modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 18:27:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 549
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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1.01M
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|---|---|---|---|---|---|---|---|---|---|
Echefa/AI-TEST
|
Echefa
| 2023-03-28T03:18:00Z | 0 | 0 | null |
[
"es",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] | null | 2023-03-28T03:17:10Z |
---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- es
- en
---
|
vocabtrimmer/xlm-roberta-base-trimmed-es-tweet-sentiment-es
|
vocabtrimmer
| 2023-03-28T03:09:42Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-06T20:01:35Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-es-tweet-sentiment-es`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (spanish).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(spanish).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 65.06 | 65.06 | 65.06 | 64.96 | 65.06 | 64.9 | 65.06 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es-tweet-sentiment-es/raw/main/eval.json).
|
alangpp255/Question_classifier_V2
|
alangpp255
| 2023-03-28T03:07:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-28T02:49:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Question_classifier_V2
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. -->
# Question_classifier_V2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0259
- Accuracy: 0.9966
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0049 | 1.0 | 1215 | 0.0219 | 0.9967 |
| 0.0009 | 2.0 | 2430 | 0.0259 | 0.9966 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Maryem13/ppo-LunarLander-v
|
Maryem13
| 2023-03-28T02:39:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:55:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -213.05 +/- 99.89
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
platzi/platzi-vit-model-andres-galvis
|
platzi
| 2023-03-28T02:29:05Z | 225 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-27T20:45:13Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
widget:
- src: https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/healthy.jpeg
example_title: Healthy
- src: https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/bean_rust.jpeg
example_title: Bean Rust
model-index:
- name: platzi-vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9849624060150376
---
<!-- 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. -->
# platzi-vit-model-andres-galvis
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0227
- Accuracy: 0.9850
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1343 | 3.85 | 500 | 0.0227 | 0.9850 |
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/xlm-roberta-base-trimmed-it-60000-tweet-sentiment-it
|
vocabtrimmer
| 2023-03-28T02:24:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T11:45:19Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-it-60000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-60000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-60000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (italian).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(italian).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 65.29 | 65.29 | 65.29 | 65.06 | 65.29 | 67.2 | 65.29 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-it-60000-tweet-sentiment-it/raw/main/eval.json).
|
sohm/ppo-LunarLander-v2-Lunar1MM
|
sohm
| 2023-03-28T02:17:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T02:17:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.72 +/- 18.19
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sonny-dev/ppo-LunarLander-v2
|
sonny-dev
| 2023-03-28T02:14:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T15:38:53Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 282.99 +/- 20.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Seonwhee-Genome/bert-base
|
Seonwhee-Genome
| 2023-03-28T02:04:36Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:klue",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-27T02:59:08Z |
---
tags:
- generated_from_trainer
datasets:
- klue
model-index:
- name: bert-base
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. -->
# bert-base
This model was trained from scratch on the klue 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
sohm/ppo-LunarLander-v2-Lunar200Kv6
|
sohm
| 2023-03-28T02:03:07Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T02:02:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -117.72 +/- 67.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Senka1/hhhgy
|
Senka1
| 2023-03-28T01:59:16Z | 0 | 0 |
nemo
|
[
"nemo",
"not_for_all_eyes",
"text-classification",
"ru",
"dataset:nyanko7/LLaMA-65B",
"license:wtfpl",
"region:us"
] |
text-classification
| 2023-03-28T01:56:11Z |
---
license: wtfpl
datasets:
- nyanko7/LLaMA-65B
language:
- ru
metrics:
- character
library_name: nemo
pipeline_tag: text-classification
tags:
- not_for_all_eyes
---
|
vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg
|
vocabtrimmer
| 2023-03-28T01:52:19Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ru",
"dataset:lmqg/qg_ruquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-17T09:12:57Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ru
datasets:
- lmqg/qg_ruquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов."
example_title: "Question Generation Example 1"
- text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки."
example_title: "Question Generation Example 2"
- text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами."
example_title: "Question Generation Example 3"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_ruquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 18.11
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 33.73
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 28.94
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 86.01
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 64.61
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ru-120000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-120000) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [vocabtrimmer/mt5-small-trimmed-ru-120000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-120000)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ru", model="vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg")
# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 86.01 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 33.64 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 26.89 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 21.94 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 18.11 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 28.94 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 64.61 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 33.73 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-ru-120000
- max_length: 512
- max_length_output: 32
- epoch: 14
- batch: 16
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
ryanaspen/ppo-SnowballTarget
|
ryanaspen
| 2023-03-28T01:43:30Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:43:25Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: ryanaspen/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
artem9k/alpaca-lora-7b
|
artem9k
| 2023-03-28T01:42:05Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-03-28T01:39:14Z |
---
license: other
---
#### Trained on Monday Mar 27
#### ALPACA LORA model
#### Trained on alpaca-data-cleaned for 3 epochs
#### micro_batch_size 10
#### all other params default
#### https://github.com/tloen/alpaca-lora
|
nan2/clbenben
|
nan2
| 2023-03-28T01:37:55Z | 31 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-28T01:32:00Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### clbenben Dreambooth model trained by nan2 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.png)
.png)
.png)
.png)
|
sohm/ppo-LunarLander-v2-Lunar200Kv4
|
sohm
| 2023-03-28T01:34:30Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:34:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -154.66 +/- 38.62
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sohm/ppo-LunarLander-v2-Lunar200Kv3
|
sohm
| 2023-03-28T01:30:53Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:30:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -180.77 +/- 43.99
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-60000
|
vocabtrimmer
| 2023-03-28T01:16:05Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-28T00:52:35Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa): `vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-60000`
This model is a trimmed version of [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-ruquad-qa | vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-60000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 416,267,264 |
| parameter_size_embedding | 512,057,344 | 122,886,144 |
| vocab_size | 250,028 | 60,003 |
| compression_rate_full | 100.0 | 68.15 |
| compression_rate_embedding | 100.0 | 24.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 60000 | 2 |
|
aymenkhs/a2c-AntBulletEnv-v0
|
aymenkhs
| 2023-03-28T01:03:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:02:50Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1402.59 +/- 168.02
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sohm/ppo-LunarLander-v2-Lunar200Kv1
|
sohm
| 2023-03-28T01:02:51Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:02:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -144.32 +/- 41.46
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-30000
|
vocabtrimmer
| 2023-03-28T00:50:19Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-28T00:28:05Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa): `vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-30000`
This model is a trimmed version of [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-ruquad-qa | vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-30000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 385,548,288 |
| parameter_size_embedding | 512,057,344 | 61,448,192 |
| vocab_size | 250,028 | 30,004 |
| compression_rate_full | 100.0 | 63.12 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 30000 | 2 |
|
ryanaspen/reinforce-cartpole
|
ryanaspen
| 2023-03-28T00:48:23Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-17T20:18:41Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
GraymanMedia/test
|
GraymanMedia
| 2023-03-28T00:46:04Z | 33 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-28T00:16:39Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### test Dreambooth model trained by GraymanMedia with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
makdong/bert-finetuned-squad22
|
makdong
| 2023-03-28T00:42:43Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-27T23:47:07Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad22
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. -->
# bert-finetuned-squad22
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Neurogen/neurogen
|
Neurogen
| 2023-03-28T00:28:01Z | 25 | 8 |
diffusers
|
[
"diffusers",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T16:19:59Z |
---
license: other
---
According to the tests, this model gives a very good detail of skin and textures. Great for close-up photorealistic portraits as well as various characters and models.
UPD 26.03.2023:
v1.1: The new version has taken a step forward in the direction of versatility.
The detail of the half body planes and full body planes has been improved (don't forget to use the Hires fix). In addition to photorealism, you can use this model for digital art and anime as well. Texture detailing has been improved, and new colors have been added.
|
jakub014/ColD-Fusion-bert-base-uncased-itr23-seed0-finetuned-sufficiency-ukp
|
jakub014
| 2023-03-28T00:10:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-28T00:04:33Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ColD-Fusion-bert-base-uncased-itr23-seed0-finetuned-sufficiency-ukp
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. -->
# ColD-Fusion-bert-base-uncased-itr23-seed0-finetuned-sufficiency-ukp
This model is a fine-tuned version of [ibm/ColD-Fusion-bert-base-uncased-itr23-seed0](https://huggingface.co/ibm/ColD-Fusion-bert-base-uncased-itr23-seed0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5288
- Accuracy: 0.8786
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 52 | 0.3410 | 0.8544 |
| No log | 2.0 | 104 | 0.4002 | 0.8689 |
| No log | 3.0 | 156 | 0.5108 | 0.8544 |
| No log | 4.0 | 208 | 0.5288 | 0.8786 |
| No log | 5.0 | 260 | 0.5707 | 0.8738 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-10000
|
vocabtrimmer
| 2023-03-28T00:04:10Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T23:42:24Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa): `vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-10000`
This model is a trimmed version of [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-ruquad-qa | vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-10000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 365,068,288 |
| parameter_size_embedding | 512,057,344 | 20,488,192 |
| vocab_size | 250,028 | 10,004 |
| compression_rate_full | 100.0 | 59.76 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 10000 | 2 |
|
Kaludi/Customer-Support-Assistant
|
Kaludi
| 2023-03-27T23:52:10Z | 71 | 1 |
transformers
|
[
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T23:46:38Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Customer-Support-Assistant-V0
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Customer-Support-Assistant-V0
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1288
- Validation Loss: 1.1047
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.2620 | 4.1992 | 0 |
| 4.0866 | 2.4363 | 1 |
| 2.4098 | 1.5674 | 2 |
| 1.5377 | 1.2284 | 3 |
| 1.1288 | 1.1047 | 4 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
PhilSad/q-taxi-v3
|
PhilSad
| 2023-03-27T23:50:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T23:50:56Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="PhilSad/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
vocabtrimmer/xlm-roberta-base-trimmed-it-tweet-sentiment-it
|
vocabtrimmer
| 2023-03-27T23:50:29Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-06T18:56:55Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-it-tweet-sentiment-it`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (italian).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(italian).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 70.92 | 70.92 | 70.92 | 70.83 | 70.92 | 72.13 | 70.92 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-it-tweet-sentiment-it/raw/main/eval.json).
|
jakub014/ColD-Fusion-bert-base-uncased-itr23-seed0-finetuned-effectiveness-dagstuhl
|
jakub014
| 2023-03-27T23:50:10Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T23:48:35Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ColD-Fusion-bert-base-uncased-itr23-seed0-finetuned-effectiveness-dagstuhl
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. -->
# ColD-Fusion-bert-base-uncased-itr23-seed0-finetuned-effectiveness-dagstuhl
This model is a fine-tuned version of [ibm/ColD-Fusion-bert-base-uncased-itr23-seed0](https://huggingface.co/ibm/ColD-Fusion-bert-base-uncased-itr23-seed0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6548
- Accuracy: 0.6508
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 16 | 0.6548 | 0.6508 |
| No log | 2.0 | 32 | 0.6502 | 0.6190 |
| No log | 3.0 | 48 | 0.6451 | 0.6190 |
| No log | 4.0 | 64 | 0.6436 | 0.6349 |
| No log | 5.0 | 80 | 0.6482 | 0.6190 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-5000
|
vocabtrimmer
| 2023-03-27T23:41:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T23:19:47Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa): `vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-5000`
This model is a trimmed version of [lmqg/mbart-large-cc25-ruquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-ruquad-qa | vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-5000 |
|:---------------------------|:----------------------------------|:----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 359,949,312 |
| parameter_size_embedding | 512,057,344 | 10,250,240 |
| vocab_size | 250,028 | 5,005 |
| compression_rate_full | 100.0 | 58.93 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 5000 | 2 |
|
PhilSad/q-FrozenLake-v1-4x4-noSlippery-5
|
PhilSad
| 2023-03-27T23:41:08Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T23:41:06Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery-5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="PhilSad/q-FrozenLake-v1-4x4-noSlippery-5", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
huggingtweets/aeg0lius
|
huggingtweets
| 2023-03-27T23:40:28Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-27T23:40:20Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1637576782198231040/KejpruXv_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">owl</div>
<div style="text-align: center; font-size: 14px;">@aeg0lius</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from owl.
| Data | owl |
| --- | --- |
| Tweets downloaded | 774 |
| Retweets | 30 |
| Short tweets | 313 |
| Tweets kept | 431 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/b6wmilz9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aeg0lius's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ymvqtlc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ymvqtlc/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aeg0lius')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
fathyshalab/autotrain-dialogsumgerman-44305111787
|
fathyshalab
| 2023-03-27T23:35:58Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"summarization",
"de",
"dataset:fathyshalab/autotrain-data-dialogsumgerman",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-03-27T19:49:05Z |
---
tags:
- autotrain
- summarization
language:
- de
widget:
- text: "I love AutoTrain 🤗"
datasets:
- fathyshalab/autotrain-data-dialogsumgerman
co2_eq_emissions:
emissions: 86.21246024573398
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 44305111787
- CO2 Emissions (in grams): 86.2125
## Validation Metrics
- Loss: 1.069
- Rouge1: 33.702
- Rouge2: 13.478
- RougeL: 29.431
- RougeLsum: 30.710
- Gen Len: 18.952
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/fathyshalab/autotrain-dialogsumgerman-44305111787
```
|
huggingtweets/ordinarygamers
|
huggingtweets
| 2023-03-27T23:23:58Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-27T23:23:50Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1403763529036046336/NTGmV9nb_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mutahar</div>
<div style="text-align: center; font-size: 14px;">@ordinarygamers</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Mutahar.
