1
value: 62.31225986727209
- type: max_precision
value: 45.86864406779661
- type: max_recall
value: 99.66480446927373
- type: similarity_accuracy
value: 60.25
- type: similarity_accuracy_threshold
value: 99.54338073730469
- type: similarity_ap
value: 56.7863613689054
- type: similarity_f1
value: 62.23499820337766
- type: similarity_f1_threshold
value: 89.95014429092407
- type: similarity_precision
value: 45.86864406779661
- type: similarity_recall
value: 96.75977653631284
task:
type: PairClassification
- dataset:
config: es
name: MTEB PawsXPairClassification (es)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: google-research-datasets/paws-x
metrics:
- type: cosine_accuracy
value: 59.699999999999996
- type: cosine_accuracy_threshold
value: 99.55930709838867
- type: cosine_ap
value: 57.31662248806265
- type: cosine_f1
value: 62.444061962134256
- type: cosine_f1_threshold
value: 74.75898265838623
- type: cosine_precision
value: 45.3953953953954
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 55.900000000000006
- type: dot_accuracy_threshold
value: 47512.90283203125
- type: dot_ap
value: 49.39339147787568
- type: dot_f1
value: 62.487082328625554
- type: dot_f1_threshold
value: 34989.03503417969
- type: dot_precision
value: 45.44088176352705
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 59.599999999999994
- type: euclidean_accuracy_threshold
value: 200.82547664642334
- type: euclidean_ap
value: 57.19737488445163
- type: euclidean_f1
value: 62.444061962134256
- type: euclidean_f1_threshold
value: 1538.8837814331055
- type: euclidean_precision
value: 45.3953953953954
- type: euclidean_recall
value: 100.0
- type: main_score
value: 57.31662248806265
- type: manhattan_accuracy
value: 59.550000000000004
- type: manhattan_accuracy_threshold
value: 5016.501617431641
- type: manhattan_ap
value: 57.089959907945065
- type: manhattan_f1
value: 62.444061962134256
- type: manhattan_f1_threshold
value: 37523.53515625
- type: manhattan_precision
value: 45.3953953953954
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 59.699999999999996
- type: max_ap
value: 57.31662248806265
- type: max_f1
value: 62.487082328625554
- type: max_precision
value: 45.44088176352705
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 59.699999999999996
- type: similarity_accuracy_threshold
value: 99.55930709838867
- type: similarity_ap
value: 57.31662248806265
- type: similarity_f1
value: 62.444061962134256
- type: similarity_f1_threshold
value: 74.75898265838623
- type: similarity_precision
value: 45.3953953953954
- type: similarity_recall
value: 100.0
task:
type: PairClassification
- dataset:
config: fr
name: MTEB PawsXPairClassification (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: google-research-datasets/paws-x
metrics:
- type: cosine_accuracy
value: 61.150000000000006
- type: cosine_accuracy_threshold
value: 99.36153888702393
- type: cosine_ap
value: 59.43845317938599
- type: cosine_f1
value: 62.51298026998961
- type: cosine_f1_threshold
value: 76.77866220474243
- type: cosine_precision
value: 45.468277945619334
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 55.75
- type: dot_accuracy_threshold
value: 48931.55212402344
- type: dot_ap
value: 50.15949290538757
- type: dot_f1
value: 62.53462603878117
- type: dot_f1_threshold
value: 34415.7958984375
- type: dot_precision
value: 45.4911838790932
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 61.050000000000004
- type: euclidean_accuracy_threshold
value: 240.8097267150879
- type: euclidean_ap
value: 59.367971294226216
- type: euclidean_f1
value: 62.51298026998961
- type: euclidean_f1_threshold
value: 1444.132423400879
- type: euclidean_precision
value: 45.468277945619334
- type: euclidean_recall
value: 100.0
- type: main_score
value: 59.43845317938599
- type: manhattan_accuracy
value: 60.95
- type: manhattan_accuracy_threshold
value: 5701.206207275391
- type: manhattan_ap
value: 59.30094096378774
- type: manhattan_f1
value: 62.53462603878117
- type: manhattan_f1_threshold
value: 33445.672607421875
- type: manhattan_precision
value: 45.4911838790932
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 61.150000000000006
- type: max_ap
value: 59.43845317938599
- type: max_f1
value: 62.53462603878117
- type: max_precision
value: 45.4911838790932
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 61.150000000000006
- type: similarity_accuracy_threshold
value: 99.36153888702393
- type: similarity_ap
value: 59.43845317938599
- type: similarity_f1
value: 62.51298026998961
- type: similarity_f1_threshold
value: 76.77866220474243
- type: similarity_precision
value: 45.468277945619334
- type: similarity_recall
value: 100.0
task:
type: PairClassification
- dataset:
config: zh
name: MTEB PawsXPairClassification (zh)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: google-research-datasets/paws-x
metrics:
- type: cosine_accuracy
value: 58.85
- type: cosine_accuracy_threshold
value: 99.73838329315186
- type: cosine_ap
value: 54.66913160570546
- type: cosine_f1
value: 62.32136632973162
- type: cosine_f1_threshold
value: 76.4499306678772
- type: cosine_precision
value: 45.265822784810126
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 56.25
- type: dot_accuracy_threshold
value: 47351.9287109375
- type: dot_ap
value: 48.5266232989438
- type: dot_f1
value: 62.277951933124356
- type: dot_f1_threshold
value: 31325.28076171875
- type: dot_precision
value: 45.220030349013655
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 58.9
- type: euclidean_accuracy_threshold
value: 144.24468278884888
- type: euclidean_ap
value: 54.66981490353506
- type: euclidean_f1
value: 62.32136632973162
- type: euclidean_f1_threshold
value: 1484.908676147461
- type: euclidean_precision
value: 45.265822784810126
- type: euclidean_recall
value: 100.0
- type: main_score
value: 54.66981490353506
- type: manhattan_accuracy
value: 58.9
- type: manhattan_accuracy_threshold
value: 3586.785125732422
- type: manhattan_ap
value: 54.668355260247736
- type: manhattan_f1
value: 62.32136632973162
- type: manhattan_f1_threshold
value: 36031.22863769531
- type: manhattan_precision
value: 45.265822784810126
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 58.9
- type: max_ap
value: 54.66981490353506
- type: max_f1
value: 62.32136632973162
- type: max_precision
value: 45.265822784810126
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 58.85
- type: similarity_accuracy_threshold
value: 99.73838329315186
- type: similarity_ap
value: 54.66913160570546
- type: similarity_f1
value: 62.32136632973162
- type: similarity_f1_threshold
value: 76.4499306678772
- type: similarity_precision
value: 45.265822784810126
- type: similarity_recall
value: 100.0
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN (default)
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 83.75346260387812
- type: f1
value: 81.98304891214909
- type: f1_weighted
value: 84.29623200830078
- type: main_score
value: 83.75346260387812
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT (default)
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 66.53846153846153
- type: f1
value: 52.71826064368638
- type: f1_weighted
value: 69.10010124630334
- type: main_score
value: 66.53846153846153
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cosine_accuracy
value: 81.8
- type: cosine_accuracy_threshold
value: 90.47793745994568
- type: cosine_ap
value: 91.42490266080884
- type: cosine_f1
value: 85.4632587859425
- type: cosine_f1_threshold
value: 90.47793745994568
- type: cosine_precision
value: 82.56172839506173
- type: cosine_recall
value: 88.57615894039735
- type: dot_accuracy
value: 74.6
- type: dot_accuracy_threshold
value: 42102.23693847656
- type: dot_ap
value: 86.20060009096979
- type: dot_f1
value: 80.02842928216063
- type: dot_f1_threshold
value: 38970.16906738281
- type: dot_precision
value: 70.1120797011208
- type: dot_recall
value: 93.21192052980133
- type: euclidean_accuracy
value: 81.5
- type: euclidean_accuracy_threshold
value: 880.433464050293
- type: euclidean_ap
value: 91.33143477982087
- type: euclidean_f1
value: 85.44600938967135
- type: euclidean_f1_threshold
value: 964.0384674072266
- type: euclidean_precision
value: 81.00890207715133
- type: euclidean_recall
value: 90.39735099337747
- type: main_score
value: 91.42490266080884
- type: manhattan_accuracy
value: 81.3
- type: manhattan_accuracy_threshold
value: 22100.830078125
- type: manhattan_ap
value: 91.25996158651282
- type: manhattan_f1
value: 85.38102643856921
- type: manhattan_f1_threshold
value: 24043.515014648438
- type: manhattan_precision
value: 80.49853372434018
- type: manhattan_recall
value: 90.89403973509934
- type: max_accuracy
value: 81.8
- type: max_ap
value: 91.42490266080884
- type: max_f1
value: 85.4632587859425
- type: max_precision
value: 82.56172839506173
- type: max_recall
value: 93.21192052980133
- type: similarity_accuracy
value: 81.8
- type: similarity_accuracy_threshold
value: 90.47793745994568
- type: similarity_ap
value: 91.42490266080884
- type: similarity_f1
value: 85.4632587859425
- type: similarity_f1_threshold
value: 90.47793745994568
- type: similarity_precision
value: 82.56172839506173
- type: similarity_recall
value: 88.57615894039735
task:
type: PairClassification
- dataset:
config: default
name: MTEB QuoraRetrieval (default)
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
split: test
type: mteb/quora
metrics:
- type: map_at_1
value: 71.419
- type: map_at_10
value: 85.542
- type: map_at_100
value: 86.161
- type: map_at_1000
value: 86.175
- type: map_at_20
value: 85.949
- type: map_at_3
value: 82.623
- type: map_at_5
value: 84.5
- type: mrr_at_1
value: 82.27
- type: mrr_at_10
value: 88.21900000000001
- type: mrr_at_100
value: 88.313
- type: mrr_at_1000
value: 88.31400000000001
- type: mrr_at_20
value: 88.286
- type: mrr_at_3
value: 87.325
- type: mrr_at_5
value: 87.97500000000001
- type: ndcg_at_1
value: 82.3
- type: ndcg_at_10
value: 89.088
- type: ndcg_at_100
value: 90.217
- type: ndcg_at_1000
value: 90.29700000000001
- type: ndcg_at_20
value: 89.697
- type: ndcg_at_3
value: 86.435
- type: ndcg_at_5
value: 87.966
- type: precision_at_1
value: 82.3
- type: precision_at_10
value: 13.527000000000001
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.165000000000001
- type: precision_at_3
value: 37.92
- type: precision_at_5
value: 24.914
- type: recall_at_1
value: 71.419
- type: recall_at_10
value: 95.831
- type: recall_at_100
value: 99.64
- type: recall_at_1000
value: 99.988
- type: recall_at_20
value: 97.76599999999999
- type: recall_at_3
value: 88.081
- type: recall_at_5
value: 92.50500000000001
- type: main_score
value: 89.088
task:
type: Retrieval
- dataset:
config: default
name: MTEB RUParaPhraserSTS (default)
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
split: test
type: merionum/ru_paraphraser
metrics:
- type: cosine_pearson
value: 67.91177744712421
- type: cosine_spearman
value: 76.77113726753656
- type: euclidean_pearson
value: 73.81454206068638
- type: euclidean_spearman
value: 76.92529493599028
- type: main_score
value: 76.77113726753656
- type: manhattan_pearson
value: 73.81690454439168
- type: manhattan_spearman
value: 76.87333776705002
- type: pearson
value: 67.91177744712421
- type: spearman
value: 76.77113726753656
task:
type: STS
- dataset:
config: default
name: MTEB RedditClustering (default)
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: main_score
value: 55.39924225216962
- type: v_measure
value: 55.39924225216962
- type: v_measure_std
value: 4.723802279292467
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P (default)
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: main_score
value: 62.87465161304012
- type: v_measure
value: 62.87465161304012
- type: v_measure_std
value: 12.082670914488473
task:
type: Clustering
- dataset:
config: default
name: MTEB RiaNewsRetrieval (default)
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
split: test
type: ai-forever/ria-news-retrieval
metrics:
- type: main_score
value: 79.209
- type: map_at_1
value: 67.33
- type: map_at_10
value: 75.633
- type: map_at_100
value: 75.897
- type: map_at_1000
value: 75.907
- type: map_at_20
value: 75.804
- type: map_at_3
value: 74.2
- type: map_at_5
value: 75.13300000000001
- type: mrr_at_1
value: 67.31
- type: mrr_at_10
value: 75.62709126984095
- type: mrr_at_100
value: 75.89105697041113
- type: mrr_at_1000
value: 75.90115653883124
- type: mrr_at_20
value: 75.79802332308172
- type: mrr_at_3
value: 74.19499999999961
- type: mrr_at_5
value: 75.12849999999939
- type: nauc_map_at_1000_diff1
value: 74.30304869630591
- type: nauc_map_at_1000_max
value: 36.477146725784046
- type: nauc_map_at_1000_std
value: -20.862772498461723
- type: nauc_map_at_100_diff1
value: 74.29833058090355
- type: nauc_map_at_100_max
value: 36.483678619667884
- type: nauc_map_at_100_std
value: -20.856274849980135
- type: nauc_map_at_10_diff1
value: 74.20729220697967
- type: nauc_map_at_10_max
value: 36.56543146170092
- type: nauc_map_at_10_std
value: -20.991081015484728
- type: nauc_map_at_1_diff1
value: 77.38899022125185
- type: nauc_map_at_1_max
value: 32.45918619669731
- type: nauc_map_at_1_std
value: -22.149586336167324
- type: nauc_map_at_20_diff1
value: 74.2447573558587
- type: nauc_map_at_20_max
value: 36.50383130240387
- type: nauc_map_at_20_std
value: -20.87013743041831
- type: nauc_map_at_3_diff1
value: 74.3054577294586
- type: nauc_map_at_3_max
value: 36.484530586652724
- type: nauc_map_at_3_std
value: -21.90543024607988
- type: nauc_map_at_5_diff1
value: 74.21062368961503
- type: nauc_map_at_5_max
value: 36.55670532498779
- type: nauc_map_at_5_std
value: -21.488786900676942
- type: nauc_mrr_at_1000_diff1
value: 74.31619177956684
- type: nauc_mrr_at_1000_max
value: 36.53498918453189
- type: nauc_mrr_at_1000_std
value: -20.75986704931237
- type: nauc_mrr_at_100_diff1
value: 74.31146790382356
- type: nauc_mrr_at_100_max
value: 36.54149252857106
- type: nauc_mrr_at_100_std
value: -20.75341959250079
- type: nauc_mrr_at_10_diff1
value: 74.22027806145095
- type: nauc_mrr_at_10_max
value: 36.622542969971725
- type: nauc_mrr_at_10_std
value: -20.889417384064117
- type: nauc_mrr_at_1_diff1
value: 77.4306709551449
- type: nauc_mrr_at_1_max
value: 32.57259463438259
- type: nauc_mrr_at_1_std
value: -21.964402859613937
- type: nauc_mrr_at_20_diff1
value: 74.25784396230718
- type: nauc_mrr_at_20_max
value: 36.561412224507336
- type: nauc_mrr_at_20_std
value: -20.767665000065723
- type: nauc_mrr_at_3_diff1
value: 74.31423253547214
- type: nauc_mrr_at_3_max
value: 36.537745749488906
- type: nauc_mrr_at_3_std
value: -21.81259529019546
- type: nauc_mrr_at_5_diff1
value: 74.22404613312771
- type: nauc_mrr_at_5_max
value: 36.60743768455219
- type: nauc_mrr_at_5_std
value: -21.39479216331971
- type: nauc_ndcg_at_1000_diff1
value: 73.48182819705742
- type: nauc_ndcg_at_1000_max
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- type: nauc_ndcg_at_1000_std
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- type: nauc_ndcg_at_100_diff1
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- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_100_std
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- type: nauc_ndcg_at_10_diff1
value: 72.82520265115987
- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
value: -19.410953792830878
- type: nauc_ndcg_at_1_diff1
value: 77.38899022125185
- type: nauc_ndcg_at_1_max
value: 32.45918619669731
- type: nauc_ndcg_at_1_std
value: -22.149586336167324
- type: nauc_ndcg_at_20_diff1
value: 72.93309285256507
- type: nauc_ndcg_at_20_max
value: 38.217372819067755
- type: nauc_ndcg_at_20_std
value: -18.864113576359333
- type: nauc_ndcg_at_3_diff1
value: 73.18253776744112
- type: nauc_ndcg_at_3_max
value: 38.008109328364
- type: nauc_ndcg_at_3_std
value: -21.68785687594153
- type: nauc_ndcg_at_5_diff1
value: 72.90474739784793
- type: nauc_ndcg_at_5_max
value: 38.29483039202184
- type: nauc_ndcg_at_5_std
value: -20.833049811453474
- type: nauc_precision_at_1000_diff1
value: 59.306217613750334
- type: nauc_precision_at_1000_max
value: 72.20747948302262
- type: nauc_precision_at_1000_std
value: 45.58837180096227
- type: nauc_precision_at_100_diff1
value: 62.87286844562389
- type: nauc_precision_at_100_max
value: 61.33108214045868
- type: nauc_precision_at_100_std
value: 20.67481963545654
- type: nauc_precision_at_10_diff1
value: 64.11222984256685
- type: nauc_precision_at_10_max
value: 50.323697746037496
- type: nauc_precision_at_10_std
value: -7.9994544634332625
- type: nauc_precision_at_1_diff1
value: 77.38899022125185
- type: nauc_precision_at_1_max
value: 32.45918619669731
- type: nauc_precision_at_1_std
value: -22.149586336167324
- type: nauc_precision_at_20_diff1
value: 62.30228127286973
- type: nauc_precision_at_20_max
value: 52.02090746208407
- type: nauc_precision_at_20_std
value: 0.7629898806370331
- type: nauc_precision_at_3_diff1
value: 68.82856645994157
- type: nauc_precision_at_3_max
value: 43.94171571306625
- type: nauc_precision_at_3_std
value: -20.78595255410148
- type: nauc_precision_at_5_diff1
value: 66.62157622497887
- type: nauc_precision_at_5_max
value: 46.69398173603811
- type: nauc_precision_at_5_std
value: -17.412423571163057
- type: nauc_recall_at_1000_diff1
value: 59.30621761375148
- type: nauc_recall_at_1000_max
value: 72.20747948302191
- type: nauc_recall_at_1000_std
value: 45.588371800962655
- type: nauc_recall_at_100_diff1
value: 62.872868445623894
- type: nauc_recall_at_100_max
value: 61.33108214045813
- type: nauc_recall_at_100_std
value: 20.67481963545666
- type: nauc_recall_at_10_diff1
value: 64.11222984256698
- type: nauc_recall_at_10_max
value: 50.32369774603755
- type: nauc_recall_at_10_std
value: -7.999454463433321
- type: nauc_recall_at_1_diff1
value: 77.38899022125185
- type: nauc_recall_at_1_max
value: 32.45918619669731
- type: nauc_recall_at_1_std
value: -22.149586336167324
- type: nauc_recall_at_20_diff1
value: 62.3022812728695
- type: nauc_recall_at_20_max
value: 52.02090746208397
- type: nauc_recall_at_20_std
value: 0.7629898806369458
- type: nauc_recall_at_3_diff1
value: 68.82856645994157
- type: nauc_recall_at_3_max
value: 43.94171571306612
- type: nauc_recall_at_3_std
value: -20.78595255410157
- type: nauc_recall_at_5_diff1
value: 66.62157622497897
- type: nauc_recall_at_5_max
value: 46.693981736038246
- type: nauc_recall_at_5_std
value: -17.412423571162954
- type: ndcg_at_1
value: 67.33
- type: ndcg_at_10
value: 79.209
- type: ndcg_at_100
value: 80.463
- type: ndcg_at_1000
value: 80.74799999999999
- type: ndcg_at_20
value: 79.81899999999999
- type: ndcg_at_3
value: 76.335
- type: ndcg_at_5
value: 78.011
- type: precision_at_1
value: 67.33
- type: precision_at_10
value: 9.020999999999999
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 4.63
- type: precision_at_3
value: 27.493000000000002
- type: precision_at_5
value: 17.308
- type: recall_at_1
value: 67.33
- type: recall_at_10
value: 90.21000000000001
- type: recall_at_100
value: 96.00999999999999
- type: recall_at_1000
value: 98.29
- type: recall_at_20
value: 92.60000000000001
- type: recall_at_3
value: 82.48
- type: recall_at_5
value: 86.53999999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB RuBQReranking (default)
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
split: test
type: ai-forever/rubq-reranking
metrics:
- type: main_score
value: 65.57453932493252
- type: map
value: 65.57453932493252
- type: mrr
value: 70.51408205663526
- type: nAUC_map_diff1
value: 26.69583260609023
- type: nAUC_map_max
value: 12.928262749610663
- type: nAUC_map_std
value: 11.702468857903128
- type: nAUC_mrr_diff1
value: 28.5206955462174
- type: nAUC_mrr_max
value: 14.207162454694227
- type: nAUC_mrr_std
value: 10.725721001555296
task:
type: Reranking
- dataset:
config: default
name: MTEB RuBQRetrieval (default)
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
split: test
type: ai-forever/rubq-retrieval
metrics:
- type: main_score
value: 72.306
- type: map_at_1
value: 44.187
- type: map_at_10
value: 64.836
- type: map_at_100
value: 65.771
- type: map_at_1000
value: 65.8
- type: map_at_20
value: 65.497
- type: map_at_3
value: 59.692
- type: map_at_5
value: 63.105
- type: mrr_at_1
value: 62.23404255319149
- type: mrr_at_10
value: 73.40810161732159
- type: mrr_at_100
value: 73.67949305473395
- type: mrr_at_1000