| Data | Mutahar |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 87 |
| Short tweets | 306 |
| Tweets kept | 2853 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6ezo4cbs/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ordinarygamers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dmhrus4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dmhrus4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ordinarygamers')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
satyrical/dqnSpaceInvaders
|
satyrical
| 2023-03-27T23:17:10Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T23:16:30Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 310.50 +/- 122.83
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga satyrical -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga satyrical -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga satyrical
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BoschAI/dqn-SpaceInvadersNoFrameskip-v4
|
BoschAI
| 2023-03-27T23:07:41Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T23:06:56Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 543.50 +/- 234.19
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga BoschAI -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga BoschAI -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga BoschAI
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
aaronrmm/dqn-SpaceInvadersNoFrameskip-v4
|
aaronrmm
| 2023-03-27T23:04:51Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T21:31:02Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 670.00 +/- 278.20
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aaronrmm -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aaronrmm -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga aaronrmm
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 10000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
pinaggle/dqn-SpaceInvadersNoFrameskip-v4
|
pinaggle
| 2023-03-27T22:37:47Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T22:37:01Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 623.50 +/- 145.88
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pinaggle -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pinaggle -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pinaggle
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
tcvrishank/histo_train_swin
|
tcvrishank
| 2023-03-27T22:31:54Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-25T03:42:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: histo_train_swin
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9
---
<!-- 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. -->
# histo_train_swin
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2654
- Accuracy: 0.9
## 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0305 | 16.67 | 100 | 0.2654 | 0.9 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-30000
|
vocabtrimmer
| 2023-03-27T22:28:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T21:19:50Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-30000`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-esquad-qa | vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-30000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 385,548,288 |
| parameter_size_embedding | 512,057,344 | 61,448,192 |
| vocab_size | 250,028 | 30,004 |
| compression_rate_full | 100.0 | 63.12 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 30000 | 2 |
|
Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit
|
Muennighoff
| 2023-03-27T22:26:36Z | 466 | 23 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gptj",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: SGPT-5.8B-weightedmean-msmarco-specb-bitfit
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 69.22388059701493
- type: ap
value: 32.04724673950256
- type: f1
value: 63.25719825770428
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 71.26109999999998
- type: ap
value: 66.16336378255403
- type: f1
value: 70.89719145825303
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 39.19199999999999
- type: f1
value: 38.580766731113826
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 27.311999999999998
- type: map_at_10
value: 42.620000000000005
- type: map_at_100
value: 43.707
- type: map_at_1000
value: 43.714999999999996
- type: map_at_3
value: 37.624
- type: map_at_5
value: 40.498
- type: mrr_at_1
value: 27.667
- type: mrr_at_10
value: 42.737
- type: mrr_at_100
value: 43.823
- type: mrr_at_1000
value: 43.830999999999996
- type: mrr_at_3
value: 37.743
- type: mrr_at_5
value: 40.616
- type: ndcg_at_1
value: 27.311999999999998
- type: ndcg_at_10
value: 51.37500000000001
- type: ndcg_at_100
value: 55.778000000000006
- type: ndcg_at_1000
value: 55.96600000000001
- type: ndcg_at_3
value: 41.087
- type: ndcg_at_5
value: 46.269
- type: precision_at_1
value: 27.311999999999998
- type: precision_at_10
value: 7.945
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.046
- type: precision_at_5
value: 12.745000000000001
- type: recall_at_1
value: 27.311999999999998
- type: recall_at_10
value: 79.445
- type: recall_at_100
value: 98.151
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 51.13799999999999
- type: recall_at_5
value: 63.727000000000004
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 45.59037428592033
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 38.86371701986363
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 61.625568691427766
- type: mrr
value: 75.83256386580486
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 89.96074355094802
- type: cos_sim_spearman
value: 86.2501580394454
- type: euclidean_pearson
value: 82.18427440380462
- type: euclidean_spearman
value: 80.14760935017947
- type: manhattan_pearson
value: 82.24621578156392
- type: manhattan_spearman
value: 80.00363016590163
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 84.49350649350649
- type: f1
value: 84.4249343233736
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 36.551459722989385
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 33.69901851846774
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 30.499
- type: map_at_10
value: 41.208
- type: map_at_100
value: 42.638
- type: map_at_1000
value: 42.754
- type: map_at_3
value: 37.506
- type: map_at_5
value: 39.422000000000004
- type: mrr_at_1
value: 37.339
- type: mrr_at_10
value: 47.051
- type: mrr_at_100
value: 47.745
- type: mrr_at_1000
value: 47.786
- type: mrr_at_3
value: 44.086999999999996
- type: mrr_at_5
value: 45.711
- type: ndcg_at_1
value: 37.339
- type: ndcg_at_10
value: 47.666
- type: ndcg_at_100
value: 52.994
- type: ndcg_at_1000
value: 54.928999999999995
- type: ndcg_at_3
value: 41.982
- type: ndcg_at_5
value: 44.42
- type: precision_at_1
value: 37.339
- type: precision_at_10
value: 9.127
- type: precision_at_100
value: 1.4749999999999999
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.449000000000002
- type: recall_at_1
value: 30.499
- type: recall_at_10
value: 60.328
- type: recall_at_100
value: 82.57900000000001
- type: recall_at_1000
value: 95.074
- type: recall_at_3
value: 44.17
- type: recall_at_5
value: 50.94
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 30.613
- type: map_at_10
value: 40.781
- type: map_at_100
value: 42.018
- type: map_at_1000
value: 42.132999999999996
- type: map_at_3
value: 37.816
- type: map_at_5
value: 39.389
- type: mrr_at_1
value: 38.408
- type: mrr_at_10
value: 46.631
- type: mrr_at_100
value: 47.332
- type: mrr_at_1000
value: 47.368
- type: mrr_at_3
value: 44.384
- type: mrr_at_5
value: 45.661
- type: ndcg_at_1
value: 38.408
- type: ndcg_at_10
value: 46.379999999999995
- type: ndcg_at_100
value: 50.81
- type: ndcg_at_1000
value: 52.663000000000004
- type: ndcg_at_3
value: 42.18
- type: ndcg_at_5
value: 43.974000000000004
- type: precision_at_1
value: 38.408
- type: precision_at_10
value: 8.656
- type: precision_at_100
value: 1.3860000000000001
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 20.276
- type: precision_at_5
value: 14.241999999999999
- type: recall_at_1
value: 30.613
- type: recall_at_10
value: 56.44
- type: recall_at_100
value: 75.044
- type: recall_at_1000
value: 86.426
- type: recall_at_3
value: 43.766
- type: recall_at_5
value: 48.998000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 37.370999999999995
- type: map_at_10
value: 49.718
- type: map_at_100
value: 50.737
- type: map_at_1000
value: 50.79
- type: map_at_3
value: 46.231
- type: map_at_5
value: 48.329
- type: mrr_at_1
value: 42.884
- type: mrr_at_10
value: 53.176
- type: mrr_at_100
value: 53.81700000000001
- type: mrr_at_1000
value: 53.845
- type: mrr_at_3
value: 50.199000000000005
- type: mrr_at_5
value: 52.129999999999995
- type: ndcg_at_1
value: 42.884
- type: ndcg_at_10
value: 55.826
- type: ndcg_at_100
value: 59.93000000000001
- type: ndcg_at_1000
value: 61.013
- type: ndcg_at_3
value: 49.764
- type: ndcg_at_5
value: 53.025999999999996
- type: precision_at_1
value: 42.884
- type: precision_at_10
value: 9.046999999999999
- type: precision_at_100
value: 1.212
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.131999999999998
- type: precision_at_5
value: 15.524
- type: recall_at_1
value: 37.370999999999995
- type: recall_at_10
value: 70.482
- type: recall_at_100
value: 88.425
- type: recall_at_1000
value: 96.03399999999999
- type: recall_at_3
value: 54.43
- type: recall_at_5
value: 62.327999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 22.875999999999998
- type: map_at_10
value: 31.715
- type: map_at_100
value: 32.847
- type: map_at_1000
value: 32.922000000000004
- type: map_at_3
value: 29.049999999999997
- type: map_at_5
value: 30.396
- type: mrr_at_1
value: 24.52
- type: mrr_at_10
value: 33.497
- type: mrr_at_100
value: 34.455000000000005
- type: mrr_at_1000
value: 34.510000000000005
- type: mrr_at_3
value: 30.791
- type: mrr_at_5
value: 32.175
- type: ndcg_at_1
value: 24.52
- type: ndcg_at_10
value: 36.95
- type: ndcg_at_100
value: 42.238
- type: ndcg_at_1000
value: 44.147999999999996
- type: ndcg_at_3
value: 31.435000000000002
- type: ndcg_at_5
value: 33.839000000000006
- type: precision_at_1
value: 24.52
- type: precision_at_10
value: 5.9319999999999995
- type: precision_at_100
value: 0.901
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.446
- type: precision_at_5
value: 9.469
- type: recall_at_1
value: 22.875999999999998
- type: recall_at_10
value: 51.38
- type: recall_at_100
value: 75.31099999999999
- type: recall_at_1000
value: 89.718
- type: recall_at_3
value: 36.26
- type: recall_at_5
value: 42.248999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 14.984
- type: map_at_10
value: 23.457
- type: map_at_100
value: 24.723
- type: map_at_1000
value: 24.846
- type: map_at_3
value: 20.873
- type: map_at_5
value: 22.357
- type: mrr_at_1
value: 18.159
- type: mrr_at_10
value: 27.431
- type: mrr_at_100
value: 28.449
- type: mrr_at_1000
value: 28.52
- type: mrr_at_3
value: 24.979000000000003
- type: mrr_at_5
value: 26.447
- type: ndcg_at_1
value: 18.159
- type: ndcg_at_10
value: 28.627999999999997
- type: ndcg_at_100
value: 34.741
- type: ndcg_at_1000
value: 37.516
- type: ndcg_at_3
value: 23.902
- type: ndcg_at_5
value: 26.294
- type: precision_at_1
value: 18.159
- type: precision_at_10
value: 5.485
- type: precision_at_100
value: 0.985
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 11.774
- type: precision_at_5
value: 8.731
- type: recall_at_1
value: 14.984
- type: recall_at_10
value: 40.198
- type: recall_at_100
value: 67.11500000000001
- type: recall_at_1000
value: 86.497
- type: recall_at_3
value: 27.639000000000003
- type: recall_at_5
value: 33.595000000000006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 29.067
- type: map_at_10
value: 39.457
- type: map_at_100
value: 40.83
- type: map_at_1000
value: 40.94
- type: map_at_3
value: 35.995
- type: map_at_5
value: 38.159
- type: mrr_at_1
value: 34.937000000000005
- type: mrr_at_10
value: 44.755
- type: mrr_at_100
value: 45.549
- type: mrr_at_1000
value: 45.589
- type: mrr_at_3
value: 41.947
- type: mrr_at_5
value: 43.733
- type: ndcg_at_1
value: 34.937000000000005
- type: ndcg_at_10
value: 45.573
- type: ndcg_at_100
value: 51.266999999999996
- type: ndcg_at_1000
value: 53.184
- type: ndcg_at_3
value: 39.961999999999996
- type: ndcg_at_5
value: 43.02
- type: precision_at_1
value: 34.937000000000005
- type: precision_at_10
value: 8.296000000000001
- type: precision_at_100
value: 1.32
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 18.8
- type: precision_at_5
value: 13.763
- type: recall_at_1
value: 29.067
- type: recall_at_10
value: 58.298
- type: recall_at_100
value: 82.25099999999999
- type: recall_at_1000
value: 94.476
- type: recall_at_3
value: 42.984
- type: recall_at_5
value: 50.658
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 25.985999999999997
- type: map_at_10
value: 35.746
- type: map_at_100
value: 37.067
- type: map_at_1000
value: 37.191
- type: map_at_3
value: 32.599000000000004
- type: map_at_5
value: 34.239000000000004
- type: mrr_at_1
value: 31.735000000000003
- type: mrr_at_10
value: 40.515
- type: mrr_at_100
value: 41.459
- type: mrr_at_1000
value: 41.516
- type: mrr_at_3
value: 37.938
- type: mrr_at_5
value: 39.25
- type: ndcg_at_1
value: 31.735000000000003
- type: ndcg_at_10
value: 41.484
- type: ndcg_at_100
value: 47.047
- type: ndcg_at_1000
value: 49.427
- type: ndcg_at_3
value: 36.254999999999995
- type: ndcg_at_5
value: 38.375
- type: precision_at_1
value: 31.735000000000003
- type: precision_at_10
value: 7.66
- type: precision_at_100
value: 1.234
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 17.427999999999997
- type: precision_at_5
value: 12.328999999999999
- type: recall_at_1
value: 25.985999999999997
- type: recall_at_10
value: 53.761
- type: recall_at_100
value: 77.149
- type: recall_at_1000
value: 93.342
- type: recall_at_3
value: 39.068000000000005
- type: recall_at_5
value: 44.693
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 24.949749999999998
- type: map_at_10
value: 34.04991666666667
- type: map_at_100
value: 35.26825
- type: map_at_1000
value: 35.38316666666667
- type: map_at_3
value: 31.181333333333335
- type: map_at_5
value: 32.77391666666667
- type: mrr_at_1
value: 29.402833333333334
- type: mrr_at_10
value: 38.01633333333333
- type: mrr_at_100
value: 38.88033333333334
- type: mrr_at_1000
value: 38.938500000000005
- type: mrr_at_3
value: 35.5175
- type: mrr_at_5
value: 36.93808333333333
- type: ndcg_at_1
value: 29.402833333333334
- type: ndcg_at_10
value: 39.403166666666664
- type: ndcg_at_100
value: 44.66408333333333
- type: ndcg_at_1000
value: 46.96283333333333
- type: ndcg_at_3
value: 34.46633333333334
- type: ndcg_at_5
value: 36.78441666666667
- type: precision_at_1
value: 29.402833333333334
- type: precision_at_10
value: 6.965833333333333
- type: precision_at_100
value: 1.1330833333333334
- type: precision_at_1000
value: 0.15158333333333335
- type: precision_at_3
value: 15.886666666666665
- type: precision_at_5
value: 11.360416666666667
- type: recall_at_1
value: 24.949749999999998
- type: recall_at_10
value: 51.29325
- type: recall_at_100
value: 74.3695
- type: recall_at_1000
value: 90.31299999999999
- type: recall_at_3
value: 37.580083333333334
- type: recall_at_5
value: 43.529666666666664
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 22.081999999999997
- type: map_at_10
value: 29.215999999999998
- type: map_at_100
value: 30.163
- type: map_at_1000
value: 30.269000000000002
- type: map_at_3
value: 26.942
- type: map_at_5
value: 28.236
- type: mrr_at_1
value: 24.847
- type: mrr_at_10
value: 31.918999999999997
- type: mrr_at_100
value: 32.817
- type: mrr_at_1000
value: 32.897
- type: mrr_at_3
value: 29.831000000000003
- type: mrr_at_5
value: 31.019999999999996
- type: ndcg_at_1
value: 24.847
- type: ndcg_at_10
value: 33.4
- type: ndcg_at_100
value: 38.354
- type: ndcg_at_1000
value: 41.045
- type: ndcg_at_3
value: 29.236
- type: ndcg_at_5
value: 31.258000000000003
- type: precision_at_1
value: 24.847
- type: precision_at_10
value: 5.353
- type: precision_at_100
value: 0.853
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 12.679000000000002
- type: precision_at_5
value: 8.988
- type: recall_at_1
value: 22.081999999999997
- type: recall_at_10
value: 43.505
- type: recall_at_100
value: 66.45400000000001
- type: recall_at_1000
value: 86.378
- type: recall_at_3
value: 32.163000000000004
- type: recall_at_5
value: 37.059999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 15.540000000000001
- type: map_at_10
value: 22.362000000000002
- type: map_at_100
value: 23.435
- type: map_at_1000
value: 23.564
- type: map_at_3
value: 20.143
- type: map_at_5
value: 21.324
- type: mrr_at_1
value: 18.892
- type: mrr_at_10
value: 25.942999999999998
- type: mrr_at_100
value: 26.883000000000003
- type: mrr_at_1000
value: 26.968999999999998
- type: mrr_at_3
value: 23.727
- type: mrr_at_5
value: 24.923000000000002
- type: ndcg_at_1
value: 18.892
- type: ndcg_at_10
value: 26.811
- type: ndcg_at_100
value: 32.066
- type: ndcg_at_1000
value: 35.166
- type: ndcg_at_3
value: 22.706
- type: ndcg_at_5
value: 24.508
- type: precision_at_1
value: 18.892
- type: precision_at_10
value: 4.942
- type: precision_at_100
value: 0.878
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 10.748000000000001
- type: precision_at_5
value: 7.784000000000001
- type: recall_at_1
value: 15.540000000000001
- type: recall_at_10
value: 36.742999999999995
- type: recall_at_100
value: 60.525
- type: recall_at_1000
value: 82.57600000000001
- type: recall_at_3
value: 25.252000000000002
- type: recall_at_5
value: 29.872
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 24.453
- type: map_at_10
value: 33.363
- type: map_at_100
value: 34.579
- type: map_at_1000
value: 34.686
- type: map_at_3
value: 30.583
- type: map_at_5
value: 32.118
- type: mrr_at_1
value: 28.918
- type: mrr_at_10
value: 37.675
- type: mrr_at_100
value: 38.567
- type: mrr_at_1000
value: 38.632
- type: mrr_at_3
value: 35.260999999999996
- type: mrr_at_5
value: 36.576
- type: ndcg_at_1
value: 28.918
- type: ndcg_at_10
value: 38.736
- type: ndcg_at_100
value: 44.261
- type: ndcg_at_1000
value: 46.72
- type: ndcg_at_3
value: 33.81
- type: ndcg_at_5
value: 36.009
- type: precision_at_1
value: 28.918
- type: precision_at_10
value: 6.586
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.360999999999999
- type: precision_at_5
value: 10.857999999999999
- type: recall_at_1
value: 24.453
- type: recall_at_10
value: 50.885999999999996
- type: recall_at_100
value: 75.03
- type: recall_at_1000
value: 92.123
- type: recall_at_3
value: 37.138
- type: recall_at_5
value: 42.864999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 24.57
- type: map_at_10
value: 33.672000000000004
- type: map_at_100
value: 35.244
- type: map_at_1000
value: 35.467
- type: map_at_3
value: 30.712
- type: map_at_5
value: 32.383
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 38.344
- type: mrr_at_100
value: 39.219
- type: mrr_at_1000
value: 39.282000000000004
- type: mrr_at_3
value: 35.771
- type: mrr_at_5
value: 37.273
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 39.567
- type: ndcg_at_100
value: 45.097
- type: ndcg_at_1000
value: 47.923
- type: ndcg_at_3
value: 34.768
- type: ndcg_at_5
value: 37.122
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 7.5889999999999995
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 16.337
- type: precision_at_5
value: 12.055
- type: recall_at_1
value: 24.57
- type: recall_at_10
value: 51.00900000000001
- type: recall_at_100
value: 75.423
- type: recall_at_1000
value: 93.671
- type: recall_at_3
value: 36.925999999999995
- type: recall_at_5
value: 43.245
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 21.356
- type: map_at_10
value: 27.904
- type: map_at_100
value: 28.938000000000002
- type: map_at_1000
value: 29.036
- type: map_at_3
value: 25.726
- type: map_at_5
value: 26.935
- type: mrr_at_1
value: 22.551
- type: mrr_at_10
value: 29.259
- type: mrr_at_100
value: 30.272
- type: mrr_at_1000
value: 30.348000000000003
- type: mrr_at_3
value: 27.295
- type: mrr_at_5
value: 28.358
- type: ndcg_at_1
value: 22.551
- type: ndcg_at_10
value: 31.817
- type: ndcg_at_100
value: 37.164
- type: ndcg_at_1000
value: 39.82
- type: ndcg_at_3
value: 27.595999999999997
- type: ndcg_at_5
value: 29.568
- type: precision_at_1
value: 22.551
- type: precision_at_10
value: 4.917
- type: precision_at_100
value: 0.828
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 11.583
- type: precision_at_5
value: 8.133
- type: recall_at_1
value: 21.356
- type: recall_at_10
value: 42.489
- type: recall_at_100
value: 67.128
- type: recall_at_1000
value: 87.441
- type: recall_at_3
value: 31.165
- type: recall_at_5
value: 35.853
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 12.306000000000001
- type: map_at_10
value: 21.523
- type: map_at_100
value: 23.358
- type: map_at_1000
value: 23.541
- type: map_at_3
value: 17.809
- type: map_at_5
value: 19.631
- type: mrr_at_1
value: 27.948
- type: mrr_at_10
value: 40.355000000000004
- type: mrr_at_100
value: 41.166000000000004
- type: mrr_at_1000
value: 41.203
- type: mrr_at_3
value: 36.819
- type: mrr_at_5
value: 38.958999999999996
- type: ndcg_at_1
value: 27.948
- type: ndcg_at_10
value: 30.462
- type: ndcg_at_100
value: 37.473
- type: ndcg_at_1000
value: 40.717999999999996
- type: ndcg_at_3
value: 24.646
- type: ndcg_at_5
value: 26.642
- type: precision_at_1
value: 27.948
- type: precision_at_10
value: 9.648
- type: precision_at_100
value: 1.7239999999999998
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 18.48
- type: precision_at_5
value: 14.293
- type: recall_at_1
value: 12.306000000000001
- type: recall_at_10
value: 37.181
- type: recall_at_100
value: 61.148
- type: recall_at_1000
value: 79.401
- type: recall_at_3
value: 22.883
- type: recall_at_5
value: 28.59
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 9.357
- type: map_at_10
value: 18.849
- type: map_at_100
value: 25.369000000000003
- type: map_at_1000
value: 26.950000000000003
- type: map_at_3
value: 13.625000000000002
- type: map_at_5
value: 15.956999999999999
- type: mrr_at_1
value: 67.75
- type: mrr_at_10
value: 74.734
- type: mrr_at_100
value: 75.1
- type: mrr_at_1000
value: 75.10900000000001
- type: mrr_at_3
value: 73.542
- type: mrr_at_5
value: 74.167
- type: ndcg_at_1
value: 55.375
- type: ndcg_at_10
value: 39.873999999999995
- type: ndcg_at_100
value: 43.098
- type: ndcg_at_1000
value: 50.69200000000001
- type: ndcg_at_3
value: 44.856
- type: ndcg_at_5
value: 42.138999999999996
- type: precision_at_1
value: 67.75
- type: precision_at_10
value: 31.1
- type: precision_at_100
value: 9.303
- type: precision_at_1000
value: 2.0060000000000002
- type: precision_at_3
value: 48.25
- type: precision_at_5
value: 40.949999999999996
- type: recall_at_1
value: 9.357
- type: recall_at_10
value: 23.832
- type: recall_at_100
value: 47.906
- type: recall_at_1000
value: 71.309
- type: recall_at_3
value: 14.512
- type: recall_at_5
value: 18.3
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 49.655
- type: f1
value: 45.51976190938951
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 62.739999999999995
- type: map_at_10
value: 73.07000000000001
- type: map_at_100
value: 73.398
- type: map_at_1000
value: 73.41
- type: map_at_3
value: 71.33800000000001
- type: map_at_5
value: 72.423
- type: mrr_at_1
value: 67.777
- type: mrr_at_10
value: 77.873
- type: mrr_at_100
value: 78.091
- type: mrr_at_1000
value: 78.094
- type: mrr_at_3
value: 76.375
- type: mrr_at_5
value: 77.316
- type: ndcg_at_1
value: 67.777
- type: ndcg_at_10
value: 78.24
- type: ndcg_at_100
value: 79.557
- type: ndcg_at_1000
value: 79.814
- type: ndcg_at_3
value: 75.125
- type: ndcg_at_5
value: 76.834
- type: precision_at_1
value: 67.777
- type: precision_at_10
value: 9.832
- type: precision_at_100
value: 1.061
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 29.433
- type: precision_at_5
value: 18.665000000000003
- type: recall_at_1
value: 62.739999999999995
- type: recall_at_10
value: 89.505
- type: recall_at_100
value: 95.102
- type: recall_at_1000
value: 96.825
- type: recall_at_3
value: 81.028
- type: recall_at_5
value: 85.28099999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 18.467
- type: map_at_10
value: 30.020999999999997
- type: map_at_100
value: 31.739
- type: map_at_1000
value: 31.934
- type: map_at_3
value: 26.003
- type: map_at_5
value: 28.338
- type: mrr_at_1
value: 35.339999999999996
- type: mrr_at_10
value: 44.108999999999995
- type: mrr_at_100
value: 44.993
- type: mrr_at_1000
value: 45.042
- type: mrr_at_3
value: 41.667
- type: mrr_at_5
value: 43.14
- type: ndcg_at_1
value: 35.339999999999996
- type: ndcg_at_10
value: 37.202
- type: ndcg_at_100
value: 43.852999999999994
- type: ndcg_at_1000
value: 47.235
- type: ndcg_at_3
value: 33.5
- type: ndcg_at_5
value: 34.985
- type: precision_at_1
value: 35.339999999999996
- type: precision_at_10
value: 10.247
- type: precision_at_100
value: 1.7149999999999999
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 22.222
- type: precision_at_5
value: 16.573999999999998
- type: recall_at_1
value: 18.467
- type: recall_at_10
value: 44.080999999999996
- type: recall_at_100
value: 68.72200000000001
- type: recall_at_1000
value: 89.087
- type: recall_at_3
value: 30.567
- type: recall_at_5
value: 36.982
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 35.726
- type: map_at_10
value: 50.207
- type: map_at_100
value: 51.05499999999999
- type: map_at_1000
value: 51.12799999999999
- type: map_at_3
value: 47.576
- type: map_at_5
value: 49.172
- type: mrr_at_1
value: 71.452
- type: mrr_at_10
value: 77.41900000000001
- type: mrr_at_100
value: 77.711
- type: mrr_at_1000
value: 77.723
- type: mrr_at_3
value: 76.39399999999999
- type: mrr_at_5
value: 77.00099999999999
- type: ndcg_at_1
value: 71.452
- type: ndcg_at_10
value: 59.260999999999996
- type: ndcg_at_100
value: 62.424
- type: ndcg_at_1000
value: 63.951
- type: ndcg_at_3
value: 55.327000000000005
- type: ndcg_at_5
value: 57.416999999999994
- type: precision_at_1
value: 71.452
- type: precision_at_10
value: 12.061
- type: precision_at_100
value: 1.455
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 34.36
- type: precision_at_5
value: 22.266
- type: recall_at_1
value: 35.726
- type: recall_at_10
value: 60.304
- type: recall_at_100
value: 72.75500000000001
- type: recall_at_1000
value: 82.978
- type: recall_at_3
value: 51.54
- type: recall_at_5
value: 55.665
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 66.63759999999999
- type: ap
value: 61.48938261286748
- type: f1
value: 66.35089269264965
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 20.842
- type: map_at_10
value: 32.992
- type: map_at_100
value: 34.236
- type: map_at_1000
value: 34.286
- type: map_at_3
value: 29.049000000000003
- type: map_at_5
value: 31.391999999999996
- type: mrr_at_1
value: 21.375
- type: mrr_at_10
value: 33.581
- type: mrr_at_100
value: 34.760000000000005
- type: mrr_at_1000
value: 34.803
- type: mrr_at_3
value: 29.704000000000004
- type: mrr_at_5
value: 32.015
- type: ndcg_at_1
value: 21.375
- type: ndcg_at_10
value: 39.905
- type: ndcg_at_100
value: 45.843
- type: ndcg_at_1000
value: 47.083999999999996
- type: ndcg_at_3
value: 31.918999999999997
- type: ndcg_at_5
value: 36.107
- type: precision_at_1
value: 21.375
- type: precision_at_10
value: 6.393
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.663
- type: precision_at_5
value: 10.324
- type: recall_at_1
value: 20.842
- type: recall_at_10
value: 61.17
- type: recall_at_100
value: 88.518
- type: recall_at_1000
value: 97.993
- type: recall_at_3
value: 39.571
- type: recall_at_5
value: 49.653999999999996
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 93.46557227542178
- type: f1
value: 92.87345917772146
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 72.42134062927497