value: 73.68707852294746
- type: mrr_at_20
value: 73.60429051697479
- type: mrr_at_3
value: 71.47360126083535
- type: mrr_at_5
value: 72.8447596532704
- type: nauc_map_at_1000_diff1
value: 39.838449035736886
- type: nauc_map_at_1000_max
value: 32.29962306877408
- type: nauc_map_at_1000_std
value: -6.324859592714388
- type: nauc_map_at_100_diff1
value: 39.824361938745426
- type: nauc_map_at_100_max
value: 32.32055222704763
- type: nauc_map_at_100_std
value: -6.301641111869559
- type: nauc_map_at_10_diff1
value: 39.50155328718487
- type: nauc_map_at_10_max
value: 31.745730244960672
- type: nauc_map_at_10_std
value: -6.867215137329693
- type: nauc_map_at_1_diff1
value: 47.66181128677822
- type: nauc_map_at_1_max
value: 21.75204233166764
- type: nauc_map_at_1_std
value: -8.06951079061697
- type: nauc_map_at_20_diff1
value: 39.78364637902108
- type: nauc_map_at_20_max
value: 32.39065528029405
- type: nauc_map_at_20_std
value: -6.368994332729006
- type: nauc_map_at_3_diff1
value: 39.51829474433183
- type: nauc_map_at_3_max
value: 28.633292697821673
- type: nauc_map_at_3_std
value: -7.2561170814963925
- type: nauc_map_at_5_diff1
value: 39.288433237676266
- type: nauc_map_at_5_max
value: 31.007702201615515
- type: nauc_map_at_5_std
value: -7.235131195162474
- type: nauc_mrr_at_1000_diff1
value: 49.599102391215226
- type: nauc_mrr_at_1000_max
value: 38.25521825911133
- type: nauc_mrr_at_1000_std
value: -10.448180939809435
- type: nauc_mrr_at_100_diff1
value: 49.5957067716212
- type: nauc_mrr_at_100_max
value: 38.26760703964535
- type: nauc_mrr_at_100_std
value: -10.438443051971081
- type: nauc_mrr_at_10_diff1
value: 49.35269710190271
- type: nauc_mrr_at_10_max
value: 38.43782589127069
- type: nauc_mrr_at_10_std
value: -10.404402063509815
- type: nauc_mrr_at_1_diff1
value: 53.32206103688421
- type: nauc_mrr_at_1_max
value: 33.52402390241035
- type: nauc_mrr_at_1_std
value: -12.73473393949936
- type: nauc_mrr_at_20_diff1
value: 49.550630850826636
- type: nauc_mrr_at_20_max
value: 38.35964703941151
- type: nauc_mrr_at_20_std
value: -10.444577766284766
- type: nauc_mrr_at_3_diff1
value: 49.12029127633829
- type: nauc_mrr_at_3_max
value: 38.01631275124067
- type: nauc_mrr_at_3_std
value: -10.523724301481309
- type: nauc_mrr_at_5_diff1
value: 49.04606949432458
- type: nauc_mrr_at_5_max
value: 38.33647550077891
- type: nauc_mrr_at_5_std
value: -10.47076409263114
- type: nauc_ndcg_at_1000_diff1
value: 41.342785916264226
- type: nauc_ndcg_at_1000_max
value: 35.75731064862711
- type: nauc_ndcg_at_1000_std
value: -5.45573422899229
- type: nauc_ndcg_at_100_diff1
value: 40.972974559636086
- type: nauc_ndcg_at_100_max
value: 36.32938573321036
- type: nauc_ndcg_at_100_std
value: -4.749631537590004
- type: nauc_ndcg_at_10_diff1
value: 39.67813474464166
- type: nauc_ndcg_at_10_max
value: 35.480200504848966
- type: nauc_ndcg_at_10_std
value: -6.318561293935512
- type: nauc_ndcg_at_1_diff1
value: 53.45970160222764
- type: nauc_ndcg_at_1_max
value: 33.14759013278075
- type: nauc_ndcg_at_1_std
value: -12.579833891774847
- type: nauc_ndcg_at_20_diff1
value: 40.67492861219249
- type: nauc_ndcg_at_20_max
value: 36.84960799838019
- type: nauc_ndcg_at_20_std
value: -5.202530835850179
- type: nauc_ndcg_at_3_diff1
value: 39.574906207408844
- type: nauc_ndcg_at_3_max
value: 31.76512164509258
- type: nauc_ndcg_at_3_std
value: -7.656143208565999
- type: nauc_ndcg_at_5_diff1
value: 39.096348529742095
- type: nauc_ndcg_at_5_max
value: 34.075926475544165
- type: nauc_ndcg_at_5_std
value: -7.238045445366631
- type: nauc_precision_at_1000_diff1
value: -14.283799754212609
- type: nauc_precision_at_1000_max
value: 6.449741756717101
- type: nauc_precision_at_1000_std
value: 4.862828679759048
- type: nauc_precision_at_100_diff1
value: -13.23173132700258
- type: nauc_precision_at_100_max
value: 11.058898534529195
- type: nauc_precision_at_100_std
value: 7.343683941814956
- type: nauc_precision_at_10_diff1
value: -7.202951643546464
- type: nauc_precision_at_10_max
value: 17.499446869433278
- type: nauc_precision_at_10_std
value: 2.8367985220406307
- type: nauc_precision_at_1_diff1
value: 53.45970160222764
- type: nauc_precision_at_1_max
value: 33.14759013278075
- type: nauc_precision_at_1_std
value: -12.579833891774847
- type: nauc_precision_at_20_diff1
value: -9.477122699154124
- type: nauc_precision_at_20_max
value: 16.80556031564312
- type: nauc_precision_at_20_std
value: 6.420218284416923
- type: nauc_precision_at_3_diff1
value: 5.5276143574150245
- type: nauc_precision_at_3_max
value: 23.65952688481666
- type: nauc_precision_at_3_std
value: -1.8730348729295785
- type: nauc_precision_at_5_diff1
value: -2.4537029093721308
- type: nauc_precision_at_5_max
value: 21.41469327545133
- type: nauc_precision_at_5_std
value: 0.1543890645722277
- type: nauc_recall_at_1000_diff1
value: -1.7474947956413491
- type: nauc_recall_at_1000_max
value: 46.22670991970479
- type: nauc_recall_at_1000_std
value: 62.582840705588794
- type: nauc_recall_at_100_diff1
value: 16.116089801097345
- type: nauc_recall_at_100_max
value: 52.54794580975103
- type: nauc_recall_at_100_std
value: 33.720245696003246
- type: nauc_recall_at_10_diff1
value: 23.134924318655482
- type: nauc_recall_at_10_max
value: 38.73754275649077
- type: nauc_recall_at_10_std
value: 0.6137471711639239
- type: nauc_recall_at_1_diff1
value: 47.66181128677822
- type: nauc_recall_at_1_max
value: 21.75204233166764
- type: nauc_recall_at_1_std
value: -8.06951079061697
- type: nauc_recall_at_20_diff1
value: 24.130616271355017
- type: nauc_recall_at_20_max
value: 48.306178640146136
- type: nauc_recall_at_20_std
value: 9.290819557000022
- type: nauc_recall_at_3_diff1
value: 29.767415016250226
- type: nauc_recall_at_3_max
value: 28.54289782140701
- type: nauc_recall_at_3_std
value: -5.1395675072005576
- type: nauc_recall_at_5_diff1
value: 25.410613126870174
- type: nauc_recall_at_5_max
value: 33.24658754857624
- type: nauc_recall_at_5_std
value: -4.211226036746632
- type: ndcg_at_1
value: 62.175000000000004
- type: ndcg_at_10
value: 72.306
- type: ndcg_at_100
value: 75.074
- type: ndcg_at_1000
value: 75.581
- type: ndcg_at_20
value: 73.875
- type: ndcg_at_3
value: 65.641
- type: ndcg_at_5
value: 69.48299999999999
- type: precision_at_1
value: 62.175000000000004
- type: precision_at_10
value: 13.907
- type: precision_at_100
value: 1.591
- type: precision_at_1000
value: 0.166
- type: precision_at_20
value: 7.446999999999999
- type: precision_at_3
value: 35.619
- type: precision_at_5
value: 24.917
- type: recall_at_1
value: 44.187
- type: recall_at_10
value: 85.10600000000001
- type: recall_at_100
value: 95.488
- type: recall_at_1000
value: 98.831
- type: recall_at_20
value: 90.22200000000001
- type: recall_at_3
value: 68.789
- type: recall_at_5
value: 77.85499999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB RuReviewsClassification (default)
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
split: test
type: ai-forever/ru-reviews-classification
metrics:
- type: accuracy
value: 67.5830078125
- type: f1
value: 67.56931936632446
- type: f1_weighted
value: 67.57137733752779
- type: main_score
value: 67.5830078125
task:
type: Classification
- dataset:
config: default
name: MTEB RuSTSBenchmarkSTS (default)
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
split: test
type: ai-forever/ru-stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 85.90493484626788
- type: cosine_spearman
value: 86.21965691667411
- type: euclidean_pearson
value: 86.07499842984909
- type: euclidean_spearman
value: 86.55506818735688
- type: main_score
value: 86.21965691667411
- type: manhattan_pearson
value: 85.95976420231729
- type: manhattan_spearman
value: 86.48604243661234
- type: pearson
value: 85.90493484626788
- type: spearman
value: 86.21965691667411
task:
type: STS
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClassification (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
- type: accuracy
value: 59.1943359375
- type: f1
value: 58.894480861440414
- type: f1_weighted
value: 58.903615560240866
- type: main_score
value: 59.1943359375
task:
type: Classification
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
- type: main_score
value: 57.99209448663228
- type: v_measure
value: 57.99209448663228
- type: v_measure_std
value: 1.0381163861993816
task:
type: Clustering
- dataset:
config: default
name: MTEB RuSciBenchOECDClassification (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: accuracy
value: 45.556640625
- type: f1
value: 45.159163104085906
- type: f1_weighted
value: 45.16098316398626
- type: main_score
value: 45.556640625
task:
type: Classification
- dataset:
config: default
name: MTEB RuSciBenchOECDClusteringP2P (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: main_score
value: 50.787548070488974
- type: v_measure
value: 50.787548070488974
- type: v_measure_std
value: 0.8569958168946827
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
metrics:
- type: map_at_1
value: 4.843
- type: map_at_10
value: 11.752
- type: map_at_100
value: 13.919
- type: map_at_1000
value: 14.198
- type: map_at_20
value: 12.898000000000001
- type: map_at_3
value: 8.603
- type: map_at_5
value: 10.069
- type: mrr_at_1
value: 23.799999999999997
- type: mrr_at_10
value: 34.449999999999996
- type: mrr_at_100
value: 35.64
- type: mrr_at_1000
value: 35.691
- type: mrr_at_20
value: 35.213
- type: mrr_at_3
value: 31.383
- type: mrr_at_5
value: 33.062999999999995
- type: ndcg_at_1
value: 23.799999999999997
- type: ndcg_at_10
value: 19.811
- type: ndcg_at_100
value: 28.108
- type: ndcg_at_1000
value: 33.1
- type: ndcg_at_20
value: 22.980999999999998
- type: ndcg_at_3
value: 19.153000000000002
- type: ndcg_at_5
value: 16.408
- type: precision_at_1
value: 23.799999999999997
- type: precision_at_10
value: 10.16
- type: precision_at_100
value: 2.1999999999999997
- type: precision_at_1000
value: 0.34099999999999997
- type: precision_at_20
value: 6.915
- type: precision_at_3
value: 17.8
- type: precision_at_5
value: 14.14
- type: recall_at_1
value: 4.843
- type: recall_at_10
value: 20.595
- type: recall_at_100
value: 44.66
- type: recall_at_1000
value: 69.152
- type: recall_at_20
value: 28.04
- type: recall_at_3
value: 10.833
- type: recall_at_5
value: 14.346999999999998
- type: main_score
value: 19.811
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL (default)
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
- type: cosine_accuracy
value: 80.90093762739502
- type: cosine_accuracy_threshold
value: 94.40930485725403
- type: cosine_ap
value: 71.15400909912427
- type: cosine_f1
value: 66.8213457076566
- type: cosine_f1_threshold
value: 91.53673648834229
- type: cosine_precision
value: 62.4922504649721
- type: cosine_recall
value: 71.7948717948718
- type: dot_accuracy
value: 78.41418671015083
- type: dot_accuracy_threshold
value: 42924.45068359375
- type: dot_ap
value: 63.34003025365763
- type: dot_f1
value: 62.518258837277244
- type: dot_f1_threshold
value: 40900.738525390625
- type: dot_precision
value: 52.99653293709758
- type: dot_recall
value: 76.21082621082621
- type: euclidean_accuracy
value: 80.67672238075826
- type: euclidean_accuracy_threshold
value: 696.0524559020996
- type: euclidean_ap
value: 70.88762835990224
- type: euclidean_f1
value: 66.711051930759
- type: euclidean_f1_threshold
value: 878.5581588745117
- type: euclidean_precision
value: 62.625
- type: euclidean_recall
value: 71.36752136752136
- type: main_score
value: 71.15400909912427
- type: manhattan_accuracy
value: 80.65633917651854
- type: manhattan_accuracy_threshold
value: 17277.72674560547
- type: manhattan_ap
value: 70.67105336611716
- type: manhattan_f1
value: 66.51346027577151
- type: manhattan_f1_threshold
value: 21687.957763671875
- type: manhattan_precision
value: 61.69305724725944
- type: manhattan_recall
value: 72.15099715099716
- type: max_accuracy
value: 80.90093762739502
- type: max_ap
value: 71.15400909912427
- type: max_f1
value: 66.8213457076566
- type: max_precision
value: 62.625
- type: max_recall
value: 76.21082621082621
- type: similarity_accuracy
value: 80.90093762739502
- type: similarity_accuracy_threshold
value: 94.40930485725403
- type: similarity_ap
value: 71.15400909912427
- type: similarity_f1
value: 66.8213457076566
- type: similarity_f1_threshold
value: 91.53673648834229
- type: similarity_precision
value: 62.4922504649721
- type: similarity_recall
value: 71.7948717948718
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 92.3339946866199
- type: cosine_spearman
value: 89.61697355115497
- type: euclidean_pearson
value: 90.3264916449669
- type: euclidean_spearman
value: 89.36270451308866
- type: main_score
value: 89.61697355115497
- type: manhattan_pearson
value: 90.18909339052534
- type: manhattan_spearman
value: 89.28337093097377
- type: pearson
value: 92.3339946866199
- type: spearman
value: 89.61697355115497
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R-PL (default)
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
- type: cosine_pearson
value: 85.27883048457821
- type: cosine_spearman
value: 80.53204892678619
- type: euclidean_pearson
value: 82.78520705216168
- type: euclidean_spearman
value: 80.27848359873212
- type: main_score
value: 80.53204892678619
- type: manhattan_pearson
value: 82.63270640583454
- type: manhattan_spearman
value: 80.21507977473146
- type: pearson
value: 85.27883048457821
- type: spearman
value: 80.53204892678619
task:
type: STS
- dataset:
config: default
name: MTEB SICKFr (default)
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cosine_pearson
value: 88.77029361817212
- type: cosine_spearman
value: 83.9453600346894
- type: euclidean_pearson
value: 85.85331086208573
- type: euclidean_spearman
value: 83.70852031985308
- type: main_score
value: 83.9453600346894
- type: manhattan_pearson
value: 85.66222265885914
- type: manhattan_spearman
value: 83.60833111525962
- type: pearson
value: 88.77029361817212
- type: spearman
value: 83.9453600346894
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 88.76435859522375
- type: cosine_spearman
value: 82.43768167804375
- type: euclidean_pearson
value: 87.43566183874832
- type: euclidean_spearman
value: 82.82166873757507
- type: main_score
value: 82.43768167804375
- type: manhattan_pearson
value: 87.39450871380951
- type: manhattan_spearman
value: 82.89253043430163
- type: pearson
value: 88.76435859522375
- type: spearman
value: 82.43768167804375
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 88.86627241652141
- type: cosine_spearman
value: 89.49011599120688
- type: euclidean_pearson
value: 89.3314120073772
- type: euclidean_spearman
value: 89.8226502776963
- type: main_score
value: 89.49011599120688
- type: manhattan_pearson
value: 89.2252179076963
- type: manhattan_spearman
value: 89.74573844021225
- type: pearson
value: 88.86627241652141
- type: spearman
value: 89.49011599120688
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 87.22891405215968
- type: cosine_spearman
value: 84.9467188157614
- type: euclidean_pearson
value: 87.20330004726237
- type: euclidean_spearman
value: 85.34806059461808
- type: main_score
value: 84.9467188157614
- type: manhattan_pearson
value: 87.15224666107623
- type: manhattan_spearman
value: 85.34596898699708
- type: pearson
value: 87.22891405215968
- type: spearman
value: 84.9467188157614
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 88.14066430111033
- type: cosine_spearman
value: 89.31337445552545
- type: euclidean_pearson
value: 89.08039335366983
- type: euclidean_spearman
value: 89.6658762856415
- type: main_score
value: 89.31337445552545
- type: manhattan_pearson
value: 89.08057438154486
- type: manhattan_spearman
value: 89.68673984203022
- type: pearson
value: 88.14066430111033
- type: spearman
value: 89.31337445552545
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 85.14908856657084
- type: cosine_spearman
value: 86.84648320786727
- type: euclidean_pearson
value: 86.11454713131947
- type: euclidean_spearman
value: 86.77738862047961
- type: main_score
value: 86.84648320786727
- type: manhattan_pearson
value: 86.07804821916372
- type: manhattan_spearman
value: 86.78676064310474
- type: pearson
value: 85.14908856657084
- type: spearman
value: 86.84648320786727
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 89.61633502468356
- type: cosine_spearman
value: 89.99772663224805
- type: euclidean_pearson
value: 90.14056501501044
- type: euclidean_spearman
value: 90.04496896837503
- type: main_score
value: 89.99772663224805
- type: manhattan_pearson
value: 90.08964860311801
- type: manhattan_spearman
value: 90.00091712362196
- type: pearson
value: 89.61633502468356
- type: spearman
value: 89.99772663224805
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 86.44548026840202
- type: cosine_spearman
value: 87.26263108768539
- type: euclidean_pearson
value: 86.42844593583838
- type: euclidean_spearman
value: 86.89388428664364
- type: main_score
value: 87.26263108768539
- type: manhattan_pearson
value: 86.47186940800881
- type: manhattan_spearman
value: 87.02163091089946
- type: pearson
value: 86.44548026840202
- type: spearman
value: 87.26263108768539
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 87.89345132532758
- type: cosine_spearman
value: 87.96246221327699
- type: euclidean_pearson
value: 88.49013032701419
- type: euclidean_spearman
value: 87.81981265317344
- type: main_score
value: 87.96246221327699
- type: manhattan_pearson
value: 88.31360914178538
- type: manhattan_spearman
value: 87.62734530005075
- type: pearson
value: 87.89345132532758
- type: spearman
value: 87.96246221327699
task:
type: STS
- dataset:
config: es-es
name: MTEB STS17 (es-es)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 88.4084678497171
- type: cosine_spearman
value: 88.77640638748285
- type: euclidean_pearson
value: 89.60124312475843
- type: euclidean_spearman
value: 88.4321442688528
- type: main_score
value: 88.77640638748285
- type: manhattan_pearson
value: 89.62375118021299
- type: manhattan_spearman
value: 88.46998118661577
- type: pearson
value: 88.4084678497171
- type: spearman
value: 88.77640638748285
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 87.30688801326498
- type: cosine_spearman
value: 87.55684697258378
- type: euclidean_pearson
value: 87.89672951056794
- type: euclidean_spearman
value: 87.28050429201674
- type: main_score
value: 87.55684697258378
- type: manhattan_pearson
value: 87.74292745320572
- type: manhattan_spearman
value: 87.16383993876582
- type: pearson
value: 87.30688801326498
- type: spearman
value: 87.55684697258378
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 73.46180375170147
- type: cosine_spearman
value: 73.39559590127081
- type: euclidean_pearson
value: 73.72613901293681
- type: euclidean_spearman
value: 71.85465165176795
- type: main_score
value: 73.39559590127081
- type: manhattan_pearson
value: 73.07859140869076
- type: manhattan_spearman
value: 71.22047343718893
- type: pearson
value: 73.46180375170147
- type: spearman
value: 73.39559590127081
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 62.47531620842637
- type: cosine_spearman
value: 66.22504667157702
- type: euclidean_pearson
value: 66.76201254783692
- type: euclidean_spearman
value: 66.86115760269463
- type: main_score
value: 66.22504667157702
- type: manhattan_pearson
value: 66.73847836793489
- type: manhattan_spearman
value: 66.7677116377695
- type: pearson
value: 62.47531620842637
- type: spearman
value: 66.22504667157702
task:
type: STS
- dataset:
config: es
name: MTEB STS22 (es)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 69.89707002436481
- type: cosine_spearman
value: 72.2054865735116
- type: euclidean_pearson
value: 71.81856615570756
- type: euclidean_spearman
value: 72.72593304629407
- type: main_score
value: 72.2054865735116
- type: manhattan_pearson
value: 72.00362684700072
- type: manhattan_spearman
value: 72.62783534769964
- type: pearson
value: 69.89707002436481
- type: spearman
value: 72.2054865735116
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 81.59623734395916
- type: cosine_spearman
value: 83.28946105111358
- type: euclidean_pearson
value: 79.377330171466
- type: euclidean_spearman
value: 81.81029781662205
- type: main_score
value: 83.28946105111358
- type: manhattan_pearson
value: 78.96970881689698
- type: manhattan_spearman
value: 81.91773236079703
- type: pearson
value: 81.59623734395916
- type: spearman
value: 83.28946105111358
task:
type: STS
- dataset:
config: de-fr
name: MTEB STS22 (de-fr)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 55.03825643126142
- type: cosine_spearman
value: 58.25792501780429
- type: euclidean_pearson
value: 50.38007603973409
- type: euclidean_spearman
value: 59.39961789383097
- type: main_score
value: 58.25792501780429
- type: manhattan_pearson
value: 50.518568927999155
- type: manhattan_spearman
value: 59.84185466003894
- type: pearson
value: 55.03825643126142
- type: spearman
value: 58.25792501780429
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 77.77233721490776
- type: cosine_spearman
value: 76.17596588017625
- type: euclidean_pearson
value: 74.47600468156611
- type: euclidean_spearman
value: 72.61278728057012
- type: main_score
value: 76.17596588017625
- type: manhattan_pearson
value: 74.48118910099699
- type: manhattan_spearman
value: 73.33167419101696
- type: pearson
value: 77.77233721490776
- type: spearman
value: 76.17596588017625
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 42.87453608131507
- type: cosine_spearman
value: 45.137849894401185
- type: euclidean_pearson
value: 31.66964197694796
- type: euclidean_spearman
value: 44.1014900837869
- type: main_score
value: 45.137849894401185
- type: manhattan_pearson
value: 31.007199259384745
- type: manhattan_spearman
value: 43.48181523288926
- type: pearson
value: 42.87453608131507
- type: spearman
value: 45.137849894401185
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 66.87400150638176
- type: cosine_spearman
value: 67.27861354834066
- type: euclidean_pearson
value: 66.81789582140216
- type: euclidean_spearman
value: 66.44220479858708
- type: main_score
value: 67.27861354834066
- type: manhattan_pearson
value: 66.92509859033235
- type: manhattan_spearman
value: 66.46841124185076
- type: pearson
value: 66.87400150638176
- type: spearman
value: 67.27861354834066
task:
type: STS
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 61.819804551576084
- type: cosine_spearman
value: 65.0864146772135
- type: euclidean_pearson
value: 62.518151090361876
- type: euclidean_spearman
value: 65.13608138548017
- type: main_score
value: 65.0864146772135
- type: manhattan_pearson
value: 62.51413246915267
- type: manhattan_spearman
value: 65.19077543064323
- type: pearson
value: 61.819804551576084
- type: spearman
value: 65.0864146772135
task:
type: STS
- dataset:
config: de
name: MTEB STS22 (de)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 54.85728696035389
- type: cosine_spearman
value: 61.60906359227576
- type: euclidean_pearson
value: 52.57582587901851
- type: euclidean_spearman
value: 61.41823097598308
- type: main_score
value: 61.60906359227576
- type: manhattan_pearson
value: 52.500978361080506
- type: manhattan_spearman
value: 61.30365596659758
- type: pearson
value: 54.85728696035389
- type: spearman
value: 61.60906359227576
task:
type: STS
- dataset:
config: fr-pl
name: MTEB STS22 (fr-pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 67.68016005631422
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 66.19871164667245
- type: euclidean_spearman
value: 73.24670207647144
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 67.0443525268974
- type: manhattan_spearman
value: 73.24670207647144
- type: pearson
value: 67.68016005631422
- type: spearman
value: 84.51542547285167
task:
type: STS
- dataset:
config: de-pl
name: MTEB STS22 (de-pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 47.49467414030747
- type: cosine_spearman
value: 56.81512095681289
- type: euclidean_pearson
value: 48.42860221765214
- type: euclidean_spearman
value: 58.63197306329092
- type: main_score
value: 56.81512095681289
- type: manhattan_pearson
value: 48.39594959260441
- type: manhattan_spearman
value: 58.63197306329092
- type: pearson
value: 47.49467414030747
- type: spearman
value: 56.81512095681289
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 76.8364678896155
- type: cosine_spearman
value: 78.45516413087114
- type: euclidean_pearson
value: 78.62779318576634
- type: euclidean_spearman
value: 78.88760695649488
- type: main_score
value: 78.45516413087114
- type: manhattan_pearson
value: 78.62131335760031
- type: manhattan_spearman
value: 78.81861844200388
- type: pearson
value: 76.8364678896155
- type: spearman
value: 78.45516413087114
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 65.16640313911604
- type: cosine_spearman
value: 60.887608967403914
- type: euclidean_pearson
value: 67.49902244990913
- type: euclidean_spearman
value: 59.2458787136538
- type: main_score
value: 60.887608967403914
- type: manhattan_pearson
value: 67.34313506388378
- type: manhattan_spearman
value: 59.05283429200166
- type: pearson
value: 65.16640313911604
- type: spearman
value: 60.887608967403914
task:
type: STS
- dataset:
config: default
name: MTEB STSB (default)
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
metrics:
- type: cosine_pearson
value: 81.5092853013241
- type: cosine_spearman
value: 83.54005474244292
- type: euclidean_pearson
value: 83.7246578378554
- type: euclidean_spearman
value: 84.46767551087716
- type: main_score
value: 83.54005474244292
- type: manhattan_pearson
value: 83.65922665594636
- type: manhattan_spearman
value: 84.42431449101848
- type: pearson
value: 81.5092853013241
- type: spearman
value: 83.54005474244292
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 87.70246866744966
- type: cosine_spearman
value: 89.44070045346106
- type: euclidean_pearson
value: 89.56956519641007
- type: euclidean_spearman
value: 89.95830112784283
- type: main_score
value: 89.44070045346106
- type: manhattan_pearson
value: 89.48264471425145
- type: manhattan_spearman
value: 89.87900732483114
- type: pearson
value: 87.70246866744966
- type: spearman
value: 89.44070045346106
task:
type: STS
- dataset:
config: de
name: MTEB STSBenchmarkMultilingualSTS (de)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 86.83701990805217
- type: cosine_spearman
value: 87.80280785492258
- type: euclidean_pearson
value: 87.77325330043514
- type: euclidean_spearman
value: 88.3564607283144
- type: main_score
value: 87.80280785492258
- type: manhattan_pearson
value: 87.6745449945946
- type: manhattan_spearman
value: 88.30660465978795
- type: pearson
value: 86.83701990805217
- type: spearman
value: 87.80280785492258
task:
type: STS
- dataset:
config: zh
name: MTEB STSBenchmarkMultilingualSTS (zh)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 84.27751020600267
- type: cosine_spearman
value: 85.63500407412486
- type: euclidean_pearson
value: 85.21829891649696
- type: euclidean_spearman
value: 85.9384575715382
- type: main_score
value: 85.63500407412486
- type: manhattan_pearson
value: 85.10797194089801
- type: manhattan_spearman
value: 85.8770162042784
- type: pearson
value: 84.27751020600267
- type: spearman
value: 85.63500407412486
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 86.56833656723254
- type: cosine_spearman
value: 87.4393978501382
- type: euclidean_pearson
value: 87.45171512751267
- type: euclidean_spearman
value: 88.13106516566947
- type: main_score
value: 87.4393978501382
- type: manhattan_pearson
value: 87.33010961793333
- type: manhattan_spearman
value: 88.06707425102182
- type: pearson
value: 86.56833656723254
- type: spearman
value: 87.4393978501382
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 85.45065540325523
- type: cosine_spearman
value: 85.47881076789359
- type: euclidean_pearson
value: 85.1999493863155
- type: euclidean_spearman
value: 85.7874947669187
- type: main_score
value: 85.47881076789359
- type: manhattan_pearson
value: 85.06075305990376
- type: manhattan_spearman
value: 85.71563015639558
- type: pearson
value: 85.45065540325523
- type: spearman
value: 85.47881076789359
task:
type: STS
- dataset:
config: es
name: MTEB STSBenchmarkMultilingualSTS (es)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 87.11952824079832
- type: cosine_spearman
value: 87.9643473573153
- type: euclidean_pearson
value: 88.11750364639971
- type: euclidean_spearman
value: 88.63695109016498
- type: main_score
value: 87.9643473573153
- type: manhattan_pearson
value: 88.00294453126699
- type: manhattan_spearman
value: 88.53750241758391
- type: pearson
value: 87.11952824079832
- type: spearman
value: 87.9643473573153
task:
type: STS
- dataset:
config: ru
name: MTEB STSBenchmarkMultilingualSTS (ru)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 85.99804354414991
- type: cosine_spearman
value: 86.30252111551002
- type: euclidean_pearson
value: 86.1880652037762
- type: euclidean_spearman
value: 86.69556223944502
- type: main_score
value: 86.30252111551002
- type: manhattan_pearson
value: 86.0736400320898
- type: manhattan_spearman
value: 86.61747927593393
- type: pearson
value: 85.99804354414991
- type: spearman
value: 86.30252111551002
task:
type: STS
- dataset:
config: en
name: MTEB STSBenchmarkMultilingualSTS (en)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 87.70246861738103
- type: cosine_spearman
value: 89.44070045346106
- type: euclidean_pearson
value: 89.56956518833663
- type: euclidean_spearman
value: 89.95830112784283
- type: main_score
value: 89.44070045346106
- type: manhattan_pearson
value: 89.48264470792915
- type: manhattan_spearman
value: 89.87900732483114
- type: pearson
value: 87.70246861738103
- type: spearman
value: 89.44070045346106
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 84.88064122814694
- type: mrr
value: 95.84832651009123
- type: main_score
value: 84.88064122814694
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: map_at_1
value: 57.289
- type: map_at_10
value: 67.88499999999999
- type: map_at_100
value: 68.477
- type: map_at_1000
value: 68.50500000000001
- type: map_at_20
value: 68.33500000000001
- type: map_at_3
value: 65.08
- type: map_at_5
value: 67.001
- type: mrr_at_1
value: 59.667
- type: mrr_at_10
value: 68.626
- type: mrr_at_100
value: 69.082
- type: mrr_at_1000
value: 69.108
- type: mrr_at_20
value: 68.958
- type: mrr_at_3
value: 66.667
- type: mrr_at_5
value: 67.983
- type: ndcg_at_1
value: 59.667
- type: ndcg_at_10
value: 72.309
- type: ndcg_at_100
value: 74.58399999999999
- type: ndcg_at_1000
value: 75.25500000000001
- type: ndcg_at_20
value: 73.656
- type: ndcg_at_3
value: 67.791
- type: ndcg_at_5
value: 70.45
- type: precision_at_1
value: 59.667
- type: precision_at_10
value: 9.567
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.083
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 57.289
- type: recall_at_10
value: 84.756
- type: recall_at_100
value: 94.5
- type: recall_at_1000
value: 99.667
- type: recall_at_20
value: 89.7
- type: recall_at_3
value: 73.22800000000001
- type: recall_at_5
value: 79.444
- type: main_score
value: 72.309
task:
type: Retrieval
- dataset:
config: default
name: MTEB SpanishNewsClusteringP2P (default)
revision: bf8ca8ddc5b7da4f7004720ddf99bbe0483480e6
split: test
type: jinaai/spanish_news_clustering
metrics:
- type: main_score
value: 45.04477709795154
- type: v_measure
value: 45.04477709795154
- type: v_measure_std
value: 0.0
task:
type: Clustering
- dataset:
config: default
name: MTEB SpanishPassageRetrievalS2S (default)
revision: 9cddf2ce5209ade52c2115ccfa00eb22c6d3a837
split: test
type: jinaai/spanish_passage_retrieval
metrics:
- type: main_score
value: 69.83
- type: map_at_1
value: 15.736
- type: map_at_10
value: 52.027
- type: map_at_100
value: 65.08800000000001
- type: map_at_1000
value: 65.08800000000001
- type: map_at_20
value: 60.79900000000001
- type: map_at_3
value: 32.869
- type: map_at_5
value: 41.436
- type: mrr_at_1
value: 75.44910179640718
- type: mrr_at_10
value: 84.43446440452426
- type: mrr_at_100
value: 84.48052612723271
- type: mrr_at_1000
value: 84.48052612723271
- type: mrr_at_20
value: 84.48052612723271
- type: mrr_at_3
value: 83.13373253493013
- type: mrr_at_5
value: 84.3013972055888
- type: nauc_map_at_1000_diff1
value: 50.611540149694356
- type: nauc_map_at_1000_max
value: 2.1102430434260238
- type: nauc_map_at_1000_std
value: -18.88993521335793
- type: nauc_map_at_100_diff1
value: 50.611540149694356
- type: nauc_map_at_100_max
value: 2.1102430434260238
- type: nauc_map_at_100_std
value: -18.88993521335793
- type: nauc_map_at_10_diff1
value: 59.13518981755268
- type: nauc_map_at_10_max
value: -9.810386627392807
- type: nauc_map_at_10_std
value: -38.31810152345078
- type: nauc_map_at_1_diff1
value: 74.96782567287174
- type: nauc_map_at_1_max
value: -29.648279252607875
- type: nauc_map_at_1_std
value: -54.017459339141595
- type: nauc_map_at_20_diff1
value: 55.26694458629849
- type: nauc_map_at_20_max
value: -1.9490244535020729
- type: nauc_map_at_20_std
value: -25.22211659104076
- type: nauc_map_at_3_diff1
value: 71.67607885031732
- type: nauc_map_at_3_max
value: -25.078101661694507
- type: nauc_map_at_3_std
value: -50.55408861920259
- type: nauc_map_at_5_diff1
value: 61.50111515417668
- type: nauc_map_at_5_max
value: -16.4114670513168
- type: nauc_map_at_5_std
value: -44.391416134859135
- type: nauc_mrr_at_1000_diff1
value: 74.18848063283234
- type: nauc_mrr_at_1000_max
value: 21.929205946778005
- type: nauc_mrr_at_1000_std
value: -36.27399268489433
- type: nauc_mrr_at_100_diff1
value: 74.18848063283234
- type: nauc_mrr_at_100_max
value: 21.929205946778005
- type: nauc_mrr_at_100_std
value: -36.27399268489433
- type: nauc_mrr_at_10_diff1
value: 74.27231582268745
- type: nauc_mrr_at_10_max
value: 21.481133301135337
- type: nauc_mrr_at_10_std
value: -36.72070854872902
- type: nauc_mrr_at_1_diff1
value: 76.54855950439561
- type: nauc_mrr_at_1_max
value: 26.99938321212366
- type: nauc_mrr_at_1_std
value: -33.098742603429635
- type: nauc_mrr_at_20_diff1
value: 74.18848063283234
- type: nauc_mrr_at_20_max
value: 21.929205946778005
- type: nauc_mrr_at_20_std
value: -36.27399268489433
- type: nauc_mrr_at_3_diff1
value: 72.05379526740143
- type: nauc_mrr_at_3_max
value: 18.875831185752528
- type: nauc_mrr_at_3_std
value: -37.27302006456391
- type: nauc_mrr_at_5_diff1
value: 74.25342356682029
- type: nauc_mrr_at_5_max
value: 20.756340085088738
- type: nauc_mrr_at_5_std
value: -37.99507208540703
- type: nauc_ndcg_at_1000_diff1
value: 53.259363764380275
- type: nauc_ndcg_at_1000_max
value: 12.936954959423218
- type: nauc_ndcg_at_1000_std
value: -16.953898675672153
- type: nauc_ndcg_at_100_diff1
value: 53.259363764380275
- type: nauc_ndcg_at_100_max
value: 12.936954959423218
- type: nauc_ndcg_at_100_std
value: -16.953898675672153
- type: nauc_ndcg_at_10_diff1
value: 53.70942345413554
- type: nauc_ndcg_at_10_max
value: -3.8465093347016186
- type: nauc_ndcg_at_10_std
value: -31.208127919994755
- type: nauc_ndcg_at_1_diff1
value: 75.30551289259554
- type: nauc_ndcg_at_1_max
value: 25.53292054129834
- type: nauc_ndcg_at_1_std
value: -33.285498788395145
- type: nauc_ndcg_at_20_diff1
value: 57.62409278278133
- type: nauc_ndcg_at_20_max
value: 2.8040586426056233
- type: nauc_ndcg_at_20_std
value: -26.270875776221704
- type: nauc_ndcg_at_3_diff1
value: 48.42294834754225
- type: nauc_ndcg_at_3_max
value: 16.912467881065822
- type: nauc_ndcg_at_3_std
value: -13.324841189277873
- type: nauc_ndcg_at_5_diff1
value: 47.512819802794596
- type: nauc_ndcg_at_5_max
value: 14.645518203506594
- type: nauc_ndcg_at_5_std
value: -17.641450435599275
- type: nauc_precision_at_1000_diff1
value: -34.43320975829637
- type: nauc_precision_at_1000_max
value: 29.08585622578186
- type: nauc_precision_at_1000_std
value: 46.55117940162061
- type: nauc_precision_at_100_diff1
value: -34.433209758296364
- type: nauc_precision_at_100_max
value: 29.085856225781885
- type: nauc_precision_at_100_std
value: 46.55117940162065
- type: nauc_precision_at_10_diff1
value: -21.895306304096902
- type: nauc_precision_at_10_max
value: 33.190476527593745
- type: nauc_precision_at_10_std
value: 37.64916268614298
- type: nauc_precision_at_1_diff1
value: 75.30551289259554
- type: nauc_precision_at_1_max
value: 25.53292054129834
- type: nauc_precision_at_1_std
value: -33.285498788395145
- type: nauc_precision_at_20_diff1
value: -27.63076748060466
- type: nauc_precision_at_20_max
value: 30.689810416086154
- type: nauc_precision_at_20_std
value: 46.164191636131626
- type: nauc_precision_at_3_diff1
value: 20.547345067837288
- type: nauc_precision_at_3_max
value: 26.177050942827528
- type: nauc_precision_at_3_std
value: 5.960466052973099
- type: nauc_precision_at_5_diff1
value: -8.928755534002669
- type: nauc_precision_at_5_max
value: 40.83262650073459
- type: nauc_precision_at_5_std
value: 26.158537031161494
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: .nan
- type: nauc_recall_at_100_max
value: .nan
- type: nauc_recall_at_100_std
value: .nan
- type: nauc_recall_at_10_diff1
value: 53.08654386169444
- type: nauc_recall_at_10_max
value: -23.276269379519356
- type: nauc_recall_at_10_std
value: -50.80707792706157
- type: nauc_recall_at_1_diff1
value: 74.96782567287174
- type: nauc_recall_at_1_max
value: -29.648279252607875
- type: nauc_recall_at_1_std
value: -54.017459339141595
- type: nauc_recall_at_20_diff1
value: 51.60121897059633
- type: nauc_recall_at_20_max
value: -14.241779530735387
- type: nauc_recall_at_20_std
value: -37.877451525215456
- type: nauc_recall_at_3_diff1
value: 66.99474984329694
- type: nauc_recall_at_3_max
value: -30.802787353187966
- type: nauc_recall_at_3_std
value: -53.58737792129713
- type: nauc_recall_at_5_diff1
value: 54.64214444958567
- type: nauc_recall_at_5_max
value: -23.341309362104703
- type: nauc_recall_at_5_std
value: -51.381363923145265
- type: ndcg_at_1
value: 76.048
- type: ndcg_at_10
value: 69.83
- type: ndcg_at_100
value: 82.11500000000001
- type: ndcg_at_1000
value: 82.11500000000001
- type: ndcg_at_20
value: 75.995
- type: ndcg_at_3
value: 69.587
- type: ndcg_at_5
value: 69.062
- type: precision_at_1
value: 76.048
- type: precision_at_10
value: 43.653
- type: precision_at_100
value: 7.718999999999999
- type: precision_at_1000
value: 0.772
- type: precision_at_20
value: 31.108000000000004
- type: precision_at_3
value: 63.87199999999999
- type: precision_at_5
value: 56.407
- type: recall_at_1
value: 15.736
- type: recall_at_10
value: 66.873
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 85.01100000000001
- type: recall_at_3
value: 36.441
- type: recall_at_5
value: 49.109
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cosine_accuracy
value: 99.87326732673267
- type: cosine_accuracy_threshold
value: 86.0752820968628
- type: cosine_ap
value: 96.98758090713252
- type: cosine_f1
value: 93.52881698685542
- type: cosine_f1_threshold
value: 86.0752820968628
- type: cosine_precision
value: 94.58077709611452
- type: cosine_recall
value: 92.5
- type: dot_accuracy
value: 99.82574257425742
- type: dot_accuracy_threshold
value: 40484.73815917969
- type: dot_ap
value: 95.68959907254845
- type: dot_f1
value: 91.31293188548865
- type: dot_f1_threshold
value: 40336.810302734375
- type: dot_precision
value: 90.15594541910332
- type: dot_recall
value: 92.5
- type: euclidean_accuracy
value: 99.87128712871286
- type: euclidean_accuracy_threshold
value: 1162.5749588012695
- type: euclidean_ap
value: 96.92640435656577
- type: euclidean_f1
value: 93.4475806451613
- type: euclidean_f1_threshold
value: 1162.5749588012695
- type: euclidean_precision
value: 94.20731707317073
- type: euclidean_recall
value: 92.7
- type: main_score
value: 96.98758090713252
- type: manhattan_accuracy
value: 99.86930693069307
- type: manhattan_accuracy_threshold
value: 28348.71826171875
- type: manhattan_ap
value: 96.93832673967925
- type: manhattan_f1
value: 93.33333333333333
- type: manhattan_f1_threshold
value: 28348.71826171875
- type: manhattan_precision
value: 94.28571428571428
- type: manhattan_recall
value: 92.4
- type: max_accuracy
value: 99.87326732673267
- type: max_ap
value: 96.98758090713252
- type: max_f1
value: 93.52881698685542
- type: max_precision
value: 94.58077709611452
- type: max_recall
value: 92.7
- type: similarity_accuracy
value: 99.87326732673267
- type: similarity_accuracy_threshold
value: 86.0752820968628
- type: similarity_ap
value: 96.98758090713252
- type: similarity_f1
value: 93.52881698685542
- type: similarity_f1_threshold
value: 86.0752820968628
- type: similarity_precision
value: 94.58077709611452
- type: similarity_recall
value: 92.5