- type: f1
value: 55.03624810959269
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 70.3866845998655
- type: f1
value: 68.9674519872921
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.27774041694687
- type: f1
value: 76.72936190462792
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 31.511745925773337
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 28.764235987575365
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.29353136386601
- type: mrr
value: 33.536774455851685
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 5.702
- type: map_at_10
value: 13.642000000000001
- type: map_at_100
value: 17.503
- type: map_at_1000
value: 19.126
- type: map_at_3
value: 9.748
- type: map_at_5
value: 11.642
- type: mrr_at_1
value: 45.82
- type: mrr_at_10
value: 54.821
- type: mrr_at_100
value: 55.422000000000004
- type: mrr_at_1000
value: 55.452999999999996
- type: mrr_at_3
value: 52.373999999999995
- type: mrr_at_5
value: 53.937000000000005
- type: ndcg_at_1
value: 44.272
- type: ndcg_at_10
value: 36.213
- type: ndcg_at_100
value: 33.829
- type: ndcg_at_1000
value: 42.557
- type: ndcg_at_3
value: 40.814
- type: ndcg_at_5
value: 39.562000000000005
- type: precision_at_1
value: 45.511
- type: precision_at_10
value: 27.214
- type: precision_at_100
value: 8.941
- type: precision_at_1000
value: 2.1870000000000003
- type: precision_at_3
value: 37.874
- type: precision_at_5
value: 34.489
- type: recall_at_1
value: 5.702
- type: recall_at_10
value: 17.638
- type: recall_at_100
value: 34.419
- type: recall_at_1000
value: 66.41
- type: recall_at_3
value: 10.914
- type: recall_at_5
value: 14.032
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 30.567
- type: map_at_10
value: 45.01
- type: map_at_100
value: 46.091
- type: map_at_1000
value: 46.126
- type: map_at_3
value: 40.897
- type: map_at_5
value: 43.301
- type: mrr_at_1
value: 34.56
- type: mrr_at_10
value: 47.725
- type: mrr_at_100
value: 48.548
- type: mrr_at_1000
value: 48.571999999999996
- type: mrr_at_3
value: 44.361
- type: mrr_at_5
value: 46.351
- type: ndcg_at_1
value: 34.531
- type: ndcg_at_10
value: 52.410000000000004
- type: ndcg_at_100
value: 56.999
- type: ndcg_at_1000
value: 57.830999999999996
- type: ndcg_at_3
value: 44.734
- type: ndcg_at_5
value: 48.701
- type: precision_at_1
value: 34.531
- type: precision_at_10
value: 8.612
- type: precision_at_100
value: 1.118
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 20.307
- type: precision_at_5
value: 14.519000000000002
- type: recall_at_1
value: 30.567
- type: recall_at_10
value: 72.238
- type: recall_at_100
value: 92.154
- type: recall_at_1000
value: 98.375
- type: recall_at_3
value: 52.437999999999995
- type: recall_at_5
value: 61.516999999999996
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 65.98
- type: map_at_10
value: 80.05600000000001
- type: map_at_100
value: 80.76299999999999
- type: map_at_1000
value: 80.786
- type: map_at_3
value: 76.848
- type: map_at_5
value: 78.854
- type: mrr_at_1
value: 75.86
- type: mrr_at_10
value: 83.397
- type: mrr_at_100
value: 83.555
- type: mrr_at_1000
value: 83.557
- type: mrr_at_3
value: 82.033
- type: mrr_at_5
value: 82.97
- type: ndcg_at_1
value: 75.88000000000001
- type: ndcg_at_10
value: 84.58099999999999
- type: ndcg_at_100
value: 86.151
- type: ndcg_at_1000
value: 86.315
- type: ndcg_at_3
value: 80.902
- type: ndcg_at_5
value: 82.953
- type: precision_at_1
value: 75.88000000000001
- type: precision_at_10
value: 12.986
- type: precision_at_100
value: 1.5110000000000001
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.382999999999996
- type: precision_at_5
value: 23.555999999999997
- type: recall_at_1
value: 65.98
- type: recall_at_10
value: 93.716
- type: recall_at_100
value: 99.21799999999999
- type: recall_at_1000
value: 99.97
- type: recall_at_3
value: 83.551
- type: recall_at_5
value: 88.998
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 40.45148482612238
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 55.749490673039126
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 4.903
- type: map_at_10
value: 11.926
- type: map_at_100
value: 13.916999999999998
- type: map_at_1000
value: 14.215
- type: map_at_3
value: 8.799999999999999
- type: map_at_5
value: 10.360999999999999
- type: mrr_at_1
value: 24.099999999999998
- type: mrr_at_10
value: 34.482
- type: mrr_at_100
value: 35.565999999999995
- type: mrr_at_1000
value: 35.619
- type: mrr_at_3
value: 31.433
- type: mrr_at_5
value: 33.243
- type: ndcg_at_1
value: 24.099999999999998
- type: ndcg_at_10
value: 19.872999999999998
- type: ndcg_at_100
value: 27.606
- type: ndcg_at_1000
value: 32.811
- type: ndcg_at_3
value: 19.497999999999998
- type: ndcg_at_5
value: 16.813
- type: precision_at_1
value: 24.099999999999998
- type: precision_at_10
value: 10.08
- type: precision_at_100
value: 2.122
- type: precision_at_1000
value: 0.337
- type: precision_at_3
value: 18.2
- type: precision_at_5
value: 14.62
- type: recall_at_1
value: 4.903
- type: recall_at_10
value: 20.438000000000002
- type: recall_at_100
value: 43.043
- type: recall_at_1000
value: 68.41000000000001
- type: recall_at_3
value: 11.068
- type: recall_at_5
value: 14.818000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 78.58086597995997
- type: cos_sim_spearman
value: 69.63214182814991
- type: euclidean_pearson
value: 72.76175489042691
- type: euclidean_spearman
value: 67.84965161872971
- type: manhattan_pearson
value: 72.73812689782592
- type: manhattan_spearman
value: 67.83610439531277
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 75.13970861325006
- type: cos_sim_spearman
value: 67.5020551515597
- type: euclidean_pearson
value: 66.33415412418276
- type: euclidean_spearman
value: 66.82145056673268
- type: manhattan_pearson
value: 66.55489484006415
- type: manhattan_spearman
value: 66.95147433279057
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 78.85850536483447
- type: cos_sim_spearman
value: 79.1633350177206
- type: euclidean_pearson
value: 72.74090561408477
- type: euclidean_spearman
value: 73.57374448302961
- type: manhattan_pearson
value: 72.92980654233226
- type: manhattan_spearman
value: 73.72777155112588
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 79.51125593897028
- type: cos_sim_spearman
value: 74.46048326701329
- type: euclidean_pearson
value: 70.87726087052985
- type: euclidean_spearman
value: 67.7721470654411
- type: manhattan_pearson
value: 71.05892792135637
- type: manhattan_spearman
value: 67.93472619779037
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 83.8299348880489
- type: cos_sim_spearman
value: 84.47194637929275
- type: euclidean_pearson
value: 78.68768462480418
- type: euclidean_spearman
value: 79.80526323901917
- type: manhattan_pearson
value: 78.6810718151946
- type: manhattan_spearman
value: 79.7820584821254
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 79.99206664843005
- type: cos_sim_spearman
value: 80.96089203722137
- type: euclidean_pearson
value: 71.31216213716365
- type: euclidean_spearman
value: 71.45258140049407
- type: manhattan_pearson
value: 71.26140340402836
- type: manhattan_spearman
value: 71.3896894666943
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 87.35697089594868
- type: cos_sim_spearman
value: 87.78202647220289
- type: euclidean_pearson
value: 84.20969668786667
- type: euclidean_spearman
value: 83.91876425459982
- type: manhattan_pearson
value: 84.24429755612542
- type: manhattan_spearman
value: 83.98826315103398
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 69.06962775868384
- type: cos_sim_spearman
value: 69.34889515492327
- type: euclidean_pearson
value: 69.28108180412313
- type: euclidean_spearman
value: 69.6437114853659
- type: manhattan_pearson
value: 69.39974983734993
- type: manhattan_spearman
value: 69.69057284482079
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 82.42553734213958
- type: cos_sim_spearman
value: 81.38977341532744
- type: euclidean_pearson
value: 76.47494587945522
- type: euclidean_spearman
value: 75.92794860531089
- type: manhattan_pearson
value: 76.4768777169467
- type: manhattan_spearman
value: 75.9252673228599
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 80.78825425914722
- type: mrr
value: 94.60017197762296
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 60.633
- type: map_at_10
value: 70.197
- type: map_at_100
value: 70.758
- type: map_at_1000
value: 70.765
- type: map_at_3
value: 67.082
- type: map_at_5
value: 69.209
- type: mrr_at_1
value: 63.333
- type: mrr_at_10
value: 71.17
- type: mrr_at_100
value: 71.626
- type: mrr_at_1000
value: 71.633
- type: mrr_at_3
value: 68.833
- type: mrr_at_5
value: 70.6
- type: ndcg_at_1
value: 63.333
- type: ndcg_at_10
value: 74.697
- type: ndcg_at_100
value: 76.986
- type: ndcg_at_1000
value: 77.225
- type: ndcg_at_3
value: 69.527
- type: ndcg_at_5
value: 72.816
- type: precision_at_1
value: 63.333
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.103
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.889000000000003
- type: precision_at_5
value: 18.2
- type: recall_at_1
value: 60.633
- type: recall_at_10
value: 87.36699999999999
- type: recall_at_100
value: 97.333
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 73.656
- type: recall_at_5
value: 82.083
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.76633663366337
- type: cos_sim_ap
value: 93.84024096781063
- type: cos_sim_f1
value: 88.08080808080808
- type: cos_sim_precision
value: 88.9795918367347
- type: cos_sim_recall
value: 87.2
- type: dot_accuracy
value: 99.46336633663367
- type: dot_ap
value: 75.78127156965245
- type: dot_f1
value: 71.41403865717193
- type: dot_precision
value: 72.67080745341616
- type: dot_recall
value: 70.19999999999999
- type: euclidean_accuracy
value: 99.67524752475248
- type: euclidean_ap
value: 88.61274955249769
- type: euclidean_f1
value: 82.30852211434735
- type: euclidean_precision
value: 89.34426229508196
- type: euclidean_recall
value: 76.3
- type: manhattan_accuracy
value: 99.67722772277227
- type: manhattan_ap
value: 88.77516158012779
- type: manhattan_f1
value: 82.36536430834212
- type: manhattan_precision
value: 87.24832214765101
- type: manhattan_recall
value: 78.0
- type: max_accuracy
value: 99.76633663366337
- type: max_ap
value: 93.84024096781063
- type: max_f1
value: 88.08080808080808
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 59.20812266121527
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 33.954248554638056
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 51.52800990025549
- type: mrr
value: 52.360394915541974
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 30.737881131277356
- type: cos_sim_spearman
value: 31.45979323917254
- type: dot_pearson
value: 26.24686017962023
- type: dot_spearman
value: 25.006732878791743
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.253
- type: map_at_10
value: 2.1399999999999997
- type: map_at_100
value: 12.873000000000001
- type: map_at_1000
value: 31.002000000000002
- type: map_at_3
value: 0.711
- type: map_at_5
value: 1.125
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 98.0
- type: mrr_at_100
value: 98.0
- type: mrr_at_1000
value: 98.0
- type: mrr_at_3
value: 98.0
- type: mrr_at_5
value: 98.0
- type: ndcg_at_1
value: 94.0
- type: ndcg_at_10
value: 84.881
- type: ndcg_at_100
value: 64.694
- type: ndcg_at_1000
value: 56.85
- type: ndcg_at_3
value: 90.061
- type: ndcg_at_5
value: 87.155
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 88.8
- type: precision_at_100
value: 65.7
- type: precision_at_1000
value: 25.080000000000002
- type: precision_at_3
value: 92.667
- type: precision_at_5
value: 90.0
- type: recall_at_1
value: 0.253
- type: recall_at_10
value: 2.292
- type: recall_at_100
value: 15.78
- type: recall_at_1000
value: 53.015
- type: recall_at_3
value: 0.7270000000000001
- type: recall_at_5
value: 1.162
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 2.116
- type: map_at_10
value: 9.625
- type: map_at_100
value: 15.641
- type: map_at_1000
value: 17.127
- type: map_at_3
value: 4.316
- type: map_at_5
value: 6.208
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 48.083999999999996
- type: mrr_at_100
value: 48.631
- type: mrr_at_1000
value: 48.649
- type: mrr_at_3
value: 42.857
- type: mrr_at_5
value: 46.224
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 25.430999999999997
- type: ndcg_at_100
value: 36.344
- type: ndcg_at_1000
value: 47.676
- type: ndcg_at_3
value: 26.144000000000002
- type: ndcg_at_5
value: 26.304
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 24.082
- type: precision_at_100
value: 7.714
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 26.939
- type: recall_at_1
value: 2.116
- type: recall_at_10
value: 16.794
- type: recall_at_100
value: 47.452
- type: recall_at_1000
value: 82.312
- type: recall_at_3
value: 5.306
- type: recall_at_5
value: 9.306000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 67.709
- type: ap
value: 13.541535578501716
- type: f1
value: 52.569619919446794
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 56.850594227504246
- type: f1
value: 57.233377364910574
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 39.463722986090474
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.09131549144662
- type: cos_sim_ap
value: 66.86677647503386
- type: cos_sim_f1
value: 62.94631710362049
- type: cos_sim_precision
value: 59.73933649289099
- type: cos_sim_recall
value: 66.51715039577837
- type: dot_accuracy
value: 80.27656911247541
- type: dot_ap
value: 54.291720398612085
- type: dot_f1
value: 54.77150537634409
- type: dot_precision
value: 47.58660957571039
- type: dot_recall
value: 64.5118733509235
- type: euclidean_accuracy
value: 82.76211480002385
- type: euclidean_ap
value: 62.430397690753296
- type: euclidean_f1
value: 59.191590539356774
- type: euclidean_precision
value: 56.296119971435374
- type: euclidean_recall
value: 62.401055408970976