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: main_score
value: 65.6560129719848
- type: v_measure
value: 65.6560129719848
- type: v_measure_std
value: 4.781229811487539
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: main_score
value: 35.07546243853692
- type: v_measure
value: 35.07546243853692
- type: v_measure_std
value: 1.1978740356240998
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 51.771005199508835
- type: mrr
value: 52.65443298531534
- type: main_score
value: 51.771005199508835
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 29.48686238342228
- type: cosine_spearman
value: 29.706543509170054
- type: dot_pearson
value: 27.95853155597859
- type: dot_spearman
value: 27.604287986935162
- type: main_score
value: 29.706543509170054
- type: pearson
value: 29.48686238342228
- type: spearman
value: 29.706543509170054
task:
type: Summarization
- dataset:
config: default
name: MTEB SummEvalFr (default)
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cosine_pearson
value: 31.551301434917868
- type: cosine_spearman
value: 30.709049789175186
- type: dot_pearson
value: 27.77050901756549
- type: dot_spearman
value: 26.715505953561795
- type: main_score
value: 30.709049789175186
- type: pearson
value: 31.551301434917868
- type: spearman
value: 30.709049789175186
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking (default)
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
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task:
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config: default
name: MTEB SyntecRetrieval (default)
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
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task:
type: Retrieval
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config: default
name: MTEB T2Reranking (default)
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
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task:
type: Reranking
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config: default
name: MTEB T2Retrieval (default)
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
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task:
type: Retrieval
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config: default
name: MTEB TERRa (default)
revision: 7b58f24536063837d644aab9a023c62199b2a612
split: dev
type: ai-forever/terra-pairclassification
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- type: main_score
value: 58.8479126365766
- type: manhattan_accuracy
value: 59.934853420195445
- type: manhattan_accuracy_threshold
value: 29897.271728515625
- type: manhattan_ap
value: 58.8479126365766
- type: manhattan_f1
value: 66.81318681318683
- type: manhattan_f1_threshold
value: 46291.802978515625
- type: manhattan_precision
value: 50.331125827814574
- type: manhattan_recall
value: 99.34640522875817
- type: max_accuracy
value: 60.586319218241044
- type: max_ap
value: 58.8479126365766
- type: max_f1
value: 67.37967914438502
- type: max_precision
value: 57.01357466063348
- type: max_recall
value: 99.34640522875817
- type: similarity_accuracy
value: 60.586319218241044
- type: similarity_accuracy_threshold
value: 82.49806761741638
- type: similarity_ap
value: 58.73198048427448
- type: similarity_f1
value: 67.37967914438502
- type: similarity_f1_threshold
value: 77.46461033821106
- type: similarity_precision
value: 57.01357466063348
- type: similarity_recall
value: 82.35294117647058
task:
type: PairClassification
- dataset:
config: default
name: MTEB TNews (default)
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 45.967999999999996
- type: f1
value: 44.699306100915706
- type: f1_weighted
value: 46.03730319014832
- type: main_score
value: 45.967999999999996
task:
type: Classification
- dataset:
config: default
name: MTEB TRECCOVID (default)
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
metrics:
- type: map_at_1
value: 0.251
- type: map_at_10
value: 1.9480000000000002
- type: map_at_100
value: 11.082
- type: map_at_1000
value: 26.700000000000003
- type: map_at_20
value: 3.3529999999999998
- type: map_at_3
value: 0.679
- type: map_at_5
value: 1.079
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 95.786
- type: mrr_at_100
value: 95.786
- type: mrr_at_1000
value: 95.786
- type: mrr_at_20
value: 95.786
- type: mrr_at_3
value: 95.0
- type: mrr_at_5
value: 95.5
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 77.71900000000001
- type: ndcg_at_100
value: 57.726
- type: ndcg_at_1000
value: 52.737
- type: ndcg_at_20
value: 72.54
- type: ndcg_at_3
value: 83.397
- type: ndcg_at_5
value: 80.806
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 81.0
- type: precision_at_100
value: 59.199999999999996
- type: precision_at_1000
value: 23.244
- type: precision_at_20
value: 75.2
- type: precision_at_3
value: 88.0
- type: precision_at_5
value: 84.8
- type: recall_at_1
value: 0.251
- type: recall_at_10
value: 2.1229999999999998
- type: recall_at_100
value: 14.496999999999998
- type: recall_at_1000
value: 50.09
- type: recall_at_20
value: 3.8309999999999995
- type: recall_at_3
value: 0.696
- type: recall_at_5
value: 1.1400000000000001
- type: main_score
value: 77.71900000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB TenKGnadClusteringP2P (default)
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
split: test
type: slvnwhrl/tenkgnad-clustering-p2p
metrics:
- type: main_score
value: 43.763609722295215
- type: v_measure
value: 43.763609722295215
- type: v_measure_std
value: 2.8751199473862457
task:
type: Clustering
- dataset:
config: default
name: MTEB TenKGnadClusteringS2S (default)
revision: 6cddbe003f12b9b140aec477b583ac4191f01786
split: test
type: slvnwhrl/tenkgnad-clustering-s2s
metrics:
- type: main_score
value: 39.762424448504355
- type: v_measure
value: 39.762424448504355
- type: v_measure_std
value: 3.30146124979502
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P (default)
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: main_score
value: 63.133819258289456
- type: v_measure
value: 63.133819258289456
- type: v_measure_std
value: 1.8854253356479695
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S (default)
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: main_score
value: 58.98195851785808
- type: v_measure
value: 58.98195851785808
- type: v_measure_std
value: 1.6237600076393737
task:
type: Clustering
- dataset:
config: default
name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: map_at_1
value: 3.3550000000000004
- type: map_at_10
value: 10.08
- type: map_at_100
value: 16.136
- type: map_at_1000
value: 17.605
- type: map_at_20
value: 12.561
- type: map_at_3
value: 5.641
- type: map_at_5
value: 7.3260000000000005
- type: mrr_at_1
value: 46.939
- type: mrr_at_10
value: 58.152
- type: mrr_at_100
value: 58.594
- type: mrr_at_1000
value: 58.601000000000006
- type: mrr_at_20
value: 58.279
- type: mrr_at_3
value: 55.102
- type: mrr_at_5
value: 56.531
- type: ndcg_at_1
value: 44.897999999999996
- type: ndcg_at_10
value: 26.298
- type: ndcg_at_100
value: 37.596000000000004
- type: ndcg_at_1000
value: 49.424
- type: ndcg_at_20
value: 27.066000000000003
- type: ndcg_at_3
value: 31.528
- type: ndcg_at_5
value: 28.219
- type: precision_at_1
value: 46.939
- type: precision_at_10
value: 22.245
- type: precision_at_100
value: 7.531000000000001
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_20
value: 17.041
- type: precision_at_3
value: 30.612000000000002
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 3.3550000000000004
- type: recall_at_10
value: 16.41
- type: recall_at_100
value: 47.272
- type: recall_at_1000
value: 83.584
- type: recall_at_20
value: 24.091
- type: recall_at_3
value: 6.8180000000000005
- type: recall_at_5
value: 9.677
- type: main_score
value: 26.298
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 91.2890625
- type: ap
value: 33.95547153875715
- type: ap_weighted
value: 33.95547153875715
- type: f1
value: 75.10768597556462
- type: f1_weighted
value: 92.00161208992606
- type: main_score
value: 91.2890625
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 71.3978494623656
- type: f1
value: 71.7194818511814
- type: f1_weighted
value: 71.13860187349744
- type: main_score
value: 71.3978494623656
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: main_score
value: 52.4921688720602
- type: v_measure
value: 52.4921688720602
- type: v_measure_std
value: 0.992768152658908
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cosine_accuracy
value: 85.11652858079513
- type: cosine_accuracy_threshold
value: 87.90839910507202
- type: cosine_ap
value: 70.90459908851724
- type: cosine_f1
value: 65.66581227877457
- type: cosine_f1_threshold
value: 85.13308763504028
- type: cosine_precision
value: 61.094708153531684
- type: cosine_recall
value: 70.97625329815304
- type: dot_accuracy
value: 83.41181379269239
- type: dot_accuracy_threshold
value: 43110.113525390625
- type: dot_ap
value: 65.64869491143095
- type: dot_f1
value: 62.05308447460914
- type: dot_f1_threshold
value: 41412.542724609375
- type: dot_precision
value: 57.38623626989464
- type: dot_recall
value: 67.54617414248021
- type: euclidean_accuracy
value: 85.15229182809799
- type: euclidean_accuracy_threshold
value: 1043.08500289917
- type: euclidean_ap
value: 70.71204383269375
- type: euclidean_f1
value: 65.20304568527919
- type: euclidean_f1_threshold
value: 1179.2595863342285
- type: euclidean_precision
value: 62.81173594132029
- type: euclidean_recall
value: 67.78364116094987
- type: main_score
value: 70.90459908851724
- type: manhattan_accuracy
value: 85.1820945341837
- type: manhattan_accuracy_threshold
value: 26115.0390625
- type: manhattan_ap
value: 70.66113937117431
- type: manhattan_f1
value: 65.33383628819313
- type: manhattan_f1_threshold
value: 29105.181884765625
- type: manhattan_precision
value: 62.40691808791736
- type: manhattan_recall
value: 68.54881266490766
- type: max_accuracy
value: 85.1820945341837
- type: max_ap
value: 70.90459908851724
- type: max_f1
value: 65.66581227877457
- type: max_precision
value: 62.81173594132029
- type: max_recall
value: 70.97625329815304
- type: similarity_accuracy
value: 85.11652858079513
- type: similarity_accuracy_threshold
value: 87.90839910507202
- type: similarity_ap
value: 70.90459908851724
- type: similarity_f1
value: 65.66581227877457
- type: similarity_f1_threshold
value: 85.13308763504028
- type: similarity_precision
value: 61.094708153531684
- type: similarity_recall
value: 70.97625329815304
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cosine_accuracy
value: 88.10299996119068
- type: cosine_accuracy_threshold
value: 84.34982895851135
- type: cosine_ap
value: 84.13755787769226
- type: cosine_f1
value: 76.0967548076923
- type: cosine_f1_threshold
value: 82.8936219215393
- type: cosine_precision
value: 74.28864769727193
- type: cosine_recall
value: 77.99507237449954
- type: dot_accuracy
value: 86.64182869561843
- type: dot_accuracy_threshold
value: 38794.677734375
- type: dot_ap
value: 80.20301567411457
- type: dot_f1
value: 73.50650291634967
- type: dot_f1_threshold
value: 37447.23205566406
- type: dot_precision
value: 69.41498460485802
- type: dot_recall
value: 78.11056359716662
- type: euclidean_accuracy
value: 87.9361198432103
- type: euclidean_accuracy_threshold
value: 1184.421157836914
- type: euclidean_ap
value: 83.79582690117218
- type: euclidean_f1
value: 75.81431709042175
- type: euclidean_f1_threshold
value: 1258.2727432250977
- type: euclidean_precision
value: 73.39099099099099
- type: euclidean_recall
value: 78.40314136125654
- type: main_score
value: 84.13755787769226
- type: manhattan_accuracy
value: 87.96134590755618
- type: manhattan_accuracy_threshold
value: 29077.291870117188
- type: manhattan_ap
value: 83.79487172269923
- type: manhattan_f1
value: 75.82421603424935
- type: manhattan_f1_threshold
value: 31224.124145507812
- type: manhattan_precision
value: 72.24740255212329
- type: manhattan_recall
value: 79.77363720357253
- type: max_accuracy
value: 88.10299996119068
- type: max_ap
value: 84.13755787769226
- type: max_f1
value: 76.0967548076923
- type: max_precision
value: 74.28864769727193
- type: max_recall
value: 79.77363720357253
- type: similarity_accuracy
value: 88.10299996119068
- type: similarity_accuracy_threshold
value: 84.34982895851135
- type: similarity_ap
value: 84.13755787769226
- type: similarity_f1
value: 76.0967548076923
- type: similarity_f1_threshold
value: 82.8936219215393
- type: similarity_precision
value: 74.28864769727193
- type: similarity_recall
value: 77.99507237449954
task:
type: PairClassification
- dataset:
config: default
name: MTEB VideoRetrieval (default)
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: main_score
value: 70.433
- type: map_at_1
value: 55.7
- type: map_at_10
value: 66.013
- type: map_at_100
value: 66.534
- type: map_at_1000
value: 66.547
- type: map_at_20
value: 66.334
- type: map_at_3
value: 64.2
- type: map_at_5
value: 65.445
- type: mrr_at_1
value: 55.7
- type: mrr_at_10
value: 66.01329365079364
- type: mrr_at_100
value: 66.53350061744233
- type: mrr_at_1000
value: 66.54744831962995
- type: mrr_at_20
value: 66.3335147364675
- type: mrr_at_3
value: 64.2
- type: mrr_at_5
value: 65.44500000000002
- type: nauc_map_at_1000_diff1
value: 76.26428836976245
- type: nauc_map_at_1000_max
value: 35.41847367373575
- type: nauc_map_at_1000_std
value: -33.04639860831992
- type: nauc_map_at_100_diff1
value: 76.25793229023193
- type: nauc_map_at_100_max
value: 35.43663260110076
- type: nauc_map_at_100_std
value: -33.04238139882945
- type: nauc_map_at_10_diff1
value: 76.2108281297711
- type: nauc_map_at_10_max
value: 35.59442419423183
- type: nauc_map_at_10_std
value: -33.32346518997277
- type: nauc_map_at_1_diff1
value: 79.17728405262736
- type: nauc_map_at_1_max
value: 31.880738163589527
- type: nauc_map_at_1_std
value: -30.891888718004584
- type: nauc_map_at_20_diff1
value: 76.2181333410193
- type: nauc_map_at_20_max
value: 35.43448818430876
- type: nauc_map_at_20_std
value: -33.35682442863193
- type: nauc_map_at_3_diff1
value: 76.10046541433466
- type: nauc_map_at_3_max
value: 34.6831278555291
- type: nauc_map_at_3_std
value: -34.030826044831116
- type: nauc_map_at_5_diff1
value: 75.96513023582064
- type: nauc_map_at_5_max
value: 34.66920832438069
- type: nauc_map_at_5_std
value: -33.79799777830796
- type: nauc_mrr_at_1000_diff1
value: 76.26428836976245
- type: nauc_mrr_at_1000_max
value: 35.41847367373575
- type: nauc_mrr_at_1000_std
value: -33.04639860831992
- type: nauc_mrr_at_100_diff1
value: 76.25793229023193
- type: nauc_mrr_at_100_max
value: 35.43663260110076
- type: nauc_mrr_at_100_std
value: -33.04238139882945
- type: nauc_mrr_at_10_diff1
value: 76.2108281297711
- type: nauc_mrr_at_10_max
value: 35.59442419423183
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value: -33.32346518997277
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value: 79.17728405262736
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value: -30.891888718004584
- type: nauc_mrr_at_20_diff1
value: 76.2181333410193
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value: 35.43448818430876
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value: -33.35682442863193
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value: 76.10046541433466
- type: nauc_mrr_at_3_max
value: 34.6831278555291
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value: -34.030826044831116
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value: 75.96513023582064
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value: 34.66920832438069
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value: -33.79799777830796
- type: nauc_ndcg_at_1000_diff1
value: 75.68118206798317
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value: 37.12252980787349
- type: nauc_ndcg_at_1000_std
value: -31.457578337430505
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value: 75.46730761564156
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value: 37.549890025544265
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value: -31.35066985945112
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value: 75.09890404887037
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value: 38.024147790014204
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value: -33.67408368593356
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value: 79.17728405262736
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value: 31.880738163589527
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value: -30.891888718004584
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value: 75.12977548171354
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value: 37.524926748917956
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value: -33.771344674947485
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value: 74.94037476984154
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value: 35.60345554050552
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value: -35.256991346321854
- type: nauc_ndcg_at_5_diff1
value: 74.54265907753783
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value: 35.57662819978585
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value: -34.879794448418465
- type: nauc_precision_at_1000_diff1
value: 74.52277207179142
- type: nauc_precision_at_1000_max
value: 94.25510945118707
- type: nauc_precision_at_1000_std
value: 91.6874157070222
- type: nauc_precision_at_100_diff1
value: 65.98346655735419
- type: nauc_precision_at_100_max
value: 78.81168727653687
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value: 27.241465691967708
- type: nauc_precision_at_10_diff1
value: 69.55050319096688
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value: 51.827749140893374
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value: -34.60818605792837
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value: 79.17728405262736
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value: -30.891888718004584
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value: 68.08078305042736
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value: 52.83318878288501
- type: nauc_precision_at_20_std
value: -35.46070292817927
- type: nauc_precision_at_3_diff1
value: 70.76249609881901
- type: nauc_precision_at_3_max
value: 38.86561868624655
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value: -39.68917853446992
- type: nauc_precision_at_5_diff1
value: 68.39110629013278
- type: nauc_precision_at_5_max
value: 39.28677163904683
- type: nauc_precision_at_5_std
value: -39.39101423819562
- type: nauc_recall_at_1000_diff1
value: 74.52277207179175
- type: nauc_recall_at_1000_max
value: 94.25510945118776
- type: nauc_recall_at_1000_std
value: 91.68741570702382
- type: nauc_recall_at_100_diff1
value: 65.9834665573548
- type: nauc_recall_at_100_max
value: 78.81168727653679
- type: nauc_recall_at_100_std
value: 27.241465691967598
- type: nauc_recall_at_10_diff1
value: 69.55050319096708
- type: nauc_recall_at_10_max
value: 51.82774914089347
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value: 74.22999999999999
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value: 71.829
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value: 17.717
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value: 2.031
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value: 0.207
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value: 9.399000000000001
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value: 44.458999999999996
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value: 31.535000000000004
- type: recall_at_1
value: 46.444
- type: recall_at_10
value: 86.275
- type: recall_at_100
value: 98.017
- type: recall_at_1000
value: 99.8
- type: recall_at_20
value: 90.935
- type: recall_at_3
value: 70.167
- type: recall_at_5
value: 78.2
task:
type: Retrieval
---
<br><br>
<p align="center">
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" 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 embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="center">
<b>jina-embeddings-v3: Multilingual Embeddings With Task LoRA</b>
</p>
## Quick Start
[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings)
## Intended Usage & Model Info
`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.
Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation),
this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**.
Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.
### Key Features:
- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options:
- `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks
- `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks
- `separation`: Used for embeddings in clustering and re-ranking applications
- `classification`: Used for embeddings in classification tasks
- `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.
### Supported Languages:
While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages:
**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek,
Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian,
Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
## Usage
**<details><summary>Apply mean pooling when integrating the model.</summary>**
<p>
### Why Use Mean Pooling?
Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level.
This approach has been shown to produce high-quality sentence embeddings.
We provide an `encode` function that handles this for you automatically.
However, if you're working with the model directly, outside of the `encode` function,
you'll need to apply mean pooling manually. Here's how you can do it:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ["How is the weather today?", "What is the current weather like today?"]
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
task = 'retrieval.query'
task_id = model._adaptation_map[task]
adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32)
with torch.no_grad():
model_output = model(**encoded_input, adapter_mask=adapter_mask)
embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/).
Alternatively, you can use `jina-embeddings-v3` directly via Transformers package:
```bash
!pip install transformers torch einops
!pip install 'numpy<2'
```
If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100),
```bash
!pip install flash-attn --no-build-isolation
```
```python
from transformers import AutoModel
# Initialize the model
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
texts = [
"Follow the white rabbit.", # English
"Sigue al conejo blanco.", # Spanish
"Suis le lapin blanc.", # French
"跟着白兔走。", # Chinese
"اتبع الأرنب الأبيض.", # Arabic
"Folge dem weißen Kaninchen.", # German
]
# When calling the `encode` function, you can choose a `task` based on the use case:
# 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching'
# Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used.
embeddings = model.encode(texts, task="text-matching")
# Compute similarities
print(embeddings[0] @ embeddings[1].T)
```
By default, the model supports a maximum sequence length of 8192 tokens.
However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(["Very long ... document"], max_length=2048)
```
In case you want to use **Matryoshka embeddings** and switch to a different dimension,
you can adjust it by passing the `truncate_dim` parameter to the `encode` function:
```python
embeddings = model.encode(['Sample text'], truncate_dim=256)
```
The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`:
```bash
!pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
task = "retrieval.query"
embeddings = model.encode(
["What is the weather like in Berlin today?"],
task=task,
prompt_name=task,
)
```
You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html).
To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'})
```
Or afterwards:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
model[0].default_task = 'classification'
```
This way you can fine-tune the LoRA adapter for the chosen task.
However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True})
```
This will allow fine-tuning the whole model instead of just the LoRA adapters.
**<details><summary>ONNX Inference.</summary>**
<p>
You can use ONNX for efficient inference with `jina-embeddings-v3`:
```python
import onnxruntime
import numpy as np
from transformers import AutoTokenizer, PretrainedConfig
# Load tokenizer and model config
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')
# Tokenize input
input_text = tokenizer('sample text', return_tensors='np')
# ONNX session
model_path = 'jina-embeddings-v3/onnx/model.onnx'
session = onnxruntime.InferenceSession(model_path)
# Prepare inputs for ONNX model
task_type = 'text-matching'
task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
inputs = {
'input_ids': input_text['input_ids'],
'attention_mask': input_text['attention_mask'],
'task_id': task_id
}
# Run model
outputs = session.run(None, inputs)
```
</p>
</details>
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## License
`jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/).
## Citation
If you find `jina-embeddings-v3` useful in your research, please cite the following paper:
```bibtex
@misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA},
author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao},
year={2024},
eprint={2409.10173},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.10173},
}
```
|
dfurman/CalmeRys-78B-Orpo-v0.1 | dfurman | 2024-10-20T04:19:26Z | 6,191 | 68 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"orpo",
"sft",
"chatml",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"base_model:MaziyarPanahi/calme-2.4-rys-78b",
"base_model:finetune:MaziyarPanahi/calme-2.4-rys-78b",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-09-24T10:25:46Z | ---
language:
- en
license: mit
library_name: transformers
tags:
- orpo
- qwen2
- sft
- chatml
base_model:
- MaziyarPanahi/calme-2.4-rys-78b
datasets:
- mlabonne/orpo-dpo-mix-40k
pipeline_tag: text-generation
inference: false
model_creator: dfurman
quantized_by: dfurman
model-index:
- name: CalmeRys-78B-Orpo-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 81.63
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 61.92
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 37.92
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 20.02
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 36.37
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.8
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/CalmeRys-78B-Orpo-v0.1
name: Open LLM Leaderboard
---
# dfurman/CalmeRys-78B-Orpo-v0.1
This model is a finetune of `MaziyarPanahi/calme-2.4-rys-78b` on 1.5k rows of the `mlabonne/orpo-dpo-mix-40k` dataset. It was trained as a generalist language model for a variety of text generation use cases, including support of agentic capabilities, roleplaying, reasoning, multi-turn conversations, long context coherence, and more.
As of Oct 2024, this is the top ranking model on the [Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) 🏆.
Thanks go out to [mlabonne](https://huggingface.co/mlabonne), [MaziyarPanahi](https://huggingface.com/MaziyarPanahi), et al. for the source dataset and base model.
## 🦾 Training
You can find the experiment on W&B at this [link](https://wandb.ai/dryanfurman/huggingface/runs/1w50nu70?nw=nwuserdryanfurman). Here are a few visualizations:
## 💻 Usage
<details>
<summary>Setup</summary>
```python
!pip install -qU transformers accelerate bitsandbytes
!huggingface-cli download dfurman/CalmeRys-78B-Orpo-v0.1
```
```python
from transformers import AutoTokenizer, BitsAndBytesConfig
import transformers
import torch
if torch.cuda.get_device_capability()[0] >= 8:
!pip install -qqq flash-attn
attn_implementation = "flash_attention_2"
torch_dtype = torch.bfloat16
else:
attn_implementation = "eager"
torch_dtype = torch.float16
# # quantize if necessary
# bnb_config = BitsAndBytesConfig(
# load_in_4bit=True,
# bnb_4bit_quant_type="nf4",
# bnb_4bit_compute_dtype=torch_dtype,
# bnb_4bit_use_double_quant=True,
# )
model = "dfurman/CalmeRys-78B-Orpo-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={
"torch_dtype": torch_dtype,
# "quantization_config": bnb_config,
"device_map": "auto",
"attn_implementation": attn_implementation,
}
)
```
</details>
### Example 1
```python
question = "Is the number 9.11 larger than 9.9?"
messages = [
{"role": "system", "content": "You are a helpful assistant that thinks step by step."},
{"role": "user", "content": question},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# print("***Prompt:\n", prompt)
outputs = pipeline(
prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95
)
print("***Generation:")
print(outputs[0]["generated_text"][len(prompt) :])
```
```
***Generation:
To compare these two numbers, it's important to look at their decimal places after the whole number part, which is 9 in both cases. Comparing the tenths place, 9.11 has a '1' and 9.9 has a '9'. Since '9' is greater than '1', 9.9 is larger than 9.11.
```
### Example 2
```python
question = """The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning.
They sold 93 loaves in the morning and 39 loaves in the afternoon.
A grocery store then returned 6 unsold loaves back to the bakery.
How many loaves of bread did the bakery have left?
Respond as succinctly as possible. Format the response as a completion of this table:
|step|subquestion|procedure|result|
|:---|:----------|:--------|:-----:|"""
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": question},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# print("***Prompt:\n", prompt)
outputs = pipeline(prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print("***Generation:")
print(outputs[0]["generated_text"][len(prompt):])
```
```
***Generation:
|1|Calculate total sold|Add morning and afternoon sales|132|
|2|Subtract sold from total|200 - 132|68|
|3|Adjust for returns|Add returned loaves to remaining|74|
```
### Example 3
```python
question = "What's a good recipe for a spicy margarita?"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": question},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# print("***Prompt:\n", prompt)
outputs = pipeline(prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print("***Generation:")
print(outputs[0]["generated_text"][len(prompt):])
```
```
***Generation:
To make a Spicy Margarita, you'll need to incorporate a chili or pepper element into your classic margarita recipe. Here’s a simple way to do it:
### Ingredients:
- 2 oz tequila (blanco or reposado)
- 1 oz fresh lime juice
- 1/2 oz triple sec (Cointreau or Grand Marnier)
- 1/2 oz agave syrup or simple syrup
- 1-2 slices of jalapeño (or more depending on how spicy you like it)
- Salt and/or chili powder for rimming the glass
- Ice
- Lime wheel for garnish
### Instructions:
1. **Muddle Jalapeño**: In a shaker, muddle the jalapeño slices slightly. This will release the oils and heat from the peppers.
2. **Add Remaining Ingredients**: Add the tequila, lime juice, triple sec, and agave syrup or simple syrup.
3. **Shake and Strain**: Fill the shaker with ice and shake vigorously until cold. Strain into a salt and/or chili powder rimmed glass filled with ice.
4. **Garnish and Serve**: Garnish with a lime wheel and enjoy.
If you prefer a smoother spiciness that doesn't overpower the drink, you could also consider making a jalapeño-infused tequila by leaving the jalapeño slices in the bottle of tequila for several hours to a couple of days, adjusting the time based on desired level of spiciness. Then use this infused tequila instead of regular tequila in the recipe above.
Another variation is to use a spicy syrup. To make this, combine equal parts water and sugar with a few sliced jalapeños in a saucepan. Bring to a boil, stirring occasionally to dissolve the sugar. Reduce heat and simmer for about 5 minutes. Let cool, strain out the jalapeños, then store in a sealed container in the refrigerator until ready to use. Use this spicy syrup instead of regular syrup in the recipe.
As always, adjust the quantity of jalapeño or the type of chili used to suit your taste. Enjoy responsibly!
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__CalmeRys-78B-Orpo-v0.1)
| Metric |Value|
|-------------------|----:|
|Avg. |50.78|
|IFEval (0-Shot) |81.63|
|BBH (3-Shot) |61.92|
|MATH Lvl 5 (4-Shot)|37.92|
|GPQA (0-shot) |20.02|
|MuSR (0-shot) |36.37|
|MMLU-PRO (5-shot) |66.80|
|
Gummybear05/wav2vec2-E10_freq | Gummybear05 | 2024-10-20T03:59:21Z | 14 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-20T02:26:04Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-E10_freq
results: []
---
<!-- 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. -->
# wav2vec2-E10_freq
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2533
- Cer: 25.5169
## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 36.7712 | 0.1289 | 200 | 4.9631 | 100.0 |
| 5.0134 | 0.2579 | 400 | 4.6670 | 100.0 |
| 4.8451 | 0.3868 | 600 | 4.6504 | 100.0 |
| 4.8129 | 0.5158 | 800 | 4.6193 | 100.0 |
| 4.769 | 0.6447 | 1000 | 4.6958 | 100.0 |
| 4.7464 | 0.7737 | 1200 | 4.6290 | 100.0 |
| 4.7596 | 0.9026 | 1400 | 4.5727 | 100.0 |
| 4.6833 | 1.0316 | 1600 | 4.5497 | 100.0 |
| 4.5065 | 1.1605 | 1800 | 4.3251 | 100.0 |
| 3.8519 | 1.2895 | 2000 | 3.2409 | 59.3221 |
| 2.9075 | 1.4184 | 2200 | 2.6012 | 47.5388 |
| 2.4945 | 1.5474 | 2400 | 2.2988 | 43.0804 |
| 2.211 | 1.6763 | 2600 | 2.0536 | 38.1990 |
| 1.9965 | 1.8053 | 2800 | 1.8464 | 34.2575 |
| 1.8016 | 1.9342 | 3000 | 1.7530 | 33.5585 |
| 1.6726 | 2.0632 | 3200 | 1.6394 | 31.5966 |
| 1.5525 | 2.1921 | 3400 | 1.4910 | 28.8828 |
| 1.4379 | 2.3211 | 3600 | 1.4146 | 28.3012 |
| 1.3769 | 2.4500 | 3800 | 1.3507 | 27.0207 |
| 1.3162 | 2.5790 | 4000 | 1.3658 | 27.2498 |
| 1.2929 | 2.7079 | 4200 | 1.2914 | 26.2042 |
| 1.2405 | 2.8369 | 4400 | 1.2875 | 26.1396 |
| 1.2097 | 2.9658 | 4600 | 1.2533 | 25.5169 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
KarthickthasanS/Food_ecommendation | KarthickthasanS | 2024-10-20T03:46:01Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-20T03:43:45Z | ---
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]
- **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] |
QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF | QuantFactory | 2024-10-20T03:40:31Z | 170 | 2 | null | [
"gguf",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T17:58:35Z |
---
license: apache-2.0
model-index:
- name: SOLAR-10.7B-Instruct-v1.0-uncensored
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 38.84
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 33.86
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 0.23
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.93
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 18.49
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 26.04
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard
---
[](https://hf.co/QuantFactory)
# QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF
This is quantized version of [w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored](https://huggingface.co/w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored) created using llama.cpp
# Original Model Card
# SOLAR-10.7B-Instruct-v1.0-uncensored
SOLAR-10.7B-Instruct-v1.0 finetuned to be less censored. Refer to [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) for model info and usage instructions.
## Training details
This model was trained using Lora and DPOTrainer on [unalignment/toxic-dpo-v0.1](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
## How to Cite
```
@misc{solarUncensoredDPO,
title={solar-10.7b-instruct-V1.0-uncensored},
url={https://huggingface.co/w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored},
author={Stepan Zuev},
year={2023},
month={Dec}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_w4r10ck__SOLAR-10.7B-Instruct-v1.0-uncensored)
| Metric |Value|
|-------------------|----:|
|Avg. |20.56|
|IFEval (0-Shot) |38.84|
|BBH (3-Shot) |33.86|
|MATH Lvl 5 (4-Shot)| 0.23|
|GPQA (0-shot) | 5.93|
|MuSR (0-shot) |18.49|
|MMLU-PRO (5-shot) |26.04|
|
TomLong/distilbert-base-uncased-finetuned-emotion | TomLong | 2024-10-20T03:18:00Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-20T03:02:46Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2199
- Accuracy: 0.925
- F1: 0.9250
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8207 | 1.0 | 250 | 0.3126 | 0.9105 | 0.9096 |
| 0.2436 | 2.0 | 500 | 0.2199 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
NLPmonster/layoutlmv3-for-complete-receipt-understanding | NLPmonster | 2024-10-20T03:14:06Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:NLPmonster/layoutlmv3-for-receipt-understanding",
"base_model:finetune:NLPmonster/layoutlmv3-for-receipt-understanding",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-06T03:16:16Z | ---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: NLPmonster/layoutlmv3-for-receipt-understanding
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-for-complete-receipt-understanding
results: []
---
<!-- 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. -->
# layoutlmv3-for-complete-receipt-understanding
This model is a fine-tuned version of [NLPmonster/layoutlmv3-for-receipt-understanding](https://huggingface.co/NLPmonster/layoutlmv3-for-receipt-understanding) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4421
- Precision: 0.6502
- Recall: 0.6607
- F1: 0.6554
- Accuracy: 0.8760
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2604 | 0.4673 | 50 | 0.6573 | 0.5077 | 0.4747 | 0.4906 | 0.7863 |
| 0.5256 | 0.9346 | 100 | 0.4824 | 0.5732 | 0.6521 | 0.6101 | 0.8465 |
| 0.4529 | 1.4019 | 150 | 0.4461 | 0.5926 | 0.6509 | 0.6204 | 0.8464 |
| 0.3767 | 1.8692 | 200 | 0.4265 | 0.5747 | 0.6740 | 0.6204 | 0.8492 |
| 0.3403 | 2.3364 | 250 | 0.4557 | 0.5564 | 0.6083 | 0.5812 | 0.8451 |
| 0.331 | 2.8037 | 300 | 0.4065 | 0.6384 | 0.6671 | 0.6524 | 0.8669 |
| 0.2984 | 3.2710 | 350 | 0.3820 | 0.6411 | 0.6411 | 0.6411 | 0.8729 |
| 0.2763 | 3.7383 | 400 | 0.4078 | 0.6104 | 0.6037 | 0.6070 | 0.8576 |
| 0.2626 | 4.2056 | 450 | 0.4203 | 0.6268 | 0.6164 | 0.6216 | 0.8589 |
| 0.2456 | 4.6729 | 500 | 0.3960 | 0.6240 | 0.6406 | 0.6322 | 0.8686 |
| 0.2078 | 5.1402 | 550 | 0.4074 | 0.6401 | 0.6290 | 0.6345 | 0.8709 |
| 0.1859 | 5.6075 | 600 | 0.3853 | 0.6511 | 0.6601 | 0.6556 | 0.8733 |
| 0.2059 | 6.0748 | 650 | 0.3845 | 0.6539 | 0.6509 | 0.6524 | 0.8772 |
| 0.1649 | 6.5421 | 700 | 0.4128 | 0.6298 | 0.6486 | 0.6390 | 0.8706 |
| 0.1599 | 7.0093 | 750 | 0.4328 | 0.6302 | 0.6578 | 0.6437 | 0.8644 |
| 0.1437 | 7.4766 | 800 | 0.4100 | 0.6510 | 0.6469 | 0.6489 | 0.8727 |
| 0.1377 | 7.9439 | 850 | 0.4409 | 0.6317 | 0.6699 | 0.6503 | 0.8711 |
| 0.1164 | 8.4112 | 900 | 0.4331 | 0.6301 | 0.6642 | 0.6467 | 0.8717 |
| 0.1149 | 8.8785 | 950 | 0.4523 | 0.6466 | 0.6555 | 0.6510 | 0.8712 |
| 0.1096 | 9.3458 | 1000 | 0.4421 | 0.6502 | 0.6607 | 0.6554 | 0.8760 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Nutanix/llama-30b_checkpoint-6500_20241020-024234-merged | Nutanix | 2024-10-20T02:52:52Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T02:43:14Z | ---
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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## 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
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[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
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## 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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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tjake/Yi-Coder-1.5B-Chat-JQ4 | tjake | 2024-10-20T02:47:28Z | 130 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2403.04652",
"base_model:01-ai/Yi-Coder-1.5B",
"base_model:finetune:01-ai/Yi-Coder-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T02:44:59Z | ---
license: apache-2.0
library_name: transformers
base_model: 01-ai/Yi-Coder-1.5B
---
# Quantized Version of 01-ai/Yi-Coder-1.5B-Chat
This model is a quantized variant of the 01-ai/Yi-Coder-1.5B-Chat model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
- Excelling in long-context understanding with a maximum context length of 128K tokens.