- type: manhattan_accuracy
value: 82.7561542588067
- type: manhattan_ap
value: 62.41882051995577
- type: manhattan_f1
value: 59.32101002778785
- type: manhattan_precision
value: 54.71361711611321
- type: manhattan_recall
value: 64.77572559366754
- type: max_accuracy
value: 84.09131549144662
- type: max_ap
value: 66.86677647503386
- type: max_f1
value: 62.94631710362049
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.79574649745798
- type: cos_sim_ap
value: 85.28960532524223
- type: cos_sim_f1
value: 77.98460043358001
- type: cos_sim_precision
value: 75.78090948714224
- type: cos_sim_recall
value: 80.32029565753002
- type: dot_accuracy
value: 85.5939767920208
- type: dot_ap
value: 76.14131706694056
- type: dot_f1
value: 72.70246298696868
- type: dot_precision
value: 65.27012127894156
- type: dot_recall
value: 82.04496458269172
- type: euclidean_accuracy
value: 86.72332828812046
- type: euclidean_ap
value: 80.84854809178995
- type: euclidean_f1
value: 72.47657499809551
- type: euclidean_precision
value: 71.71717171717171
- type: euclidean_recall
value: 73.25223283030489
- type: manhattan_accuracy
value: 86.7563162184189
- type: manhattan_ap
value: 80.87598895575626
- type: manhattan_f1
value: 72.54617892068092
- type: manhattan_precision
value: 68.49268225960881
- type: manhattan_recall
value: 77.10963966738528
- type: max_accuracy
value: 88.79574649745798
- type: max_ap
value: 85.28960532524223
- type: max_f1
value: 77.98460043358001
---
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 249592 with parameters:
```
{'batch_size': 2, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTJModel
(1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-2.7B-weightedmean-msmarco-specb-bitfit
|
Muennighoff
| 2023-03-27T22:24:48Z | 406 | 3 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: SGPT-2.7B-weightedmean-msmarco-specb-bitfit
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 67.56716417910448
- type: ap
value: 30.75574629595259
- type: f1
value: 61.805121301858655
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 71.439575
- type: ap
value: 65.91341330532453
- type: f1
value: 70.90561852619555
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 35.748000000000005
- type: f1
value: 35.48576287186347
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 25.96
- type: map_at_10
value: 41.619
- type: map_at_100
value: 42.673
- type: map_at_1000
value: 42.684
- type: map_at_3
value: 36.569
- type: map_at_5
value: 39.397
- type: mrr_at_1
value: 26.316
- type: mrr_at_10
value: 41.772
- type: mrr_at_100
value: 42.82
- type: mrr_at_1000
value: 42.83
- type: mrr_at_3
value: 36.724000000000004
- type: mrr_at_5
value: 39.528999999999996
- type: ndcg_at_1
value: 25.96
- type: ndcg_at_10
value: 50.491
- type: ndcg_at_100
value: 54.864999999999995
- type: ndcg_at_1000
value: 55.10699999999999
- type: ndcg_at_3
value: 40.053
- type: ndcg_at_5
value: 45.134
- type: precision_at_1
value: 25.96
- type: precision_at_10
value: 7.8950000000000005
- type: precision_at_100
value: 0.9780000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.714000000000002
- type: precision_at_5
value: 12.489
- type: recall_at_1
value: 25.96
- type: recall_at_10
value: 78.947
- type: recall_at_100
value: 97.795
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 50.141999999999996
- type: recall_at_5
value: 62.446999999999996
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 44.72125714642202
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 35.081451519142064
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 59.634661990392054
- type: mrr
value: 73.6813525040672
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 87.42754550496836
- type: cos_sim_spearman
value: 84.84289705838664
- type: euclidean_pearson
value: 85.59331970450859
- type: euclidean_spearman
value: 85.8525586184271
- type: manhattan_pearson
value: 85.41233134466698
- type: manhattan_spearman
value: 85.52303303767404
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 83.21753246753246
- type: f1
value: 83.15394543120915
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 34.41414219680629
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 30.533275862270028
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 30.808999999999997
- type: map_at_10
value: 40.617
- type: map_at_100
value: 41.894999999999996
- type: map_at_1000
value: 42.025
- type: map_at_3
value: 37.0
- type: map_at_5
value: 38.993
- type: mrr_at_1
value: 37.482
- type: mrr_at_10
value: 46.497
- type: mrr_at_100
value: 47.144000000000005
- type: mrr_at_1000
value: 47.189
- type: mrr_at_3
value: 43.705
- type: mrr_at_5
value: 45.193
- type: ndcg_at_1
value: 37.482
- type: ndcg_at_10
value: 46.688
- type: ndcg_at_100
value: 51.726000000000006
- type: ndcg_at_1000
value: 53.825
- type: ndcg_at_3
value: 41.242000000000004
- type: ndcg_at_5
value: 43.657000000000004
- type: precision_at_1
value: 37.482
- type: precision_at_10
value: 8.827
- type: precision_at_100
value: 1.393
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 19.361
- type: precision_at_5
value: 14.106
- type: recall_at_1
value: 30.808999999999997
- type: recall_at_10
value: 58.47
- type: recall_at_100
value: 80.51899999999999
- type: recall_at_1000
value: 93.809
- type: recall_at_3
value: 42.462
- type: recall_at_5
value: 49.385
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 26.962000000000003
- type: map_at_10
value: 36.93
- type: map_at_100
value: 38.102000000000004
- type: map_at_1000
value: 38.22
- type: map_at_3
value: 34.065
- type: map_at_5
value: 35.72
- type: mrr_at_1
value: 33.567
- type: mrr_at_10
value: 42.269
- type: mrr_at_100
value: 42.99
- type: mrr_at_1000
value: 43.033
- type: mrr_at_3
value: 40.064
- type: mrr_at_5
value: 41.258
- type: ndcg_at_1
value: 33.567
- type: ndcg_at_10
value: 42.405
- type: ndcg_at_100
value: 46.847
- type: ndcg_at_1000
value: 48.951
- type: ndcg_at_3
value: 38.312000000000005
- type: ndcg_at_5
value: 40.242
- type: precision_at_1
value: 33.567
- type: precision_at_10
value: 8.032
- type: precision_at_100
value: 1.295
- type: precision_at_1000
value: 0.17600000000000002
- type: precision_at_3
value: 18.662
- type: precision_at_5
value: 13.299
- type: recall_at_1
value: 26.962000000000003
- type: recall_at_10
value: 52.489
- type: recall_at_100
value: 71.635
- type: recall_at_1000
value: 85.141
- type: recall_at_3
value: 40.28
- type: recall_at_5
value: 45.757
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 36.318
- type: map_at_10
value: 47.97
- type: map_at_100
value: 49.003
- type: map_at_1000
value: 49.065999999999995
- type: map_at_3
value: 45.031
- type: map_at_5
value: 46.633
- type: mrr_at_1
value: 41.504999999999995
- type: mrr_at_10
value: 51.431000000000004
- type: mrr_at_100
value: 52.129000000000005
- type: mrr_at_1000
value: 52.161
- type: mrr_at_3
value: 48.934
- type: mrr_at_5
value: 50.42
- type: ndcg_at_1
value: 41.504999999999995
- type: ndcg_at_10
value: 53.676
- type: ndcg_at_100
value: 57.867000000000004
- type: ndcg_at_1000
value: 59.166
- type: ndcg_at_3
value: 48.516
- type: ndcg_at_5
value: 50.983999999999995
- type: precision_at_1
value: 41.504999999999995
- type: precision_at_10
value: 8.608
- type: precision_at_100
value: 1.1560000000000001
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 21.462999999999997
- type: precision_at_5
value: 14.721
- type: recall_at_1
value: 36.318
- type: recall_at_10
value: 67.066
- type: recall_at_100
value: 85.34
- type: recall_at_1000
value: 94.491
- type: recall_at_3
value: 53.215999999999994
- type: recall_at_5
value: 59.214
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 22.167
- type: map_at_10
value: 29.543999999999997
- type: map_at_100
value: 30.579
- type: map_at_1000
value: 30.669999999999998
- type: map_at_3
value: 26.982
- type: map_at_5
value: 28.474
- type: mrr_at_1
value: 24.068
- type: mrr_at_10
value: 31.237
- type: mrr_at_100
value: 32.222
- type: mrr_at_1000
value: 32.292
- type: mrr_at_3
value: 28.776000000000003
- type: mrr_at_5
value: 30.233999999999998
- type: ndcg_at_1
value: 24.068
- type: ndcg_at_10
value: 33.973
- type: ndcg_at_100
value: 39.135
- type: ndcg_at_1000
value: 41.443999999999996
- type: ndcg_at_3
value: 29.018
- type: ndcg_at_5
value: 31.558999999999997
- type: precision_at_1
value: 24.068
- type: precision_at_10
value: 5.299
- type: precision_at_100
value: 0.823
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 12.166
- type: precision_at_5
value: 8.767999999999999
- type: recall_at_1
value: 22.167
- type: recall_at_10
value: 46.115
- type: recall_at_100
value: 69.867
- type: recall_at_1000
value: 87.234
- type: recall_at_3
value: 32.798
- type: recall_at_5
value: 38.951
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 12.033000000000001
- type: map_at_10
value: 19.314
- type: map_at_100
value: 20.562
- type: map_at_1000
value: 20.695
- type: map_at_3
value: 16.946
- type: map_at_5
value: 18.076999999999998
- type: mrr_at_1
value: 14.801
- type: mrr_at_10
value: 22.74
- type: mrr_at_100
value: 23.876
- type: mrr_at_1000
value: 23.949
- type: mrr_at_3
value: 20.211000000000002
- type: mrr_at_5
value: 21.573
- type: ndcg_at_1
value: 14.801
- type: ndcg_at_10
value: 24.038
- type: ndcg_at_100
value: 30.186
- type: ndcg_at_1000
value: 33.321
- type: ndcg_at_3
value: 19.431
- type: ndcg_at_5
value: 21.34
- type: precision_at_1
value: 14.801
- type: precision_at_10
value: 4.776
- type: precision_at_100
value: 0.897
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 9.66
- type: precision_at_5
value: 7.239
- type: recall_at_1
value: 12.033000000000001
- type: recall_at_10
value: 35.098
- type: recall_at_100
value: 62.175000000000004
- type: recall_at_1000
value: 84.17099999999999
- type: recall_at_3
value: 22.61
- type: recall_at_5
value: 27.278999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 26.651000000000003
- type: map_at_10
value: 36.901
- type: map_at_100
value: 38.249
- type: map_at_1000
value: 38.361000000000004
- type: map_at_3
value: 33.891
- type: map_at_5
value: 35.439
- type: mrr_at_1
value: 32.724
- type: mrr_at_10
value: 42.504
- type: mrr_at_100
value: 43.391999999999996
- type: mrr_at_1000
value: 43.436
- type: mrr_at_3
value: 39.989999999999995
- type: mrr_at_5
value: 41.347
- type: ndcg_at_1
value: 32.724
- type: ndcg_at_10
value: 43.007
- type: ndcg_at_100
value: 48.601
- type: ndcg_at_1000
value: 50.697
- type: ndcg_at_3
value: 37.99
- type: ndcg_at_5
value: 40.083999999999996
- type: precision_at_1
value: 32.724
- type: precision_at_10
value: 7.872999999999999
- type: precision_at_100
value: 1.247
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 18.062
- type: precision_at_5
value: 12.666
- type: recall_at_1
value: 26.651000000000003
- type: recall_at_10
value: 55.674
- type: recall_at_100
value: 78.904
- type: recall_at_1000
value: 92.55799999999999
- type: recall_at_3
value: 41.36
- type: recall_at_5
value: 46.983999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 22.589000000000002
- type: map_at_10
value: 32.244
- type: map_at_100
value: 33.46
- type: map_at_1000
value: 33.593
- type: map_at_3
value: 29.21
- type: map_at_5
value: 31.019999999999996
- type: mrr_at_1
value: 28.425
- type: mrr_at_10
value: 37.282
- type: mrr_at_100
value: 38.187
- type: mrr_at_1000
value: 38.248
- type: mrr_at_3
value: 34.684
- type: mrr_at_5
value: 36.123
- type: ndcg_at_1
value: 28.425
- type: ndcg_at_10
value: 37.942
- type: ndcg_at_100
value: 43.443
- type: ndcg_at_1000
value: 45.995999999999995
- type: ndcg_at_3
value: 32.873999999999995
- type: ndcg_at_5
value: 35.325
- type: precision_at_1
value: 28.425
- type: precision_at_10
value: 7.1
- type: precision_at_100
value: 1.166
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.02
- type: precision_at_5
value: 11.644
- type: recall_at_1
value: 22.589000000000002
- type: recall_at_10
value: 50.03999999999999
- type: recall_at_100
value: 73.973
- type: recall_at_1000
value: 91.128
- type: recall_at_3
value: 35.882999999999996
- type: recall_at_5
value: 42.187999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.190833333333334
- type: map_at_10
value: 31.504916666666666
- type: map_at_100
value: 32.64908333333334
- type: map_at_1000
value: 32.77075
- type: map_at_3
value: 28.82575
- type: map_at_5
value: 30.2755
- type: mrr_at_1
value: 27.427499999999995
- type: mrr_at_10
value: 35.36483333333334
- type: mrr_at_100
value: 36.23441666666666
- type: mrr_at_1000
value: 36.297583333333336
- type: mrr_at_3
value: 32.97966666666667
- type: mrr_at_5
value: 34.294583333333335
- type: ndcg_at_1
value: 27.427499999999995
- type: ndcg_at_10
value: 36.53358333333333
- type: ndcg_at_100
value: 41.64508333333333
- type: ndcg_at_1000
value: 44.14499999999999
- type: ndcg_at_3
value: 31.88908333333333
- type: ndcg_at_5
value: 33.98433333333333
- type: precision_at_1
value: 27.427499999999995
- type: precision_at_10
value: 6.481083333333333
- type: precision_at_100
value: 1.0610833333333334
- type: precision_at_1000
value: 0.14691666666666667
- type: precision_at_3
value: 14.656749999999999
- type: precision_at_5
value: 10.493583333333332
- type: recall_at_1
value: 23.190833333333334
- type: recall_at_10
value: 47.65175
- type: recall_at_100
value: 70.41016666666667
- type: recall_at_1000
value: 87.82708333333332
- type: recall_at_3
value: 34.637583333333325
- type: recall_at_5
value: 40.05008333333333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.409
- type: map_at_10
value: 26.794
- type: map_at_100
value: 27.682000000000002
- type: map_at_1000
value: 27.783
- type: map_at_3
value: 24.461
- type: map_at_5
value: 25.668000000000003
- type: mrr_at_1
value: 22.853
- type: mrr_at_10
value: 29.296
- type: mrr_at_100
value: 30.103
- type: mrr_at_1000
value: 30.179000000000002
- type: mrr_at_3
value: 27.173000000000002
- type: mrr_at_5
value: 28.223
- type: ndcg_at_1
value: 22.853
- type: ndcg_at_10
value: 31.007