- Supporting 52 major programming languages:
```bash
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
```
For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
<p align="left">
<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/>
</p>
# Models
| Name | Type | Length | Download |
|--------------------|------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
| Yi-Coder-9B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat) |
| Yi-Coder-1.5B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat) |
| Yi-Coder-9B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B) |
| Yi-Coder-1.5B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B) |
| |
# Benchmarks
As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
<p align="left">
<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/>
</p>
# Quick Start
You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
device = "cuda" # the device to load the model onto
model_path = "01-ai/Yi-Coder-9B-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
prompt = "Write a quick sort algorithm."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024,
eos_token_id=tokenizer.eos_token_id
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
|
qucklecrabik/model | qucklecrabik | 2024-10-20T02:23:10Z | 17 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:IlyaGusev/saiga_llama3_8b",
"base_model:quantized:IlyaGusev/saiga_llama3_8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-20T02:14:05Z | ---
base_model: IlyaGusev/saiga_llama3_8b
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** qucklecrabik
- **License:** apache-2.0
- **Finetuned from model :** IlyaGusev/saiga_llama3_8b
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)
|
EliteAide/ea-llama-1b | EliteAide | 2024-10-20T02:22:33Z | 137 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T02:21:53Z | ---
library_name: transformers
tags:
- unsloth
---
# 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]
- **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. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[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] |
magnifi/parser_user_v25a_epoch_7_lr_0.002 | magnifi | 2024-10-20T01:56:41Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T01:54:21Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral 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)
|
vocabtrimmer/camembert-base.xnli-fr.1 | vocabtrimmer | 2024-10-20T01:47:06Z | 5 | 0 | null | [
"safetensors",
"camembert",
"region:us"
] | null | 2024-10-20T01:46:51Z | # `vocabtrimmer/camembert-base.xnli-fr.1`
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the
[xnli](https://huggingface.co/datasets/xnli) (fr).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(fr).
* Evaluation on test split
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 33.31 | 33.31 | 33.31 | 18.87 | 33.31 | 22.16 | 33.31 |
* Evaluation on validation split
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 33.25 | 33.25 | 33.25 | 18.66 | 33.25 | 21.63 | 33.25 |
Check the result file [here](https://huggingface.co/vocabtrimmer/camembert-base.xnli-fr.1/raw/main/eval.json). |
Cran-May/Apollo2-9B-Q5_K_M-GGUF | Cran-May | 2024-10-20T01:41:03Z | 8 | 0 | null | [
"gguf",
"biology",
"medical",
"llama-cpp",
"gguf-my-repo",
"question-answering",
"ar",
"en",
"zh",
"ko",
"ja",
"mn",
"th",
"vi",
"lo",
"mg",
"de",
"pt",
"es",
"fr",
"ru",
"it",
"hr",
"gl",
"cs",
"co",
"la",
"uk",
"bs",
"bg",
"eo",
"sq",
"da",
"sa",
"no",
"gn",
"sr",
"sk",
"gd",
"lb",
"hi",
"ku",
"mt",
"he",
"ln",
"bm",
"sw",
"ig",
"rw",
"ha",
"dataset:FreedomIntelligence/ApolloMoEDataset",
"base_model:FreedomIntelligence/Apollo2-9B",
"base_model:quantized:FreedomIntelligence/Apollo2-9B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | question-answering | 2024-10-20T01:40:33Z | ---
license: apache-2.0
datasets:
- FreedomIntelligence/ApolloMoEDataset
language:
- ar
- en
- zh
- ko
- ja
- mn
- th
- vi
- lo
- mg
- de
- pt
- es
- fr
- ru
- it
- hr
- gl
- cs
- co
- la
- uk
- bs
- bg
- eo
- sq
- da
- sa
- 'no'
- gn
- sr
- sk
- gd
- lb
- hi
- ku
- mt
- he
- ln
- bm
- sw
- ig
- rw
- ha
metrics:
- accuracy
base_model: FreedomIntelligence/Apollo2-9B
pipeline_tag: question-answering
tags:
- biology
- medical
- llama-cpp
- gguf-my-repo
---
# Cran-May/Apollo2-9B-Q5_K_M-GGUF
This model was converted to GGUF format from [`FreedomIntelligence/Apollo2-9B`](https://huggingface.co/FreedomIntelligence/Apollo2-9B) 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/FreedomIntelligence/Apollo2-9B) 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 Cran-May/Apollo2-9B-Q5_K_M-GGUF --hf-file apollo2-9b-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Cran-May/Apollo2-9B-Q5_K_M-GGUF --hf-file apollo2-9b-q5_k_m-imat.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 Cran-May/Apollo2-9B-Q5_K_M-GGUF --hf-file apollo2-9b-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Cran-May/Apollo2-9B-Q5_K_M-GGUF --hf-file apollo2-9b-q5_k_m-imat.gguf -c 2048
```
|
BMRetriever/BMRetriever-410M | BMRetriever | 2024-10-20T01:29:02Z | 213 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"feature-extraction",
"medical",
"biology",
"retrieval",
"LLM",
"en",
"dataset:MedRAG/textbooks",
"dataset:MedRAG/pubmed",
"dataset:MedRAG/statpearls",
"dataset:mteb/raw_biorxiv",
"dataset:mteb/raw_medrxiv",
"dataset:ms_marco",
"dataset:BMRetriever/biomed_retrieval_dataset",
"arxiv:2404.18443",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-04-22T22:47:22Z | ---
license: mit
datasets:
- MedRAG/textbooks
- MedRAG/pubmed
- MedRAG/statpearls
- mteb/raw_biorxiv
- mteb/raw_medrxiv
- ms_marco
- BMRetriever/biomed_retrieval_dataset
language:
- en
tags:
- medical
- biology
- retrieval
- LLM
---
This model has been finetuned following the approach described in the paper **BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers**, published in EMNLP 2024. The associated GitHub repository is available here https://github.com/ritaranx/BMRetriever.
This model has 410M parameters. See the paper [link](https://arxiv.org/abs/2404.18443) for details.
## Usage
Pre-trained models can be loaded through the HuggingFace transformers library:
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("BMRetriever/BMRetriever-410M")
tokenizer = AutoTokenizer.from_pretrained("BMRetriever/BMRetriever-410M")
```
Then embeddings for different sentences can be obtained by doing the following:
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
embedding = last_hidden[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden.shape[0]
embedding = last_hidden[torch.arange(batch_size, device=last_hidden.device), sequence_lengths]
return embedding
def get_detailed_instruct_query(task_description: str, query: str) -> str:
return f'{task_description}\nQuery: {query}'
def get_detailed_instruct_passage(passage: str) -> str:
return f'Represent this passage\npassage: {passage}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a scientific claim, retrieve documents that support or refute the claim'
queries = [
get_detailed_instruct_query(task, 'Cis-acting lncRNAs control the expression of genes that are positioned in the vicinity of their transcription sites.'),
get_detailed_instruct_query(task, 'Forkhead 0 (fox0) transcription factors are involved in apoptosis.')
]
# No need to add instruction for retrieval documents
documents = [
get_detailed_instruct_passage("Gene regulation by the act of long non-coding RNA transcription Long non-protein-coding RNAs (lncRNAs) are proposed to be the largest transcript class in the mouse and human transcriptomes. Two important questions are whether all lncRNAs are functional and how they could exert a function. Several lncRNAs have been shown to function through their product, but this is not the only possible mode of action. In this review we focus on a role for the process of lncRNA transcription, independent of the lncRNA product, in regulating protein-coding-gene activity in cis. We discuss examples where lncRNA transcription leads to gene silencing or activation, and describe strategies to determine if the lncRNA product or its transcription causes the regulatory effect."),
get_detailed_instruct_passage("Noncoding transcription at enhancers: general principles and functional models. Mammalian genomes are extensively transcribed outside the borders of protein-coding genes. Genome-wide studies recently demonstrated that cis-regulatory genomic elements implicated in transcriptional control, such as enhancers and locus-control regions, represent major sites of extragenic noncoding transcription. Enhancer-templated transcripts provide a quantitatively small contribution to the total amount of cellular nonribosomal RNA; nevertheless, the possibility that enhancer transcription and the resulting enhancer RNAs may, in some cases, have functional roles, rather than represent mere transcriptional noise at accessible genomic regions, is supported by an increasing amount of experimental data. In this article we review the current knowledge on enhancer transcription and its functional implications.")
]
input_texts = queries + documents
max_length = 512
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length-1, padding=True, truncation=True, return_tensors='pt')
# Important! Adding EOS token at the end
batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt').to("cuda")
model.eval()
with torch.no_grad():
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
```
Then similarity scores between the different sentences are obtained with a dot product between the embeddings:
```python
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
```
## Citation
If you find this repository helpful, please kindly consider citing the corresponding paper. Thanks!
```
@inproceedings{xu2024bmretriever,
title={BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers},
author={Ran Xu and Wenqi Shi and Yue Yu and Yuchen Zhuang and Yanqiao Zhu and May D. Wang and Joyce C. Ho and Chao Zhang and Carl Yang},
year={2024},
booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
}
``` |
MrGohlke/ID_CTI_Llama70B_v2.1 | MrGohlke | 2024-10-20T00:47:09Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T22:01:41Z | ---
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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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<!-- 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
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### 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. -->
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## 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
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[More Information Needed]
## Training Details
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### 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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
ragefu/ftxclip20241019model | ragefu | 2024-10-20T00:21:21Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"xclip",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-10-20T00:20:54Z | ---
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]
- **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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
richardchai/plp_sentiment_clr_distilbert | richardchai | 2024-10-19T22:56:54Z | 5 | 0 | null | [
"safetensors",
"distilbert",
"en",
"license:mit",
"region:us"
] | null | 2024-10-19T22:56:18Z | ---
language: en
license: mit
---
# Model Card
Bank Sentiment Classifier - distilBERT
Developed by: Richard Chai, https://www.linkedin.com/in/richardchai/
This model has been fine-tuned for Bank User Sentiment Identification.
Currently, it identifies the following Sentiment:
'very negative': 0,
'negative': 1,
'neutral': 2,
'positive': 3,
'very positive': 4
## Model Details
- **Model type**: Transformer-based (e.g., BERT, DistilBERT, etc.): distilBERT
- **Dataset**: Stanford Sentiment Treebank SST-5 or another sentiment dataset
- **Fine-tuning**: The model was fine-tuned for X epochs using a learning rate of Y on a dataset with Z samples.
## Usage
You can use this model to classify text sentiment as follows:
```python
from transformers import pipeline
# Check if GPU is available
device = 0 if torch.cuda.is_available() else -1
model_checkpt = "richardchai/plp_sentiment_clr_distilbert"
clf = pipeline('text-classification', model="model_trained/distilbert", device=device)
result = clf(f"['please tell me more about your fixed deposit.', 'your savings rate is terrible!', 'Yay! I have finally paid off my loan!', 'I am rich, hurray!']")
print(result)
```
|
bartowski/LongWriter-glm4-9b-abliterated-GGUF | bartowski | 2024-10-19T22:41:35Z | 419 | 2 | null | [
"gguf",
"llm",
"glm",
"glm4",
"chatglm",
"llama",
"chat",
"instruct",
"it",
"abliterated",
"longwriter",
"long context",
"text-generation",
"en",
"base_model:byroneverson/LongWriter-glm4-9b-abliterated",
"base_model:quantized:byroneverson/LongWriter-glm4-9b-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-19T22:17:34Z | ---
base_model: byroneverson/LongWriter-glm4-9b-abliterated
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- llm
- glm
- glm4
- chatglm
- llama
- chat
- instruct
- it
- abliterated
- longwriter
- long context
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of LongWriter-glm4-9b-abliterated
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3930">b3930</a> for quantization.
Original model: https://huggingface.co/byroneverson/LongWriter-glm4-9b-abliterated
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
[gMASK]<sop><|system|>
{system_prompt}<|user|>
{prompt}<|assistant|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [LongWriter-glm4-9b-abliterated-f16.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-f16.gguf) | f16 | 18.81GB | false | Full F16 weights. |
| [LongWriter-glm4-9b-abliterated-Q8_0.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q8_0.gguf) | Q8_0 | 9.99GB | false | Extremely high quality, generally unneeded but max available quant. |
| [LongWriter-glm4-9b-abliterated-Q6_K_L.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q6_K_L.gguf) | Q6_K_L | 8.56GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q6_K.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q6_K.gguf) | Q6_K | 8.26GB | false | Very high quality, near perfect, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q5_K_L.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q5_K_L.gguf) | Q5_K_L | 7.53GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q5_K_M.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q5_K_M.gguf) | Q5_K_M | 7.14GB | false | High quality, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q4_K_L.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_K_L.gguf) | Q4_K_L | 6.71GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q5_K_S.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q5_K_S.gguf) | Q5_K_S | 6.69GB | false | High quality, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q4_K_M.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_K_M.gguf) | Q4_K_M | 6.25GB | false | Good quality, default size for must use cases, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q3_K_XL.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q3_K_XL.gguf) | Q3_K_XL | 5.82GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [LongWriter-glm4-9b-abliterated-Q4_K_S.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_K_S.gguf) | Q4_K_S | 5.75GB | false | Slightly lower quality with more space savings, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q4_0.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_0.gguf) | Q4_0 | 5.47GB | false | Legacy format, generally not worth using over similarly sized formats |
| [LongWriter-glm4-9b-abliterated-Q4_0_8_8.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_0_8_8.gguf) | Q4_0_8_8 | 5.46GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
| [LongWriter-glm4-9b-abliterated-Q4_0_4_8.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_0_4_8.gguf) | Q4_0_4_8 | 5.46GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). *Don't use on Mac or Windows*. |
| [LongWriter-glm4-9b-abliterated-Q4_0_4_4.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q4_0_4_4.gguf) | Q4_0_4_4 | 5.46GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. *Don't use on Mac or Windows*. |
| [LongWriter-glm4-9b-abliterated-Q3_K_L.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q3_K_L.gguf) | Q3_K_L | 5.28GB | false | Lower quality but usable, good for low RAM availability. |
| [LongWriter-glm4-9b-abliterated-IQ4_XS.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-IQ4_XS.gguf) | IQ4_XS | 5.25GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [LongWriter-glm4-9b-abliterated-Q3_K_M.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q3_K_M.gguf) | Q3_K_M | 5.06GB | false | Low quality. |
| [LongWriter-glm4-9b-abliterated-IQ3_M.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-IQ3_M.gguf) | IQ3_M | 4.81GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [LongWriter-glm4-9b-abliterated-Q2_K_L.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q2_K_L.gguf) | Q2_K_L | 4.60GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [LongWriter-glm4-9b-abliterated-Q3_K_S.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q3_K_S.gguf) | Q3_K_S | 4.59GB | false | Low quality, not recommended. |
| [LongWriter-glm4-9b-abliterated-IQ3_XS.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-IQ3_XS.gguf) | IQ3_XS | 4.43GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [LongWriter-glm4-9b-abliterated-Q2_K.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-Q2_K.gguf) | Q2_K | 3.99GB | false | Very low quality but surprisingly usable. |
| [LongWriter-glm4-9b-abliterated-IQ2_M.gguf](https://huggingface.co/bartowski/LongWriter-glm4-9b-abliterated-GGUF/blob/main/LongWriter-glm4-9b-abliterated-IQ2_M.gguf) | IQ2_M | 3.93GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## 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/LongWriter-glm4-9b-abliterated-GGUF --include "LongWriter-glm4-9b-abliterated-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/LongWriter-glm4-9b-abliterated-GGUF --include "LongWriter-glm4-9b-abliterated-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (LongWriter-glm4-9b-abliterated-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## 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.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
keithdrexel/unsloth-llama-3.2-3b-tldr-unsloth-chat-template | keithdrexel | 2024-10-19T22:20:09Z | 139 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-3B-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T22:17:28Z | ---
base_model: unsloth/Llama-3.2-3B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** keithdrexel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-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)
|
merty/attempt4_7b_qwen2.5 | merty | 2024-10-19T22:01:25Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T21:48:07Z | ---
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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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[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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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
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[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]
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[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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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RaniRahbani/Llama-3.1_8b_Dietitian_v3 | RaniRahbani | 2024-10-19T21:51:19Z | 19 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T21:42:27Z | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** RaniRahbani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
goku35855/speecht5_finetuned_turkish | goku35855 | 2024-10-19T21:40:46Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-10-19T21:33:04Z | ---
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]
- **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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[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]
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## Model Card Contact
[More Information Needed] |
swap-uniba/LLaVA-NDiNO_pt_short_long | swap-uniba | 2024-10-19T21:31:40Z | 7 | 0 | null | [
"safetensors",
"llava_next",
"text-generation",
"it",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | text-generation | 2024-10-18T08:16:35Z | ---
license: llama3
language:
- it
base_model:
- meta-llama/Meta-Llama-3-8B
- openai/clip-vit-large-patch14-336
pipeline_tag: text-generation
---
# Model Card for LLaVA-NDiNO_pt_short_long
## Model description
<!-- Provide a quick summary of what the model is/does. -->
**LLaVA-NDiNO** is a family of *Large Vision Language Models (LVLMs)* that have been trained for the Italian language.
The model was trained by instruction-tuning [LLaVA-NDiNO_pt_short](https://huggingface.co/swap-uniba/LLaVA-NDiNO_pt_short) on an Italian machine-translated version of [LLaVA Conversation 58k](https://huggingface.co/datasets/jxu124/llava_conversation_58k).
If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
- **Repository:** https://github.com/swapUniba/LLaVA-NDiNO
- **Developed by:** Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, Giovanni Semeraro
- **Funded by:** PNRR project FAIR - Future AI Research
- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
- **Model type:** LLaMA 3 + CLIP
- **Language(s) (NLP):** Italian
- **License:** Llama 3 Community License
- **Finetuned from model:** [swap-uniba/LLaVA-NDiNO_pt_short](https://huggingface.co/swap-uniba/LLaVA-NDiNO_pt_short)
## Example Usage
```python
import torch
import requests
from PIL import Image
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, set_seed
model_name = "swap-uniba/LLaVA-NDiNO_pt_short_long"
processor = LlavaNextProcessor.from_pretrained(model_name)
model = LlavaNextForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw)
chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
conversation = [
{
"role": "user",
"content": "<image>\nCosa c'è di strano in questa immagine?"
},
]
prompt = processor.apply_chat_template(conversation, chat_template, add_generation_prompt=True)
inputs = processor(prompt, image, return_tensors="pt")
set_seed(42)
output = model.generate(**inputs, max_new_tokens=4096)
print(processor.decode(output[0][inputs.input_ids.shape[1]:]))
```
## Citation
```
@inproceedings{musacchioLLaVANDiNO,
title={LLaVA-NDiNO: Empowering LLMs with Multimodality for the Italian Language},
author={Musacchio, Elio and Siciliani, Lucia and Basile, Pierpaolo and Semeraro, Giovanni},
booktitle={Proceedings of the Eighth Workshop on Natural Language for Artificial Intelligence (NL4AI 2024) co-located with 23th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2024)},
year={2024}
}
``` |
Sagicc/whisper-large-v3-sr-onnx | Sagicc | 2024-10-19T21:31:16Z | 6 | 2 | transformers.js | [
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"base_model:Sagicc/whisper-large-v3-sr-combined",
"base_model:quantized:Sagicc/whisper-large-v3-sr-combined",
"license:mit",
"region:us"
] | automatic-speech-recognition | 2023-11-25T16:42:14Z | ---
base_model: Sagicc/whisper-large-v3-sr-combined
library_name: transformers.js
license: mit
---
Fine-tunned Serbian Whisper v3 to use it with Transformers.js
ONNX converted
https://huggingface.co/Sagicc/whisper-large-v3-sr-combined with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
Sagicc/whisper-medium-sr-onnx | Sagicc | 2024-10-19T21:30:41Z | 8 | 0 | transformers.js | [
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"base_model:Sagicc/whisper-medium-sr-v2",
"base_model:quantized:Sagicc/whisper-medium-sr-v2",
"license:mit",
"region:us"
] | automatic-speech-recognition | 2024-01-02T14:59:05Z | ---
base_model: Sagicc/whisper-medium-sr-v2
library_name: transformers.js
license: mit
---
Fine-tunned Serbian Whisper medium to use it with Transformers.js
ONNX converted
[Sagicc/whisper-medium-sr-v2](https://huggingface.co/Sagicc/whisper-medium-sr-v2) with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
129developer/test-4bit | 129developer | 2024-10-19T21:17:54Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-19T21:12:12Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** 129developer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
ussipan/SipanGPT-0.2-Llama-3.2-1B-GGUF | ussipan | 2024-10-19T21:08:25Z | 158 | 0 | transformers | [
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"q4_k_m",
"4bit",
"sharegpt",
"pretaining",
"finetuning",
"Q5_K_M",
"Q8_0",
"uss",
"Perú",
"Lambayeque",
"Chiclayo",
"text2text-generation",
"es",
"dataset:ussipan/sipangpt",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] | text2text-generation | 2024-10-09T07:51:46Z | ---
base_model: unsloth/Meta-Llama-3.2-1B-Instruct
language:
- es
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
- q4_k_m
- 4bit
- sharegpt
- pretaining
- finetuning
- Q5_K_M
- Q8_0
- uss
- Perú
- Lambayeque
- Chiclayo
datasets:
- ussipan/sipangpt
pipeline_tag: text2text-generation
new_version: ussipan/SipanGPT-0.3-Llama-3.2-1B-GGUF
---
# SipánGPT 0.2 Llama 3.2 1B GGUF
- Modelo pre-entrenado para responder preguntas de la Universidad Señor de Sipán de Lambayeque, Perú.