- type: ndcg_at_100
value: 35.581
- type: ndcg_at_1000
value: 38.147
- type: ndcg_at_3
value: 26.590999999999998
- type: ndcg_at_5
value: 28.43
- type: precision_at_1
value: 22.853
- type: precision_at_10
value: 5.031
- type: precision_at_100
value: 0.7939999999999999
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 11.401
- type: precision_at_5
value: 8.16
- type: recall_at_1
value: 20.409
- type: recall_at_10
value: 41.766
- type: recall_at_100
value: 62.964
- type: recall_at_1000
value: 81.682
- type: recall_at_3
value: 29.281000000000002
- type: recall_at_5
value: 33.83
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 14.549000000000001
- type: map_at_10
value: 20.315
- type: map_at_100
value: 21.301000000000002
- type: map_at_1000
value: 21.425
- type: map_at_3
value: 18.132
- type: map_at_5
value: 19.429
- type: mrr_at_1
value: 17.86
- type: mrr_at_10
value: 23.860999999999997
- type: mrr_at_100
value: 24.737000000000002
- type: mrr_at_1000
value: 24.82
- type: mrr_at_3
value: 21.685
- type: mrr_at_5
value: 23.008
- type: ndcg_at_1
value: 17.86
- type: ndcg_at_10
value: 24.396
- type: ndcg_at_100
value: 29.328
- type: ndcg_at_1000
value: 32.486
- type: ndcg_at_3
value: 20.375
- type: ndcg_at_5
value: 22.411
- type: precision_at_1
value: 17.86
- type: precision_at_10
value: 4.47
- type: precision_at_100
value: 0.8099999999999999
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 9.475
- type: precision_at_5
value: 7.170999999999999
- type: recall_at_1
value: 14.549000000000001
- type: recall_at_10
value: 33.365
- type: recall_at_100
value: 55.797
- type: recall_at_1000
value: 78.632
- type: recall_at_3
value: 22.229
- type: recall_at_5
value: 27.339000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.286
- type: map_at_10
value: 30.728
- type: map_at_100
value: 31.840000000000003
- type: map_at_1000
value: 31.953
- type: map_at_3
value: 28.302
- type: map_at_5
value: 29.615000000000002
- type: mrr_at_1
value: 27.239
- type: mrr_at_10
value: 34.408
- type: mrr_at_100
value: 35.335
- type: mrr_at_1000
value: 35.405
- type: mrr_at_3
value: 32.151999999999994
- type: mrr_at_5
value: 33.355000000000004
- type: ndcg_at_1
value: 27.239
- type: ndcg_at_10
value: 35.324
- type: ndcg_at_100
value: 40.866
- type: ndcg_at_1000
value: 43.584
- type: ndcg_at_3
value: 30.898999999999997
- type: ndcg_at_5
value: 32.812999999999995
- type: precision_at_1
value: 27.239
- type: precision_at_10
value: 5.896
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 13.713000000000001
- type: precision_at_5
value: 9.683
- type: recall_at_1
value: 23.286
- type: recall_at_10
value: 45.711
- type: recall_at_100
value: 70.611
- type: recall_at_1000
value: 90.029
- type: recall_at_3
value: 33.615
- type: recall_at_5
value: 38.41
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.962
- type: map_at_10
value: 31.942999999999998
- type: map_at_100
value: 33.384
- type: map_at_1000
value: 33.611000000000004
- type: map_at_3
value: 29.243000000000002
- type: map_at_5
value: 30.446
- type: mrr_at_1
value: 28.458
- type: mrr_at_10
value: 36.157000000000004
- type: mrr_at_100
value: 37.092999999999996
- type: mrr_at_1000
value: 37.163000000000004
- type: mrr_at_3
value: 33.86
- type: mrr_at_5
value: 35.086
- type: ndcg_at_1
value: 28.458
- type: ndcg_at_10
value: 37.201
- type: ndcg_at_100
value: 42.591
- type: ndcg_at_1000
value: 45.539
- type: ndcg_at_3
value: 32.889
- type: ndcg_at_5
value: 34.483000000000004
- type: precision_at_1
value: 28.458
- type: precision_at_10
value: 7.332
- type: precision_at_100
value: 1.437
- type: precision_at_1000
value: 0.233
- type: precision_at_3
value: 15.547
- type: precision_at_5
value: 11.146
- type: recall_at_1
value: 23.962
- type: recall_at_10
value: 46.751
- type: recall_at_100
value: 71.626
- type: recall_at_1000
value: 90.93900000000001
- type: recall_at_3
value: 34.138000000000005
- type: recall_at_5
value: 38.673
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.555
- type: map_at_10
value: 24.759
- type: map_at_100
value: 25.732
- type: map_at_1000
value: 25.846999999999998
- type: map_at_3
value: 22.646
- type: map_at_5
value: 23.791999999999998
- type: mrr_at_1
value: 20.148
- type: mrr_at_10
value: 26.695999999999998
- type: mrr_at_100
value: 27.605
- type: mrr_at_1000
value: 27.695999999999998
- type: mrr_at_3
value: 24.522
- type: mrr_at_5
value: 25.715
- type: ndcg_at_1
value: 20.148
- type: ndcg_at_10
value: 28.746
- type: ndcg_at_100
value: 33.57
- type: ndcg_at_1000
value: 36.584
- type: ndcg_at_3
value: 24.532
- type: ndcg_at_5
value: 26.484
- type: precision_at_1
value: 20.148
- type: precision_at_10
value: 4.529
- type: precision_at_100
value: 0.736
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 10.351
- type: precision_at_5
value: 7.32
- type: recall_at_1
value: 18.555
- type: recall_at_10
value: 39.275999999999996
- type: recall_at_100
value: 61.511
- type: recall_at_1000
value: 84.111
- type: recall_at_3
value: 27.778999999999996
- type: recall_at_5
value: 32.591
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 10.366999999999999
- type: map_at_10
value: 18.953999999999997
- type: map_at_100
value: 20.674999999999997
- type: map_at_1000
value: 20.868000000000002
- type: map_at_3
value: 15.486
- type: map_at_5
value: 17.347
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 35.419
- type: mrr_at_100
value: 36.361
- type: mrr_at_1000
value: 36.403
- type: mrr_at_3
value: 31.747999999999998
- type: mrr_at_5
value: 34.077
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 27.11
- type: ndcg_at_100
value: 33.981
- type: ndcg_at_1000
value: 37.444
- type: ndcg_at_3
value: 21.471999999999998
- type: ndcg_at_5
value: 23.769000000000002
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 8.704
- type: precision_at_100
value: 1.606
- type: precision_at_1000
value: 0.22499999999999998
- type: precision_at_3
value: 16.287
- type: precision_at_5
value: 13.068
- type: recall_at_1
value: 10.366999999999999
- type: recall_at_10
value: 33.706
- type: recall_at_100
value: 57.375
- type: recall_at_1000
value: 76.79
- type: recall_at_3
value: 20.18
- type: recall_at_5
value: 26.215
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 8.246
- type: map_at_10
value: 15.979
- type: map_at_100
value: 21.025
- type: map_at_1000
value: 22.189999999999998
- type: map_at_3
value: 11.997
- type: map_at_5
value: 13.697000000000001
- type: mrr_at_1
value: 60.75000000000001
- type: mrr_at_10
value: 68.70100000000001
- type: mrr_at_100
value: 69.1
- type: mrr_at_1000
value: 69.111
- type: mrr_at_3
value: 66.583
- type: mrr_at_5
value: 67.87100000000001
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 34.702
- type: ndcg_at_100
value: 37.607
- type: ndcg_at_1000
value: 44.322
- type: ndcg_at_3
value: 39.555
- type: ndcg_at_5
value: 36.684
- type: precision_at_1
value: 60.75000000000001
- type: precision_at_10
value: 26.625
- type: precision_at_100
value: 7.969999999999999
- type: precision_at_1000
value: 1.678
- type: precision_at_3
value: 41.833
- type: precision_at_5
value: 34.5
- type: recall_at_1
value: 8.246
- type: recall_at_10
value: 20.968
- type: recall_at_100
value: 42.065000000000005
- type: recall_at_1000
value: 63.671
- type: recall_at_3
value: 13.039000000000001
- type: recall_at_5
value: 16.042
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 49.214999999999996
- type: f1
value: 44.85952451163755
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 56.769000000000005
- type: map_at_10
value: 67.30199999999999
- type: map_at_100
value: 67.692
- type: map_at_1000
value: 67.712
- type: map_at_3
value: 65.346
- type: map_at_5
value: 66.574
- type: mrr_at_1
value: 61.370999999999995
- type: mrr_at_10
value: 71.875
- type: mrr_at_100
value: 72.195
- type: mrr_at_1000
value: 72.206
- type: mrr_at_3
value: 70.04
- type: mrr_at_5
value: 71.224
- type: ndcg_at_1
value: 61.370999999999995
- type: ndcg_at_10
value: 72.731
- type: ndcg_at_100
value: 74.468
- type: ndcg_at_1000
value: 74.91600000000001
- type: ndcg_at_3
value: 69.077
- type: ndcg_at_5
value: 71.111
- type: precision_at_1
value: 61.370999999999995
- type: precision_at_10
value: 9.325999999999999
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 27.303
- type: precision_at_5
value: 17.525
- type: recall_at_1
value: 56.769000000000005
- type: recall_at_10
value: 85.06
- type: recall_at_100
value: 92.767
- type: recall_at_1000
value: 95.933
- type: recall_at_3
value: 75.131
- type: recall_at_5
value: 80.17
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 15.753
- type: map_at_10
value: 25.875999999999998
- type: map_at_100
value: 27.415
- type: map_at_1000
value: 27.590999999999998
- type: map_at_3
value: 22.17
- type: map_at_5
value: 24.236
- type: mrr_at_1
value: 31.019000000000002
- type: mrr_at_10
value: 39.977000000000004
- type: mrr_at_100
value: 40.788999999999994
- type: mrr_at_1000
value: 40.832
- type: mrr_at_3
value: 37.088
- type: mrr_at_5
value: 38.655
- type: ndcg_at_1
value: 31.019000000000002
- type: ndcg_at_10
value: 33.286
- type: ndcg_at_100
value: 39.528999999999996
- type: ndcg_at_1000
value: 42.934
- type: ndcg_at_3
value: 29.29
- type: ndcg_at_5
value: 30.615
- type: precision_at_1
value: 31.019000000000002
- type: precision_at_10
value: 9.383
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 19.753
- type: precision_at_5
value: 14.815000000000001
- type: recall_at_1
value: 15.753
- type: recall_at_10
value: 40.896
- type: recall_at_100
value: 64.443
- type: recall_at_1000
value: 85.218
- type: recall_at_3
value: 26.526
- type: recall_at_5
value: 32.452999999999996
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 32.153999999999996
- type: map_at_10
value: 43.651
- type: map_at_100
value: 44.41
- type: map_at_1000
value: 44.487
- type: map_at_3
value: 41.239
- type: map_at_5
value: 42.659000000000006
- type: mrr_at_1
value: 64.30799999999999
- type: mrr_at_10
value: 71.22500000000001
- type: mrr_at_100
value: 71.57
- type: mrr_at_1000
value: 71.59100000000001
- type: mrr_at_3
value: 69.95
- type: mrr_at_5
value: 70.738
- type: ndcg_at_1
value: 64.30799999999999
- type: ndcg_at_10
value: 52.835
- type: ndcg_at_100
value: 55.840999999999994
- type: ndcg_at_1000
value: 57.484
- type: ndcg_at_3
value: 49.014
- type: ndcg_at_5
value: 51.01599999999999
- type: precision_at_1
value: 64.30799999999999
- type: precision_at_10
value: 10.77
- type: precision_at_100
value: 1.315
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 30.223
- type: precision_at_5
value: 19.716
- type: recall_at_1
value: 32.153999999999996
- type: recall_at_10
value: 53.849000000000004
- type: recall_at_100
value: 65.75999999999999
- type: recall_at_1000
value: 76.705
- type: recall_at_3
value: 45.334
- type: recall_at_5
value: 49.291000000000004
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 63.5316
- type: ap
value: 58.90084300359825
- type: f1
value: 63.35727889030892
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 20.566000000000003
- type: map_at_10
value: 32.229
- type: map_at_100
value: 33.445
- type: map_at_1000
value: 33.501
- type: map_at_3
value: 28.504
- type: map_at_5
value: 30.681000000000004
- type: mrr_at_1
value: 21.218
- type: mrr_at_10
value: 32.816
- type: mrr_at_100
value: 33.986
- type: mrr_at_1000
value: 34.035
- type: mrr_at_3
value: 29.15
- type: mrr_at_5
value: 31.290000000000003
- type: ndcg_at_1
value: 21.218
- type: ndcg_at_10
value: 38.832
- type: ndcg_at_100
value: 44.743
- type: ndcg_at_1000
value: 46.138
- type: ndcg_at_3
value: 31.232
- type: ndcg_at_5
value: 35.099999999999994
- type: precision_at_1
value: 21.218
- type: precision_at_10
value: 6.186
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 13.314
- type: precision_at_5
value: 9.943
- type: recall_at_1
value: 20.566000000000003
- type: recall_at_10
value: 59.192
- type: recall_at_100
value: 86.626
- type: recall_at_1000
value: 97.283
- type: recall_at_3
value: 38.492
- type: recall_at_5
value: 47.760000000000005
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 92.56269949840402
- type: f1
value: 92.1020975473988
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 71.8467852257182
- type: f1
value: 53.652719348592015
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 69.00806993947546
- type: f1
value: 67.41429618885515
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.90114324142569
- type: f1
value: 76.25183590651454
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 31.350109978273395
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 28.768923695767327
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.716396735210754
- type: mrr
value: 32.88970538547634
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 5.604
- type: map_at_10
value: 12.379999999999999
- type: map_at_100
value: 15.791
- type: map_at_1000
value: 17.327
- type: map_at_3
value: 9.15
- type: map_at_5
value: 10.599
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 53.374
- type: mrr_at_100
value: 54.089
- type: mrr_at_1000
value: 54.123
- type: mrr_at_3
value: 51.44499999999999
- type: mrr_at_5
value: 52.59
- type: ndcg_at_1
value: 42.879
- type: ndcg_at_10
value: 33.891
- type: ndcg_at_100
value: 31.391999999999996
- type: ndcg_at_1000
value: 40.36
- type: ndcg_at_3
value: 39.076
- type: ndcg_at_5
value: 37.047000000000004
- type: precision_at_1
value: 44.582
- type: precision_at_10
value: 25.294
- type: precision_at_100
value: 8.285
- type: precision_at_1000
value: 2.1479999999999997
- type: precision_at_3
value: 36.120000000000005
- type: precision_at_5
value: 31.95
- type: recall_at_1
value: 5.604
- type: recall_at_10
value: 16.239
- type: recall_at_100
value: 32.16
- type: recall_at_1000
value: 64.513
- type: recall_at_3
value: 10.406
- type: recall_at_5
value: 12.684999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 25.881
- type: map_at_10
value: 39.501
- type: map_at_100
value: 40.615
- type: map_at_1000
value: 40.661
- type: map_at_3
value: 35.559000000000005
- type: map_at_5
value: 37.773
- type: mrr_at_1
value: 29.229
- type: mrr_at_10
value: 41.955999999999996
- type: mrr_at_100
value: 42.86
- type: mrr_at_1000
value: 42.893
- type: mrr_at_3
value: 38.562000000000005
- type: mrr_at_5
value: 40.542
- type: ndcg_at_1
value: 29.2
- type: ndcg_at_10
value: 46.703
- type: ndcg_at_100
value: 51.644
- type: ndcg_at_1000
value: 52.771
- type: ndcg_at_3
value: 39.141999999999996
- type: ndcg_at_5
value: 42.892
- type: precision_at_1
value: 29.2
- type: precision_at_10
value: 7.920000000000001
- type: precision_at_100
value: 1.0659999999999998
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 18.105
- type: precision_at_5
value: 13.036
- type: recall_at_1
value: 25.881
- type: recall_at_10
value: 66.266
- type: recall_at_100
value: 88.116
- type: recall_at_1000
value: 96.58200000000001
- type: recall_at_3
value: 46.526
- type: recall_at_5
value: 55.154
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 67.553
- type: map_at_10
value: 81.34
- type: map_at_100
value: 82.002
- type: map_at_1000
value: 82.027
- type: map_at_3
value: 78.281
- type: map_at_5
value: 80.149
- type: mrr_at_1
value: 77.72
- type: mrr_at_10
value: 84.733
- type: mrr_at_100
value: 84.878
- type: mrr_at_1000
value: 84.879
- type: mrr_at_3
value: 83.587
- type: mrr_at_5
value: 84.32600000000001
- type: ndcg_at_1
value: 77.75
- type: ndcg_at_10
value: 85.603
- type: ndcg_at_100
value: 87.069
- type: ndcg_at_1000
value: 87.25
- type: ndcg_at_3
value: 82.303
- type: ndcg_at_5
value: 84.03699999999999
- type: precision_at_1
value: 77.75
- type: precision_at_10
value: 13.04
- type: precision_at_100
value: 1.5070000000000001
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.903
- type: precision_at_5
value: 23.738
- type: recall_at_1
value: 67.553
- type: recall_at_10
value: 93.903
- type: recall_at_100
value: 99.062
- type: recall_at_1000
value: 99.935
- type: recall_at_3
value: 84.58099999999999
- type: recall_at_5
value: 89.316
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 46.46887711230235
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 54.166876298246926
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 4.053
- type: map_at_10
value: 9.693999999999999
- type: map_at_100
value: 11.387
- type: map_at_1000
value: 11.654
- type: map_at_3
value: 7.053
- type: map_at_5
value: 8.439
- type: mrr_at_1
value: 19.900000000000002
- type: mrr_at_10
value: 29.359
- type: mrr_at_100
value: 30.484
- type: mrr_at_1000
value: 30.553
- type: mrr_at_3
value: 26.200000000000003
- type: mrr_at_5
value: 28.115000000000002
- type: ndcg_at_1
value: 19.900000000000002
- type: ndcg_at_10
value: 16.575
- type: ndcg_at_100
value: 23.655
- type: ndcg_at_1000
value: 28.853
- type: ndcg_at_3
value: 15.848
- type: ndcg_at_5
value: 14.026
- type: precision_at_1
value: 19.900000000000002
- type: precision_at_10
value: 8.450000000000001
- type: precision_at_100
value: 1.872
- type: precision_at_1000
value: 0.313
- type: precision_at_3
value: 14.667
- type: precision_at_5
value: 12.32
- type: recall_at_1
value: 4.053
- type: recall_at_10
value: 17.169999999999998
- type: recall_at_100
value: 38.025
- type: recall_at_1000
value: 63.571999999999996
- type: recall_at_3
value: 8.903
- type: recall_at_5
value: 12.477
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 77.7548748519677
- type: cos_sim_spearman
value: 68.19926431966059
- type: euclidean_pearson
value: 71.69016204991725
- type: euclidean_spearman
value: 66.98099673026834
- type: manhattan_pearson
value: 71.62994072488664
- type: manhattan_spearman
value: 67.03435950744577
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 75.91051402657887
- type: cos_sim_spearman
value: 66.99390786191645
- type: euclidean_pearson
value: 71.54128036454578
- type: euclidean_spearman
value: 69.25605675649068
- type: manhattan_pearson
value: 71.60981030780171
- type: manhattan_spearman
value: 69.27513670128046
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 77.23835466417793
- type: cos_sim_spearman
value: 77.57623085766706
- type: euclidean_pearson
value: 77.5090992200725
- type: euclidean_spearman
value: 77.88601688144924
- type: manhattan_pearson
value: 77.39045060647423
- type: manhattan_spearman
value: 77.77552718279098
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 77.91692485139602
- type: cos_sim_spearman
value: 72.78258293483495
- type: euclidean_pearson
value: 74.64773017077789
- type: euclidean_spearman
value: 71.81662299104619
- type: manhattan_pearson
value: 74.71043337995533
- type: manhattan_spearman
value: 71.83960860845646
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 82.13422113617578
- type: cos_sim_spearman
value: 82.61707296911949
- type: euclidean_pearson
value: 81.42487480400861
- type: euclidean_spearman
value: 82.17970991273835
- type: manhattan_pearson
value: 81.41985055477845
- type: manhattan_spearman
value: 82.15823204362937
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 79.07989542843826
- type: cos_sim_spearman
value: 80.09839524406284
- type: euclidean_pearson
value: 76.43186028364195
- type: euclidean_spearman
value: 76.76720323266471
- type: manhattan_pearson
value: 76.4674747409161
- type: manhattan_spearman
value: 76.81797407068667
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 87.0420983224933
- type: cos_sim_spearman
value: 87.25017540413702
- type: euclidean_pearson
value: 84.56384596473421
- type: euclidean_spearman
value: 84.72557417564886
- type: manhattan_pearson
value: 84.7329954474549
- type: manhattan_spearman
value: 84.75071371008909
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 68.47031320016424
- type: cos_sim_spearman
value: 68.7486910762485
- type: euclidean_pearson
value: 71.30330985913915
- type: euclidean_spearman
value: 71.59666258520735
- type: manhattan_pearson
value: 71.4423884279027
- type: manhattan_spearman
value: 71.67460706861044
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 80.79514366062675
- type: cos_sim_spearman
value: 79.20585637461048
- type: euclidean_pearson
value: 78.6591557395699
- type: euclidean_spearman
value: 77.86455794285718
- type: manhattan_pearson
value: 78.67754806486865
- type: manhattan_spearman
value: 77.88178687200732
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 77.71580844366375
- type: mrr
value: 93.04215845882513
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 56.39999999999999
- type: map_at_10
value: 65.701
- type: map_at_100
value: 66.32000000000001
- type: map_at_1000
value: 66.34100000000001
- type: map_at_3
value: 62.641999999999996
- type: map_at_5
value: 64.342
- type: mrr_at_1
value: 58.667
- type: mrr_at_10
value: 66.45299999999999
- type: mrr_at_100
value: 66.967
- type: mrr_at_1000
value: 66.988
- type: mrr_at_3
value: 64.11099999999999
- type: mrr_at_5
value: 65.411
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_10
value: 70.165
- type: ndcg_at_100
value: 72.938
- type: ndcg_at_1000
value: 73.456
- type: ndcg_at_3
value: 64.79
- type: ndcg_at_5
value: 67.28
- type: precision_at_1
value: 58.667
- type: precision_at_10
value: 9.4
- type: precision_at_100
value: 1.087
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.889
- type: precision_at_5
value: 16.667
- type: recall_at_1
value: 56.39999999999999
- type: recall_at_10
value: 83.122
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 68.378
- type: recall_at_5
value: 74.68299999999999
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.76831683168317
- type: cos_sim_ap
value: 93.47124923047998
- type: cos_sim_f1
value: 88.06122448979592
- type: cos_sim_precision
value: 89.89583333333333
- type: cos_sim_recall
value: 86.3
- type: dot_accuracy
value: 99.57326732673268
- type: dot_ap
value: 84.06577868167207
- type: dot_f1
value: 77.82629791363416
- type: dot_precision
value: 75.58906691800189
- type: dot_recall
value: 80.2
- type: euclidean_accuracy
value: 99.74257425742574
- type: euclidean_ap
value: 92.1904681653555
- type: euclidean_f1
value: 86.74821610601427
- type: euclidean_precision
value: 88.46153846153845
- type: euclidean_recall
value: 85.1
- type: manhattan_accuracy
value: 99.74554455445545
- type: manhattan_ap
value: 92.4337790809948
- type: manhattan_f1
value: 86.86765457332653
- type: manhattan_precision
value: 88.81922675026124
- type: manhattan_recall
value: 85.0
- type: max_accuracy
value: 99.76831683168317
- type: max_ap
value: 93.47124923047998
- type: max_f1
value: 88.06122448979592
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 59.194098673976484
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 32.5744032578115
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 49.61186384154483
- type: mrr
value: 50.55424253034547
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 30.027210161713946
- type: cos_sim_spearman
value: 31.030178065751735
- type: dot_pearson
value: 30.09179785685587
- type: dot_spearman
value: 30.408303252207813
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.22300000000000003
- type: map_at_10
value: 1.762
- type: map_at_100
value: 9.984
- type: map_at_1000
value: 24.265
- type: map_at_3
value: 0.631
- type: map_at_5
value: 0.9950000000000001
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 92.833
- type: mrr_at_100
value: 92.833
- type: mrr_at_1000
value: 92.833
- type: mrr_at_3
value: 92.333
- type: mrr_at_5
value: 92.833
- type: ndcg_at_1
value: 83.0
- type: ndcg_at_10
value: 75.17
- type: ndcg_at_100
value: 55.432
- type: ndcg_at_1000
value: 49.482
- type: ndcg_at_3
value: 82.184
- type: ndcg_at_5
value: 79.712
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 78.60000000000001
- type: precision_at_100
value: 56.56
- type: precision_at_1000
value: 22.334
- type: precision_at_3
value: 86.667
- type: precision_at_5
value: 83.6
- type: recall_at_1
value: 0.22300000000000003
- type: recall_at_10
value: 1.9879999999999998
- type: recall_at_100
value: 13.300999999999998
- type: recall_at_1000
value: 46.587
- type: recall_at_3
value: 0.6629999999999999
- type: recall_at_5
value: 1.079
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 3.047
- type: map_at_10
value: 8.792
- type: map_at_100
value: 14.631
- type: map_at_1000
value: 16.127
- type: map_at_3
value: 4.673
- type: map_at_5
value: 5.897
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 49.271
- type: mrr_at_100
value: 50.181
- type: mrr_at_1000
value: 50.2
- type: mrr_at_3
value: 44.558
- type: mrr_at_5
value: 47.925000000000004
- type: ndcg_at_1
value: 35.714
- type: ndcg_at_10
value: 23.44
- type: ndcg_at_100
value: 35.345
- type: ndcg_at_1000
value: 46.495
- type: ndcg_at_3
value: 26.146
- type: ndcg_at_5
value: 24.878
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 20.816000000000003
- type: precision_at_100
value: 7.428999999999999
- type: precision_at_1000
value: 1.494
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 24.082
- type: recall_at_1
value: 3.047
- type: recall_at_10
value: 14.975
- type: recall_at_100
value: 45.943
- type: recall_at_1000
value: 80.31099999999999
- type: recall_at_3
value: 5.478000000000001
- type: recall_at_5
value: 8.294
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 68.84080000000002
- type: ap
value: 13.135219251019848
- type: f1
value: 52.849999421995506
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 56.68647425014149
- type: f1
value: 56.97981427365949
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 40.8911707239219
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.04226023722954
- type: cos_sim_ap
value: 63.681339908301325
- type: cos_sim_f1
value: 60.349184470480125
- type: cos_sim_precision
value: 53.437754271765655
- type: cos_sim_recall
value: 69.31398416886545
- type: dot_accuracy
value: 81.46271681468677
- type: dot_ap
value: 57.78072296265885
- type: dot_f1
value: 56.28769265132901
- type: dot_precision
value: 48.7993803253292
- type: dot_recall
value: 66.49076517150397
- type: euclidean_accuracy
value: 82.16606067830959
- type: euclidean_ap
value: 59.974530371203514
- type: euclidean_f1
value: 56.856023506366306
- type: euclidean_precision
value: 53.037916857012334
- type: euclidean_recall
value: 61.2664907651715
- type: manhattan_accuracy
value: 82.16606067830959
- type: manhattan_ap
value: 59.98962379571767
- type: manhattan_f1
value: 56.98153158451947
- type: manhattan_precision
value: 51.41158989598811
- type: manhattan_recall
value: 63.90501319261214
- type: max_accuracy
value: 83.04226023722954
- type: max_ap
value: 63.681339908301325
- type: max_f1
value: 60.349184470480125
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.56871191834517
- type: cos_sim_ap
value: 84.80240716354544
- type: cos_sim_f1
value: 77.07765285922385
- type: cos_sim_precision
value: 74.84947406601378
- type: cos_sim_recall
value: 79.44256236526024
- type: dot_accuracy
value: 86.00923662048356
- type: dot_ap
value: 78.6556459012073
- type: dot_f1
value: 72.7583749109052
- type: dot_precision
value: 67.72823779193206
- type: dot_recall
value: 78.59562673236834
- type: euclidean_accuracy
value: 87.84103698529127
- type: euclidean_ap
value: 83.50424424952834
- type: euclidean_f1
value: 75.74496544549307
- type: euclidean_precision
value: 73.19402556369381
- type: euclidean_recall
value: 78.48013550970127
- type: manhattan_accuracy
value: 87.9225365777933
- type: manhattan_ap
value: 83.49479248597825
- type: manhattan_f1
value: 75.67748162447101
- type: manhattan_precision
value: 73.06810035842294
- type: manhattan_recall
value: 78.48013550970127
- type: max_accuracy
value: 88.56871191834517
- type: max_ap
value: 84.80240716354544
- type: max_f1
value: 77.07765285922385
---
# SGPT-2.7B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 124796 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 7.5e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit
|
Muennighoff
| 2023-03-27T22:21:38Z | 504 | 5 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: SGPT-1.3B-weightedmean-msmarco-specb-bitfit