- Pre-trained model to answer questions from the Señor de Sipán University of Lambayeque, Peru.
## Testing the model
- Debido a la cantidad de conversaciones con las que fue entrenado (5400 conversaciones), el modelo genera bastantes alucinaciones.
- Due to the number of conversations with which it was trained (5400 conversations), the model generates quite a few hallucinations.
# Uploaded model
- **Developed by:** jhangmez
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.2-1B-Instruct
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)
---
## SipánGPT 0.2 Llama 3.2 1B GGUF
<div style="display: flex; align-items: center; height: fit-content;">
<img src="https://avatars.githubusercontent.com/u/60937214?v=4" width="40" style="margin-right: 10px;"/>
<span>Hecho con ❤️ por Jhan Gómez P.</span>
</div> |
lucasaltmann/7501843503374 | lucasaltmann | 2024-10-19T20:53:02Z | 20 | 0 | ultralytics | [
"ultralytics",
"v8",
"modelos",
"model-index",
"region:us"
] | null | 2024-08-21T23:23:11Z |
---
tags:
- modelos
library_name: ultralytics
library_version: 8.0.239
inference: false
model-index:
- name: lucasaltmann/7501843503374
results:
- task:
type: object-detection
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.995 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="lucasaltmann/7501843503374" src="https://huggingface.co/lucasaltmann/7501843503374/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['7501843503374']
```
|
nihiluis/legal-sachzivil-subsumption-roberta | nihiluis | 2024-10-19T20:52:49Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T20:52:12Z | ---
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]
- **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] |
bunnycore/Phi-3.5-mini-TitanFusion-V2 | bunnycore | 2024-10-19T20:47:02Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"mergekit",
"merge",
"conversational",
"custom_code",
"arxiv:2306.01708",
"base_model:ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1",
"base_model:merge:ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1",
"base_model:bunnycore/Phi-3.5-mini-TitanFusion-0.1",
"base_model:merge:bunnycore/Phi-3.5-mini-TitanFusion-0.1",
"base_model:bunnycore/Phi-3.5-rp-lora_model",
"base_model:merge:bunnycore/Phi-3.5-rp-lora_model",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:merge:microsoft/Phi-3.5-mini-instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T20:44:33Z | ---
base_model:
- bunnycore/Phi-3.5-mini-TitanFusion-0.1
- bunnycore/Phi-3.5-rp-lora_model
- microsoft/Phi-3.5-mini-instruct
- bunnycore/Phi-3.5-mini-TitanFusion-0.1
- ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1
- bunnycore/Phi-3.5-rp-lora_model
library_name: transformers
tags:
- mergekit
- merge
---
# merge
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) as a base.
### Models Merged
The following models were included in the merge:
* [bunnycore/Phi-3.5-mini-TitanFusion-0.1](https://huggingface.co/bunnycore/Phi-3.5-mini-TitanFusion-0.1) + [bunnycore/Phi-3.5-rp-lora_model](https://huggingface.co/bunnycore/Phi-3.5-rp-lora_model)
* [bunnycore/Phi-3.5-mini-TitanFusion-0.1](https://huggingface.co/bunnycore/Phi-3.5-mini-TitanFusion-0.1)
* [ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1) + [bunnycore/Phi-3.5-rp-lora_model](https://huggingface.co/bunnycore/Phi-3.5-rp-lora_model)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: bunnycore/Phi-3.5-mini-TitanFusion-0.1+bunnycore/Phi-3.5-rp-lora_model
parameters:
density: 0.5
weight: 0.5
- model: bunnycore/Phi-3.5-mini-TitanFusion-0.1
parameters:
density: 0.5
weight: 0.5
- model: ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1+bunnycore/Phi-3.5-rp-lora_model
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: microsoft/Phi-3.5-mini-instruct
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
fierce-cats/beatrice-trainer | fierce-cats | 2024-10-19T20:30:05Z | 0 | 27 | null | [
"audio",
"speech",
"voice-conversion",
"audio-to-audio",
"dataset:reazon-research/reazonspeech",
"dataset:dns-challenge",
"dataset:libritts-r",
"arxiv:2101.08692",
"arxiv:2306.06546",
"arxiv:2006.11477",
"arxiv:2210.13438",
"arxiv:2010.05646",
"arxiv:2306.00814",
"arxiv:2309.02836",
"arxiv:2106.07889",
"arxiv:2111.02392",
"arxiv:2401.03078",
"arxiv:2402.00892",
"arxiv:2309.14507",
"arxiv:2401.10460",
"license:mit",
"region:us"
] | audio-to-audio | 2024-06-12T14:31:07Z | ---
license: mit
pipeline_tag: audio-to-audio
tags:
- audio
- speech
- voice-conversion
datasets:
- reazon-research/reazonspeech
- dns-challenge
- libritts-r
---
# Beatrice Trainer
超低遅延・低負荷・低容量を特徴とする完全無料の声質変換 VST 「[Beatrice 2](https://prj-beatrice.com)」のモデル学習用ツールキットです。
Beatrice 2 は、以下を目標に開発されています。
* 自分の変換された声を聴きながら、歌を快適に歌えるようにする
* 入力された声の抑揚を変換音声に正確に反映し、繊細な表現を可能にする
* 変換音声の高い自然性と明瞭さ
* 多様な変換先話者
* 公式 VST での変換時、外部の録音機器を使った実測で 50ms 程度の遅延
* 開発者のノート PC (Intel Core i7-1165G7) でシングルスレッドで動作させ、RTF < 0.25 となる程度の負荷
* 最小構成で 30MB 以下の容量
* VST と [VC Client](https://github.com/w-okada/voice-changer) での動作
* その他 (内緒)
## Release Notes
* **2024-10-20**: Beatrice Trainer 2.0.0-beta.2 をリリースしました。
* **[公式 VST](https://prj-beatrice.com) や [VC Client](https://github.com/w-okada/voice-changer) を最新版にアップデートしてください。新しい Trainer で生成したモデルは、古いバージョンの公式 VST や VC Client で動作しません。**
* [Scaled Weight Standardization](https://arxiv.org/abs/2101.08692) の導入により、学習の安定性が向上しました。
* 無音に非常に近い音声に対する損失の計算結果が nan になる問題を修正し、学習の安定性が向上しました。
* 周期信号の生成方法を変更し、事前学習モデルを用いない場合により少ない学習ステップ数で高品質な変換音声を生成できるようになりました。
* [FIRNet](https://ast-astrec.nict.go.jp/release/preprints/preprint_icassp_2024_ohtani.pdf) に着想を得たポストフィルタ構造を導入し、変換音声の品質が向上しました。
* [D4C](https://www.sciencedirect.com/science/article/pii/S0167639316300413) を損失関数に導入し、変換音声の品質が向上しました。
* [Multi-scale mel loss](https://arxiv.org/abs/2306.06546) を導入しました。
* 冗長な逆伝播の除去や `torch.backends.cudnn.benchmark` の部分的な無効化などにより、学習速度が向上しました。
* 学習データにモノラルでない音声ファイルが含まれる場合にエラーが発生する問題を修正しました。
* 音量計算の誤りを修正し、学習時と推論時の変換結果の不一致が解消されました。
* PyTorch のバージョンの下限を修正しました。
* Windows 環境で CPU 版の PyTorch がインストールされる問題を修正しました。
* Windows 環境で DataLoader の動作が非常に遅くなる問題を修正しました。
* その他いくつかの変更を行いました。
* **2024-07-27**: Beatrice Trainer 2.0.0-beta.0 をリリースしました。
## Prerequisites
Beatrice は、既存の学習済みモデルを用いて声質の変換を行うだけであれば GPU を必要としません。
しかし、新たなモデルの作成を効率良く行うためには GPU が必要です。
学習スクリプトを実行すると、デフォルト設定では 9GB 程度の VRAM を消費します。
GeForce RTX 4090 を使用した場合、 30 分程度で学習が完了します。
GPU を手元に用意できない場合でも、以下のリポジトリを使用して Google Colab 上で学習を行うことができます。
* [w-okada/beatrice-trainer-colab](https://github.com/w-okada/beatrice-trainer-colab)
## Getting Started
### 1. Download This Repo
Git などを使用して、このリポジトリをダウンロードしてください。
```sh
git lfs install
git clone https://huggingface.co/fierce-cats/beatrice-trainer
cd beatrice-trainer
```
### 2. Environment Setup
Poetry などを使用して、依存ライブラリをインストールしてください。
```sh
poetry install
poetry shell
# Alternatively, you can use pip to install dependencies directly:
# pip3 install -e .
```
正しくインストールできていれば、 `python3 beatrice_trainer -h` で以下のようなヘルプが表示されます。
```
usage: beatrice_trainer [-h] [-d DATA_DIR] [-o OUT_DIR] [-r] [-c CONFIG]
options:
-h, --help show this help message and exit
-d DATA_DIR, --data_dir DATA_DIR
directory containing the training data
-o OUT_DIR, --out_dir OUT_DIR
output directory
-r, --resume resume training
-c CONFIG, --config CONFIG
path to the config file
```
### 3. Prepare Your Training Data
下図のように学習データを配置してください。
```
your_training_data_dir
+---alice
| +---alices_wonderful_speech.wav
| +---alices_excellent_speech.flac // FLAC, MP3, and some other formats are also okay.
| `---...
+---bob
| +---bobs_fantastic_speech.wav
| +---bobs_speeches
| | `---bobs_awesome_speech.wav // Audio files in nested directory will also be used.
| `---...
`---...
```
学習データ用ディレクトリの直下に各話者のディレクトリを作る必要があります。
各話者のディレクトリの中の構造や音声ファイルの名前は自由です。
学習を行うデータが 1 話者のみの場合も、話者のディレクトリを作る必要があることに注意してください。
```
your_training_data_dir_with_only_one_speaker
+---charlies_brilliant_speech.wav // Wrong.
`---...
```
```
your_training_data_dir_with_only_one_speaker
`---charlie
+---charlies_brilliant_speech.wav // Correct!
`---...
```
### 4. Train Your Model
学習データを配置したディレクトリと出力ディレクトリを指定して学習を開始します。
```sh
python3 beatrice_trainer -d <your_training_data_dir> -o <output_dir>
```
(Windowns の場合、 `beatrice_trainer` の代わりに `.\beatrice_trainer\__main__.py` を指定しないと正しく動作しないという報告があります。)
学習の状況は、 TensorBoard で確認できます。
```sh
tensorboard --logdir <output_dir>
```
### 5. After Training
学習が正常に完了すると、出力ディレクトリ内に `paraphernalia_(data_dir_name)_(step)` という名前のディレクトリが生成されています。
このディレクトリを[公式 VST](https://prj-beatrice.com) や [VC Client](https://github.com/w-okada/voice-changer) で読み込むことで、ストリーム (リアルタイム) 変換を行うことができます。
**読み込めない場合は公式 VST や VC Client のバージョンが古い可能性がありますので、最新のバージョンにアップデートしてください。**
## Detailed Usage
### Training
使用するハイパーパラメータや事前学習済みモデルをデフォルトと異なるものにする場合は、デフォルト値の書かれたコンフィグファイルである `assets/default_config.json` を別の場所にコピーして値を編集し、 `-c` でファイルを指定します。
`assets/default_config.json` を直接編集すると壊れるので注意してください。
また、コンフィグファイルに `data_dir` キーと `out_dir` キーを追加し、学習データを配置したディレクトリと出力ディレクトリを絶対パスまたはリポジトリルートからの相対パスで記載することで、コマンドライン引数での指定を省略できます。
```sh
python3 beatrice_trainer -c <your_config.json>
```
何らかの理由で学習が中断された場合、出力ディレクトリに `checkpoint_latest.pt` が生成されていれば、その学習を行っていたコマンドに `-r` オプションを追加して実行することで、最後に保存されたチェックポイントから学習を再開できます。
```sh
python3 beatrice_trainer -d <your_training_data_dir> -o <output_dir> -r
```
### Output Files
学習スクリプトを実行すると、出力ディレクトリ内に以下のファイル・ディレクトリが生成されます。
* `paraphernalia_(data_dir_name)_(step)`
* ストリーム変換に必要なファイルを全て含むディレクトリです。
* 学習途中のものも出力される場合があり、必要なステップ数のもの以外は削除して問題ありません。
* このディレクトリ以外の出力物はストリーム変換に使用されないため、不要であれば削除して問題ありません。
* `checkpoint_(data_dir_name)_(step)`
* 学習を途中から再開するためのチェックポイントです。
* checkpoint_latest.pt にリネームし、 `-r` オプションを付けて学習スクリプトを実行すると、そのステップ数から学習を再開できます。
* `checkpoint_latest.pt`
* 最も新しい checkpoint_(data_dir_name)_(step) のコピーです。
* `config.json`
* 学習に使用されたコンフィグです。
* `events.out.tfevents.*`
* TensorBoard で表示される情報を含むデータです。
### Customize Paraphernalia
学習スクリプトによって生成された paraphernalia ディレクトリ内にある `beatrice_paraphernalia_*.toml` ファイルを編集することで、 VST や VC Client 上での表示を変更できます。
`model.version` は、生成されたモデルのフォーマットバージョンを表すため、変更しないでください。
各 `description` は、長すぎると全文が表示されない場合があります。
現在表示できていても、将来的な VST や VC Client の仕様変更により表示できなくなる可能性があるため、余裕を持った文字数・行数に収めてください。
`portrait` に設定する画像は、 PNG 形式かつ正方形としてください。
## Distribution of Trained Models
このリポジトリを用いて生成したモデルの配布を歓迎します。
配布されたモデルは、 Project Beatrice およびその関係者の管理する SNS アカウントやウェブサイト上でご紹介させていただく場合があります。
その際、 `portrait` に設定された画像を掲載することがありますので、予めご承知おきください。
## Resource
このリポジトリには、学習などに使用する各種データが含まれています。
詳しくは [assets/README.md](https://huggingface.co/fierce-cats/beatrice-trainer/blob/main/assets/README.md) をご覧ください。
## Reference
* [wav2vec 2.0](https://arxiv.org/abs/2006.11477) ([Official implementation](https://github.com/facebookresearch/fairseq), [MIT License](https://github.com/facebookresearch/fairseq/blob/main/LICENSE))
* FeatureExtractor の実装に利用。
* [EnCodec](https://arxiv.org/abs/2210.13438) ([Official implementation](https://github.com/facebookresearch/encodec), [MIT License](https://github.com/facebookresearch/encodec/blob/main/LICENSE))
* GradBalancer の実装に利用。
* [HiFi-GAN](https://arxiv.org/abs/2010.05646) ([Official implementation](https://github.com/jik876/hifi-gan), [MIT License](https://github.com/jik876/hifi-gan/blob/master/LICENSE))
* DiscriminatorP の実装に利用。
* [Vocos](https://arxiv.org/abs/2306.00814) ([Official implementation](https://github.com/gemelo-ai/vocos), [MIT License](https://github.com/gemelo-ai/vocos/blob/main/LICENSE))
* ConvNeXtBlock の実装に利用。
* [BigVSAN](https://arxiv.org/abs/2309.02836) ([Official implementation](https://github.com/sony/bigvsan), [MIT License](https://github.com/sony/bigvsan/blob/main/LICENSE))
* SAN モジュールの実装に利用。
* [D4C](https://www.sciencedirect.com/science/article/pii/S0167639316300413) ([Unofficial implementation by tuanad121](https://github.com/tuanad121/Python-WORLD), [MIT License](https://github.com/tuanad121/Python-WORLD/blob/master/LICENSE.txt))
* 損失関数の実装に利用。
* [UnivNet](https://arxiv.org/abs/2106.07889) ([Unofficial implementation by maum-ai](https://github.com/maum-ai/univnet), [BSD 3-Clause License](https://github.com/maum-ai/univnet/blob/master/LICENSE))
* DiscriminatorR の実装に利用。
* [NF-ResNets](https://arxiv.org/abs/2101.08692)
* Scaled Weight Standardization のアイデアを利用。
* [Soft-VC](https://arxiv.org/abs/2111.02392)
* PhoneExtractor の基本的なアイデアとして利用。
* [Descript Audio Codec](https://arxiv.org/abs/2306.06546)
* Multi-scale mel loss のアイデアを利用。
* [StreamVC](https://arxiv.org/abs/2401.03078)
* 声質変換スキームの基本的なアイデアとして利用。
* [FIRNet](https://ast-astrec.nict.go.jp/release/preprints/preprint_icassp_2024_ohtani.pdf)
* FIR フィルタを Vocoder に適用するアイデアを利用。
* [EVA-GAN](https://arxiv.org/abs/2402.00892)
* SiLU を vocoder に適用するアイデアを利用。
* [Subramani et al., 2024](https://arxiv.org/abs/2309.14507)
* PitchEstimator の基本的なアイデアとして利用。
* [Agrawal et al., 2024](https://arxiv.org/abs/2401.10460)
* Vocoder の基本的なアイデアとして利用。
## License
このリポジトリ内のソースコードおよび学習済みモデルは MIT License のもとで公開されています。
詳しくは [LICENSE](https://huggingface.co/fierce-cats/beatrice-trainer/blob/main/LICENSE) をご覧ください。
|
TIGER-Lab/StructLM-13B | TIGER-Lab | 2024-10-19T20:29:59Z | 5 | 9 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:TIGER-Lab/SKGInstruct",
"arxiv:2402.16671",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-25T21:49:48Z | ---
license: mit
datasets:
- TIGER-Lab/SKGInstruct
language:
- en
metrics:
- accuracy
---
# 🏗️ StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Project Page: [https://tiger-ai-lab.github.io/StructLM/](https://tiger-ai-lab.github.io/StructLM/)
Paper: [https://arxiv.org/pdf/2402.16671.pdf](https://arxiv.org/pdf/2402.16671.pdf)
Code: [https://github.com/TIGER-AI-Lab/StructLM](https://github.com/TIGER-AI-Lab/StructLM)
## Introduction
StructLM, is a series of open-source large language models (LLMs) finetuned for structured knowledge grounding (SKG) tasks. We release 3 models:
7B | [StructLM-7B](https://huggingface.co/TIGER-Lab/StructLM-7B)
13B | [StructLM-13B](https://huggingface.co/TIGER-Lab/StructLM-13B)
34B | [StructLM-34B](https://huggingface.co/TIGER-Lab/StructLM-34B)
## Training Data
These models are trained on 🤗 [SKGInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct), an instruction-tuning dataset containing mixture of 19 SKG tasks combined with 🤗 [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Check out the dataset card for more details.
## Training Procedure
The models are fine-tuned with CodeLlama-Instruct-hf models as base models. Each model is trained for 3 epochs, and the best checkpoint is selected.
## Evaluation
Here are a subset of model evaluation results:
### Held in
| **Model** | **ToTTo** | **GrailQA** | **CompWebQ** | **MMQA** | **Feverous** | **Spider** | **TabFact** | **Dart** |
|-----------------------|--------------|----------|----------|----------|----------|----------|----------|----------|
| **StructLM-7B** | 49.4 | 80.4 | 78.3 | 85.2 | 84.4 | 72.4 | 80.8 | 62.2 |
| **StructLM-13B** | 49.3 | 79.2 | 80.4 | 86.0 | 85.0 | 74.1 | 84.7 | 61.4 |
| **StructLM-34B** | 50.2 | 82.2 | 81.9 | 88.1 | 85.7 | 74.6 | 86.6 | 61.8 |
### Held out
| **Model** | **BIRD** | **InfoTabs** | **FinQA** | **SQA** |
|-----------------------|--------------|----------|----------|----------|
| **StructLM-7B** | 22.3 | 55.3 | 27.3 | 49.7 |
| **StructLM-13B** | 22.8 | 58.1 | 25.6 | 36.1 |
| **StructLM-34B** | 24.7 | 61.8 | 36.2 | 44.2 |
## Usage
You can use the models through Huggingface's Transformers library.
Check our Github repo for the evaluation code: [https://github.com/TIGER-AI-Lab/StructLM](https://github.com/TIGER-AI-Lab/StructLM)
## Prompt Format
\*\*\***IMPORTANT**\*\*\*
**For this 13B model, the prompt format (different from 7B) is**
```
[INST] [INST] <<SYS>>
You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified.
<</SYS>>
{instruction} [/INST] [/INST]
```
To see concrete examples of this linearization, you can directly reference the 🤗 [SKGInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct) (coming soon).
We will provide code for linearizing this data shortly.
A few examples:
**tabular data**
```
col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
```
**knowledge triples (dart)**
```
Hawaii Five-O : notes : Episode: The Flight of the Jewels | [TABLECONTEXT] : [title] : Jeff Daniels | [TABLECONTEXT] : title : Hawaii Five-O
```
**knowledge graph schema (grailqa)**
```
top antiquark: m.094nrqp | physics.particle_antiparticle.self_antiparticle physics.particle_family physics.particle.antiparticle physics.particle_family.subclasses physics.subatomic_particle_generation physics.particle_family.particles physics.particle common.image.appears_in_topic_gallery physics.subatomic_particle_generation.particles physics.particle.family physics.particle_family.parent_class physics.particle_antiparticle physics.particle_antiparticle.particle physics.particle.generation
```
**example input**
```
[INST] [INST] <<SYS>>
You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified.
<</SYS>>
Use the information in the following table to solve the problem, choose between the choices if they are provided. table:
col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
question:
Allie kept track of how many kilometers she walked during the past 5 days. What is the range of the numbers? [/INST] [/INST]
```
## Intended Uses
These models are trained for research purposes. They are designed to be proficient in interpreting linearized structured input. Downstream uses can potentially include various applications requiring the interpretation of structured data.
## Limitations
While we've tried to build an SKG-specialized model capable of generalizing, we have shown that this is a challenging domain, and it may lack performance characteristics that allow it to be directly used in chat or other applications.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@misc{zhuang2024structlm,
title={StructLM: Towards Building Generalist Models for Structured Knowledge Grounding},
author={Alex Zhuang and Ge Zhang and Tianyu Zheng and Xinrun Du and Junjie Wang and Weiming Ren and Stephen W. Huang and Jie Fu and Xiang Yue and Wenhu Chen},
year={2024},
eprint={2402.16671},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
tjake/Mistral-7B-Instruct-v0.3-JQ4 | tjake | 2024-10-19T20:23:03Z | 50 | 0 | null | [
"safetensors",
"mistral",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:finetune:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2024-10-19T20:21:08Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.3
extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# Quantized Version of mistralai/Mistral-7B-Instruct-v0.3
This model is a quantized variant of the mistralai/Mistral-7B-Instruct-v0.3 model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
---
# Model Card for Mistral-7B-Instruct-v0.3
The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md)
- Extended vocabulary to 32768
- Supports v3 Tokenizer
- Supports function calling
## Installation
It is recommended to use `mistralai/Mistral-7B-Instruct-v0.3` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
```
pip install mistral_inference
```
## Download
```py
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
```
### Chat
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
```
mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256
```
### Instruct following
```py
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
### Function calling
```py
from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris?"),
],
)
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
## Generate with `transformers`
If you want to use Hugging Face `transformers` to generate text, you can do something like this.
```py
from transformers import pipeline
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3")
chatbot(messages)
```
## Function calling with `transformers`
To use this example, you'll need `transformers` version 4.42.0 or higher. Please see the
[function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling)
in the `transformers` docs for more information.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
def get_current_weather(location: str, format: str):
"""
Get the current weather
Args:
location: The city and state, e.g. San Francisco, CA
format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"])
"""
pass
conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
tools = [get_current_weather]
# format and tokenize the tool use prompt
inputs = tokenizer.apply_chat_template(
conversation,
tools=tools,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
inputs.to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool
results to the chat history so that the model can use them in its next generation. For a full tool calling example, please
see the [function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling),
and note that Mistral **does** use tool call IDs, so these must be included in your tool calls and tool results. They should be
exactly 9 alphanumeric characters.
## Limitations
The Mistral 7B 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, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall
|
nihiluis/legal-sach-subsumtion-roberta | nihiluis | 2024-10-19T20:21:48Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T20:21:03Z | ---
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]
- **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] |
nayem-ng/mdjannatulnayem_llama2_7b_finetuned_casuallm_lora | nayem-ng | 2024-10-19T20:20:47Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"peft",
"trl",
"torch",
"wandb",
"ipex",
"en",
"dataset:mlabonne/mini-platypus",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:finetune:NousResearch/Llama-2-7b-hf",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T13:13:30Z | ---
library_name: transformers
tags:
- peft
- trl
- torch
- wandb
- ipex
license: apache-2.0
language:
- en
base_model:
- NousResearch/Llama-2-7b-hf
datasets:
- mlabonne/mini-platypus
pipeline_tag: text-generation
---
# Model Card for Fine-Tuned Llama-2-7b Model
## Model Details
### Model Description
This model is a fine-tuned version of the Llama-2-7b model, specifically adapted for causal language modeling tasks. The fine-tuning utilizes the PEFT (Parameter-Efficient Fine-Tuning) technique with LoRA (Low-Rank Adaptation) to optimize performance while reducing computational costs. The training was conducted using the `mlabonne/mini-platypus` dataset and incorporates features such as integration with W&B for experiment tracking and Intel's Extension for PyTorch (IPEX) for enhanced performance.
- **Developed by:** Md. Jannatul Nayem
- **Model type:** Causal Language Model
- **Language(s) (NLP):** Engish
- **License:** Apache 2.0
- **Finetuned from model :** NousResearch/Llama-2-7b-hf
## Uses
### Direct Use
The model can be utilized for text generation tasks where the generation of coherent and contextually relevant text is required. This includes applications like chatbots, content creation, and interactive storytelling.
### Downstream Use
When fine-tuned, this model can serve in larger ecosystems for tasks like personalized dialogue systems, question answering, and other natural language understanding applications.
### Out-of-Scope Use
The model is not intended for use in generating harmful or misleading content, and users should exercise caution to prevent misuse in sensitive areas such as misinformation or hate speech.
### Recommendations
Users should consider implementing bias mitigation strategies and ensure thorough evaluation of the model's outputs, especially in sensitive applications.
## How to Get Started with the Model
Use the following code snippet to get started with loading and using the model:
```python
# Import necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import intel_extension_for_pytorch as ipex # Optional for Intel optimization
# Specify your Hugging Face model repository
hf_model = "nayem-ng/mdjannatulnayem_llama2_7b_finetuned_casuallm_lora"
# Load the fine-tuned model and tokenizer
model = AutoModelForCausalLM.from_pretrained(hf_model)
tokenizer = AutoTokenizer.from_pretrained(hf_model)
# Move the model to the desired device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Set the model to evaluation mode
model.eval()
# Optional: Optimize with Intel extensions for PyTorch
# Uncomment the next line if you want to use Intel optimizations
# model = ipex.optimize(model)
# Function to generate text
def generate_text(prompt, max_length=50):
# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt").to(device)
# Generate output
with torch.no_grad():
outputs = model.generate(**inputs, max_length=max_length)
# Decode and return the generated text
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example usage
if __name__ == "__main__":
prompt = "Once upon a time"
generated_text = generate_text(prompt)
print("Generated Text:", generated_text)
```
## Training Details
### Training Data
The model was fine-tuned using the mlabonne/mini-platypus dataset, which consists of diverse text inputs designed to enhance the model's capabilities in conversational settings.
[mlabonne/mini-platypus](https://huggingface.co/datasets/mlabonne/mini-platypus)
### Training Procedure
The training utilized a supervised fine-tuning procedure with the following hyperparameters:
#### Training Hyperparameters
The model was trained using bfloat16 (bf16) mixed precision, which allows for faster training times and reduced memory usage compared to traditional fp32 (float32). This precision format is particularly beneficial when working with large models, as it helps to maintain numerical stability while optimizing performance on compatible hardware.
- Training regime: bf16 mixed precision
- Number of epochs: 1
- Batch size: 10
- Warmup steps: 10
- Gradient accumulation steps: 1
- Learning rate: 2e-4
- Warmup steps: 10
- Evaluation strategy: Evaluations are performed every 1000 steps to monitor the model's performance during training.
## Model Examination
Further interpretability studies can be conducted to understand decision-making processes within the model's responses.
### Model Architecture and Objective
The model is based on the Transformer architecture, specifically designed for Causal Language Modeling (CLM).
### Compute Infrastructure
Intel® Tiber™ AI Cloud
#### Hardware
Intel(R) Xeon(R) Platinum 8480+
#### Software
PyTorch, Transformers Library (from Hugging Face),PEFT, TRL, WandB, Intel Extension for PyTorch (IPEX)
## Model Card Contact
🤖 Md. Jannatul nayem | [Mail]([email protected]) | [LinkedIn](https://www.linkedin.com/in/md-jannatul-nayem) |
nihiluis/legal-sach-components-roberta | nihiluis | 2024-10-19T20:17:01Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T20:14:49Z | ---
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]
- **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
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[More Information Needed]
### Downstream Use [optional]
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[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
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[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:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] |
GGorman/Qwen2.5-Coder-7B-Instruct-Q8-mlx | GGorman | 2024-10-19T20:12:36Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] | text-generation | 2024-10-19T20:12:05Z | ---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE
language:
- en
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
- mlx
---
# GGorman/Qwen2.5-Coder-7B-Instruct-Q8-mlx
The Model [GGorman/Qwen2.5-Coder-7B-Instruct-Q8-mlx](https://huggingface.co/GGorman/Qwen2.5-Coder-7B-Instruct-Q8-mlx) was converted to MLX format from [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) using mlx-lm version **0.19.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("GGorman/Qwen2.5-Coder-7B-Instruct-Q8-mlx")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
tjake/Llama-3.1-8B-Instruct-JQ4 | tjake | 2024-10-19T20:00:59Z | 77 | 0 | null | [
"safetensors",
"llama",
"facebook",
"meta",
"pytorch",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | text-generation | 2024-10-19T19:59:03Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
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\ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\
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\ to the Llama Materials and will continue in full force and effect until terminated\
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\ if you are in breach of any term or condition of this Agreement. Upon termination\
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\ 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.\n### Llama 3.1 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
\ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
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\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
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\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\
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\ or harmful conduct in the provision of employment, employment benefits, credit,\
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\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
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\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
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\ 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\n2. 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 Llama 3.1 related\
\ to the following:\n 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\n 2. Guns and illegal weapons (including weapon development)\n 3.\
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\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
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\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
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\ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
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\ and other means of fake online engagement\n4. Fail to appropriately disclose to\
\ end users any known dangers of your AI system\nPlease 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:\n * Reporting issues with\
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\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]"
extra_gated_fields:
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Last Name: text
Date of birth: date_picker
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Affiliation: text
Job title:
type: select
options:
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geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
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and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
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---
# Quantized Version of meta-llama/Llama-3.1-8B-Instruct
This model is a quantized variant of the meta-llama/Llama-3.1-8B-Instruct model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
---
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 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>Input modalities</strong>
</td>
<td><strong>Output modalities</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="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 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, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/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 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**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 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Tool use with transformers
LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
Here is a quick example showing a single simple tool:
```python
# First, define a tool
def get_current_temperature(location: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
# Next, create a chat and apply the chat template
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```
You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
and then call the tool and append the result, with the `tool` role, like so:
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>46.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
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 including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### 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 on 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).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
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 3.1 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 3.1 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 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’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 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
yvajaya/gtp2-geopo3 | yvajaya | 2024-10-19T19:57:40Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T19:57:19Z | ---
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]
- **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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mekjr1/ai-detect-2 | mekjr1 | 2024-10-19T19:55:36Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"longformer",
"text-classification",
"generated_from_trainer",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T13:45:50Z | ---
library_name: transformers
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ai-detect-2
results: []
---
<!-- 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. -->
# ai-detect-2
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6213
- Accuracy: 0.9234
- Precision: 0.8980
- Recall: 0.9901
- F1: 0.9418
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1356 | 1.0 | 6250 | 0.2998 | 0.9344 | 0.9132 | 0.9891 | 0.9497 |
| 0.0932 | 2.0 | 12500 | 0.5421 | 0.9052 | 0.8764 | 0.9879 | 0.9288 |
| 0.0019 | 3.0 | 18750 | 0.3828 | 0.9338 | 0.9118 | 0.9899 | 0.9493 |
| 0.0678 | 4.0 | 25000 | 0.2624 | 0.953 | 0.9384 | 0.9899 | 0.9635 |
| 0.0006 | 5.0 | 31250 | 0.5998 | 0.9083 | 0.8760 | 0.9942 | 0.9314 |
| 0.0002 | 6.0 | 37500 | 0.4959 | 0.9384 | 0.9183 | 0.9896 | 0.9526 |
| 0.0371 | 7.0 | 43750 | 0.6213 | 0.9234 | 0.8980 | 0.9901 | 0.9418 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
tdnathmlenthusiast/speecht5_finetuned_voice_dataset_bn_v_2 | tdnathmlenthusiast | 2024-10-19T19:54:07Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-10-18T17:29:25Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_voice_dataset_bn_v_2
results: []
---
<!-- 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. -->
# speecht5_finetuned_voice_dataset_bn_v_2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5100
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 125
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5988 | 0.3487 | 250 | 0.5447 |
| 0.569 | 0.6975 | 500 | 0.5202 |
| 0.5604 | 1.0462 | 750 | 0.5109 |
| 0.5569 | 1.3949 | 1000 | 0.5100 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
sugiv/Fuyu-8b-transfer-learned-spiqa-simplified | sugiv | 2024-10-19T19:35:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"fuyu",
"image-text-to-text",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-10-19T08:43:41Z | ---
library_name: transformers
license: cc-by-4.0
---
This model is transfer learned on scientific image visual question answering simplified dataset, sugiv/spiqa-simplified-for-fuyu8b-transfer-learning and it is based on
adept/fuyu-8b. Most of the model layers are frozen and as I am GPU poor, this transfer learned model was trained only on a subset of simplified dataset and for two epochs only on A100, 80GB rented and $10 dollars was total spent.
``` python
model_path="sugiv/Fuyu-8b-transfer-learned-spiqa-simplified"
processor = FuyuProcessor.from_pretrained(model_path)
model = FuyuForCausalLM.from_pretrained(model_path, device_map="auto")
text_prompt = "What color is the bus?\n"
url = "https://huggingface.co/adept/fuyu-8b/resolve/main/bus.png"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=text_prompt, images=image, return_tensors="pt").to("cuda:0")
# Move inputs to the same device as the model
device = next(model.parameters()).device
inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
# If 'image_patches' is a list of tensors, move each tensor to the correct device
if 'image_patches' in inputs and isinstance(inputs['image_patches'], list):
inputs['image_patches'] = [patch.to(device) for patch in inputs['image_patches']]
outputs = model.generate(
**inputs,
max_new_tokens=400,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
top_k=40,
top_p=0.92,
temperature=0.7,
do_sample=True
)
# Decode the output
generated_text = processor.decode(outputs[0], skip_special_tokens=True)
# Clean up the generated text
generated_text = generated_text.replace("|SPEAKER|", "").replace("|NEWLINE|", " ").strip()
if "\x04" in generated_text:
generated_text = generated_text.split("\x04")[-1].strip()
print(generated_text)
``` |
AndreyRzhaksinskiy/CDS-starcoder2-Ins-7b-E2E-20241019 | AndreyRzhaksinskiy | 2024-10-19T19:21:20Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T17:57:59Z | ---
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]
- **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] |
SuyashKirti96/model_test_5 | SuyashKirti96 | 2024-10-19T19:21:11Z | 19 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.2-3B-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-3B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T19:06:08Z | ---
base_model: unsloth/Llama-3.2-3B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** SuyashKirti96
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-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)
|
enver/lisan3.2_3b_freshtokenizer | enver | 2024-10-19T19:00:21Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-19T18:58:05Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** enver
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
MHGanainy/gpt2-xl-lora-multi-512-4-top | MHGanainy | 2024-10-19T18:46:37Z | 10 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openai-community/gpt2-xl",
"base_model:adapter:openai-community/gpt2-xl",
"license:mit",
"region:us"
] | null | 2024-10-16T20:09:39Z | ---
library_name: peft
license: mit
base_model: openai-community/gpt2-xl
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl-lora-multi-512-4
results: []
---
<!-- 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. -->
# gpt2-xl-lora-multi-512-4
This model is a fine-tuned version of [openai-community/gpt2-xl](https://huggingface.co/openai-community/gpt2-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3651
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 8735
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Watsonnn/whisper-large-v3-feng-20241019034925 | Watsonnn | 2024-10-19T18:42:07Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"zh",
"dataset:daniel0321forever/fsc-voice-data",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-18T19:49:47Z | ---
base_model: whisper-large-v3
datasets:
- daniel0321forever/fsc-voice-data
language:
- zh
library_name: transformers
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-feng-20241019034925
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: fsc-voice-data
type: daniel0321forever/fsc-voice-data
args: 'config: hi, split: test'
metrics:
- type: wer
value: 40.304826418289586
name: Wer
---
<!-- 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. -->
# whisper-large-v3-feng-20241019034925
This model is a fine-tuned version of [whisper-large-v3](https://huggingface.co/whisper-large-v3) on the fsc-voice-data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1469
- Wer: 40.3048
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2248 | 0.1565 | 100 | 0.1960 | 48.6876 |
| 0.205 | 0.3131 | 200 | 0.1892 | 45.6816 |
| 0.1894 | 0.4696 | 300 | 0.1750 | 44.7925 |
| 0.1879 | 0.6261 | 400 | 0.1731 | 44.6655 |
| 0.1733 | 0.7826 | 500 | 0.1626 | 43.7341 |
| 0.1627 | 0.9392 | 600 | 0.1532 | 40.8129 |
| 0.1093 | 1.0957 | 700 | 0.1500 | 41.2786 |
| 0.0782 | 1.2522 | 800 | 0.1508 | 39.9238 |
| 0.0731 | 1.4087 | 900 | 0.1508 | 39.8391 |
| 0.0784 | 1.5653 | 1000 | 0.1469 | 40.3048 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
Harshatheeswar/gpt2-scratch | Harshatheeswar | 2024-10-19T18:30:05Z | 41 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:Harshatheeswar/gpt2-scratch",
"base_model:finetune:Harshatheeswar/gpt2-scratch",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-11T21:36:34Z | ---
library_name: transformers
base_model: Harshatheeswar/gpt2-scratch
tags:
- generated_from_trainer
model-index:
- name: gpt2-scratch
results: []
---
<!-- 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. -->
# gpt2-scratch
This model is a fine-tuned version of [Harshatheeswar/gpt2-scratch](https://huggingface.co/Harshatheeswar/gpt2-scratch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1380
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.2094 | 1.0 | 1390 | 4.1380 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Niha14/speecht5_tts_voxpopuli_mr | Niha14 | 2024-10-19T18:28:01Z | 13 | 0 | null | [
"tensorboard",
"safetensors",
"speecht5",
"text-to-speech",
"en",
"mr",
"dataset:mozilla-foundation/common_voice_11_0",
"arxiv:1910.09700",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"region:us"
] | text-to-speech | 2024-10-13T18:30:08Z | ---
datasets:
- mozilla-foundation/common_voice_11_0
language:
- en
- mr
base_model:
- microsoft/speecht5_tts
- speechbrain/spkrec-xvect-voxceleb
- FacebookAI/xlm-roberta-base
pipeline_tag: text-to-speech
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Niharika Amritkar
- **Model type:** Text-to-Speech Model
### 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] |
nclgbd/llava-med-v1.5-mistral-7b-pretrain | nclgbd | 2024-10-19T18:24:56Z | 13 | 0 | null | [
"safetensors",
"llava_llama",
"generated_from_trainer",
"base_model:microsoft/llava-med-v1.5-mistral-7b",
"base_model:finetune:microsoft/llava-med-v1.5-mistral-7b",
"license:apache-2.0",
"region:us"
] | null | 2024-10-17T09:51:37Z | ---
base_model: microsoft/llava-med-v1.5-mistral-7b
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: llava-med-v1.5-mistral-7b-pretrain
results: []
---
<!-- 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. -->
# llava-med-v1.5-mistral-7b-pretrain
This model is a fine-tuned version of [microsoft/llava-med-v1.5-mistral-7b](https://huggingface.co/microsoft/llava-med-v1.5-mistral-7b) on an unknown dataset.
## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.0
- Tokenizers 0.15.1
|
Manas2708/gemma-2b-sql | Manas2708 | 2024-10-19T18:22:16Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-19T18:21:10Z | ---
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]
- **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] |
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