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 65.20895522388061
- type: ap
value: 29.59212705444778
- type: f1
value: 59.97099864321921
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 73.20565
- type: ap
value: 67.36680643550963
- type: f1
value: 72.90420520325125
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 34.955999999999996
- type: f1
value: 34.719324437696955
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 26.101999999999997
- type: map_at_10
value: 40.958
- type: map_at_100
value: 42.033
- type: map_at_1000
value: 42.042
- type: map_at_3
value: 36.332
- type: map_at_5
value: 38.608
- type: mrr_at_1
value: 26.387
- type: mrr_at_10
value: 41.051
- type: mrr_at_100
value: 42.118
- type: mrr_at_1000
value: 42.126999999999995
- type: mrr_at_3
value: 36.415
- type: mrr_at_5
value: 38.72
- type: ndcg_at_1
value: 26.101999999999997
- type: ndcg_at_10
value: 49.68
- type: ndcg_at_100
value: 54.257999999999996
- type: ndcg_at_1000
value: 54.486000000000004
- type: ndcg_at_3
value: 39.864
- type: ndcg_at_5
value: 43.980000000000004
- type: precision_at_1
value: 26.101999999999997
- type: precision_at_10
value: 7.781000000000001
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.714000000000002
- type: precision_at_5
value: 12.034
- type: recall_at_1
value: 26.101999999999997
- type: recall_at_10
value: 77.809
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 50.141999999999996
- type: recall_at_5
value: 60.171
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 43.384194916953774
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 33.70962633433912
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 58.133058996870076
- type: mrr
value: 72.10922041946972
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 86.62153841660047
- type: cos_sim_spearman
value: 83.01514456843276
- type: euclidean_pearson
value: 86.00431518427241
- type: euclidean_spearman
value: 83.85552516285783
- type: manhattan_pearson
value: 85.83025803351181
- type: manhattan_spearman
value: 83.86636878343106
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 82.05844155844156
- type: f1
value: 82.0185837884764
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 35.05918333141837
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 30.71055028830579
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 26.519
- type: map_at_10
value: 35.634
- type: map_at_100
value: 36.961
- type: map_at_1000
value: 37.088
- type: map_at_3
value: 32.254
- type: map_at_5
value: 34.22
- type: mrr_at_1
value: 32.332
- type: mrr_at_10
value: 41.168
- type: mrr_at_100
value: 41.977
- type: mrr_at_1000
value: 42.028999999999996
- type: mrr_at_3
value: 38.196999999999996
- type: mrr_at_5
value: 40.036
- type: ndcg_at_1
value: 32.332
- type: ndcg_at_10
value: 41.471000000000004
- type: ndcg_at_100
value: 46.955999999999996
- type: ndcg_at_1000
value: 49.262
- type: ndcg_at_3
value: 35.937999999999995
- type: ndcg_at_5
value: 38.702999999999996
- type: precision_at_1
value: 32.332
- type: precision_at_10
value: 7.7829999999999995
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 16.834
- type: precision_at_5
value: 12.418
- type: recall_at_1
value: 26.519
- type: recall_at_10
value: 53.190000000000005
- type: recall_at_100
value: 76.56500000000001
- type: recall_at_1000
value: 91.47800000000001
- type: recall_at_3
value: 38.034
- type: recall_at_5
value: 45.245999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 25.356
- type: map_at_10
value: 34.596
- type: map_at_100
value: 35.714
- type: map_at_1000
value: 35.839999999999996
- type: map_at_3
value: 32.073
- type: map_at_5
value: 33.475
- type: mrr_at_1
value: 31.274
- type: mrr_at_10
value: 39.592
- type: mrr_at_100
value: 40.284
- type: mrr_at_1000
value: 40.339999999999996
- type: mrr_at_3
value: 37.378
- type: mrr_at_5
value: 38.658
- type: ndcg_at_1
value: 31.274
- type: ndcg_at_10
value: 39.766
- type: ndcg_at_100
value: 44.028
- type: ndcg_at_1000
value: 46.445
- type: ndcg_at_3
value: 35.934
- type: ndcg_at_5
value: 37.751000000000005
- type: precision_at_1
value: 31.274
- type: precision_at_10
value: 7.452
- type: precision_at_100
value: 1.217
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 17.431
- type: precision_at_5
value: 12.306000000000001
- type: recall_at_1
value: 25.356
- type: recall_at_10
value: 49.344
- type: recall_at_100
value: 67.497
- type: recall_at_1000
value: 83.372
- type: recall_at_3
value: 38.227
- type: recall_at_5
value: 43.187999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 32.759
- type: map_at_10
value: 43.937
- type: map_at_100
value: 45.004
- type: map_at_1000
value: 45.07
- type: map_at_3
value: 40.805
- type: map_at_5
value: 42.497
- type: mrr_at_1
value: 37.367
- type: mrr_at_10
value: 47.237
- type: mrr_at_100
value: 47.973
- type: mrr_at_1000
value: 48.010999999999996
- type: mrr_at_3
value: 44.65
- type: mrr_at_5
value: 46.050999999999995
- type: ndcg_at_1
value: 37.367
- type: ndcg_at_10
value: 49.659
- type: ndcg_at_100
value: 54.069
- type: ndcg_at_1000
value: 55.552
- type: ndcg_at_3
value: 44.169000000000004
- type: ndcg_at_5
value: 46.726
- type: precision_at_1
value: 37.367
- type: precision_at_10
value: 8.163
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 19.707
- type: precision_at_5
value: 13.718
- type: recall_at_1
value: 32.759
- type: recall_at_10
value: 63.341
- type: recall_at_100
value: 82.502
- type: recall_at_1000
value: 93.259
- type: recall_at_3
value: 48.796
- type: recall_at_5
value: 54.921
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.962
- type: map_at_10
value: 25.863000000000003
- type: map_at_100
value: 26.817999999999998
- type: map_at_1000
value: 26.918
- type: map_at_3
value: 23.043
- type: map_at_5
value: 24.599
- type: mrr_at_1
value: 20.452
- type: mrr_at_10
value: 27.301
- type: mrr_at_100
value: 28.233000000000004
- type: mrr_at_1000
value: 28.310000000000002
- type: mrr_at_3
value: 24.539
- type: mrr_at_5
value: 26.108999999999998
- type: ndcg_at_1
value: 20.452
- type: ndcg_at_10
value: 30.354999999999997
- type: ndcg_at_100
value: 35.336
- type: ndcg_at_1000
value: 37.927
- type: ndcg_at_3
value: 24.705
- type: ndcg_at_5
value: 27.42
- type: precision_at_1
value: 20.452
- type: precision_at_10
value: 4.949
- type: precision_at_100
value: 0.7799999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 10.358
- type: precision_at_5
value: 7.774
- type: recall_at_1
value: 18.962
- type: recall_at_10
value: 43.056
- type: recall_at_100
value: 66.27300000000001
- type: recall_at_1000
value: 85.96000000000001
- type: recall_at_3
value: 27.776
- type: recall_at_5
value: 34.287
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 11.24
- type: map_at_10
value: 18.503
- type: map_at_100
value: 19.553
- type: map_at_1000
value: 19.689999999999998
- type: map_at_3
value: 16.150000000000002
- type: map_at_5
value: 17.254
- type: mrr_at_1
value: 13.806
- type: mrr_at_10
value: 21.939
- type: mrr_at_100
value: 22.827
- type: mrr_at_1000
value: 22.911
- type: mrr_at_3
value: 19.32
- type: mrr_at_5
value: 20.558
- type: ndcg_at_1
value: 13.806
- type: ndcg_at_10
value: 23.383000000000003
- type: ndcg_at_100
value: 28.834
- type: ndcg_at_1000
value: 32.175
- type: ndcg_at_3
value: 18.651999999999997
- type: ndcg_at_5
value: 20.505000000000003
- type: precision_at_1
value: 13.806
- type: precision_at_10
value: 4.714
- type: precision_at_100
value: 0.864
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 9.328
- type: precision_at_5
value: 6.841
- type: recall_at_1
value: 11.24
- type: recall_at_10
value: 34.854
- type: recall_at_100
value: 59.50299999999999
- type: recall_at_1000
value: 83.25
- type: recall_at_3
value: 22.02
- type: recall_at_5
value: 26.715
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 23.012
- type: map_at_10
value: 33.048
- type: map_at_100
value: 34.371
- type: map_at_1000
value: 34.489
- type: map_at_3
value: 29.942999999999998
- type: map_at_5
value: 31.602000000000004
- type: mrr_at_1
value: 28.104000000000003
- type: mrr_at_10
value: 37.99
- type: mrr_at_100
value: 38.836
- type: mrr_at_1000
value: 38.891
- type: mrr_at_3
value: 35.226
- type: mrr_at_5
value: 36.693999999999996
- type: ndcg_at_1
value: 28.104000000000003
- type: ndcg_at_10
value: 39.037
- type: ndcg_at_100
value: 44.643
- type: ndcg_at_1000
value: 46.939
- type: ndcg_at_3
value: 33.784
- type: ndcg_at_5
value: 36.126000000000005
- type: precision_at_1
value: 28.104000000000003
- type: precision_at_10
value: 7.2669999999999995
- type: precision_at_100
value: 1.193
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 16.298000000000002
- type: precision_at_5
value: 11.684
- type: recall_at_1
value: 23.012
- type: recall_at_10
value: 52.054
- type: recall_at_100
value: 75.622
- type: recall_at_1000
value: 90.675
- type: recall_at_3
value: 37.282
- type: recall_at_5
value: 43.307
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 30.209999999999997
- type: map_at_100
value: 31.52
- type: map_at_1000
value: 31.625999999999998
- type: map_at_3
value: 26.951000000000004
- type: map_at_5
value: 28.938999999999997
- type: mrr_at_1
value: 26.941
- type: mrr_at_10
value: 35.13
- type: mrr_at_100
value: 36.15
- type: mrr_at_1000
value: 36.204
- type: mrr_at_3
value: 32.42
- type: mrr_at_5
value: 34.155
- type: ndcg_at_1
value: 26.941
- type: ndcg_at_10
value: 35.726
- type: ndcg_at_100
value: 41.725
- type: ndcg_at_1000
value: 44.105
- type: ndcg_at_3
value: 30.184
- type: ndcg_at_5
value: 33.176
- type: precision_at_1
value: 26.941
- type: precision_at_10
value: 6.654999999999999
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 14.346
- type: precision_at_5
value: 10.868
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 47.359
- type: recall_at_100
value: 73.436
- type: recall_at_1000
value: 89.988
- type: recall_at_3
value: 32.34
- type: recall_at_5
value: 39.856
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.67566666666667
- type: map_at_10
value: 28.479333333333333
- type: map_at_100
value: 29.612249999999996
- type: map_at_1000
value: 29.731166666666663
- type: map_at_3
value: 25.884
- type: map_at_5
value: 27.298916666666667
- type: mrr_at_1
value: 24.402583333333332
- type: mrr_at_10
value: 32.07041666666667
- type: mrr_at_100
value: 32.95841666666667
- type: mrr_at_1000
value: 33.025416666666665
- type: mrr_at_3
value: 29.677749999999996
- type: mrr_at_5
value: 31.02391666666667
- type: ndcg_at_1
value: 24.402583333333332
- type: ndcg_at_10
value: 33.326166666666666
- type: ndcg_at_100
value: 38.51566666666667
- type: ndcg_at_1000
value: 41.13791666666667
- type: ndcg_at_3
value: 28.687749999999994
- type: ndcg_at_5
value: 30.84766666666667
- type: precision_at_1
value: 24.402583333333332
- type: precision_at_10
value: 5.943749999999999
- type: precision_at_100
value: 1.0098333333333334
- type: precision_at_1000
value: 0.14183333333333334
- type: precision_at_3
value: 13.211500000000001
- type: precision_at_5
value: 9.548416666666668
- type: recall_at_1
value: 20.67566666666667
- type: recall_at_10
value: 44.245583333333336
- type: recall_at_100
value: 67.31116666666667
- type: recall_at_1000
value: 85.87841666666665
- type: recall_at_3
value: 31.49258333333333
- type: recall_at_5
value: 36.93241666666667
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 18.34
- type: map_at_10
value: 23.988
- type: map_at_100
value: 24.895
- type: map_at_1000
value: 24.992
- type: map_at_3
value: 21.831
- type: map_at_5
value: 23.0
- type: mrr_at_1
value: 20.399
- type: mrr_at_10
value: 26.186
- type: mrr_at_100
value: 27.017999999999997
- type: mrr_at_1000
value: 27.090999999999998
- type: mrr_at_3
value: 24.08
- type: mrr_at_5
value: 25.230000000000004
- type: ndcg_at_1
value: 20.399
- type: ndcg_at_10
value: 27.799000000000003
- type: ndcg_at_100
value: 32.579
- type: ndcg_at_1000
value: 35.209
- type: ndcg_at_3
value: 23.684
- type: ndcg_at_5
value: 25.521
- type: precision_at_1
value: 20.399
- type: precision_at_10
value: 4.585999999999999
- type: precision_at_100
value: 0.755
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 10.276
- type: precision_at_5
value: 7.362
- type: recall_at_1
value: 18.34
- type: recall_at_10
value: 37.456
- type: recall_at_100
value: 59.86
- type: recall_at_1000
value: 79.703
- type: recall_at_3
value: 26.163999999999998
- type: recall_at_5
value: 30.652
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 12.327
- type: map_at_10
value: 17.572
- type: map_at_100
value: 18.534
- type: map_at_1000
value: 18.653
- type: map_at_3
value: 15.703
- type: map_at_5
value: 16.752
- type: mrr_at_1
value: 15.038000000000002
- type: mrr_at_10
value: 20.726
- type: mrr_at_100
value: 21.61
- type: mrr_at_1000
value: 21.695
- type: mrr_at_3
value: 18.829
- type: mrr_at_5
value: 19.885
- type: ndcg_at_1
value: 15.038000000000002
- type: ndcg_at_10
value: 21.241
- type: ndcg_at_100
value: 26.179000000000002
- type: ndcg_at_1000
value: 29.316
- type: ndcg_at_3
value: 17.762
- type: ndcg_at_5
value: 19.413
- type: precision_at_1
value: 15.038000000000002
- type: precision_at_10
value: 3.8920000000000003
- type: precision_at_100
value: 0.75
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 8.351
- type: precision_at_5
value: 6.187
- type: recall_at_1
value: 12.327
- type: recall_at_10
value: 29.342000000000002
- type: recall_at_100
value: 51.854
- type: recall_at_1000
value: 74.648
- type: recall_at_3
value: 19.596
- type: recall_at_5
value: 23.899
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.594
- type: map_at_10
value: 27.878999999999998
- type: map_at_100
value: 28.926000000000002
- type: map_at_1000
value: 29.041
- type: map_at_3
value: 25.668999999999997
- type: map_at_5
value: 26.773999999999997
- type: mrr_at_1
value: 23.694000000000003
- type: mrr_at_10
value: 31.335
- type: mrr_at_100
value: 32.218
- type: mrr_at_1000
value: 32.298
- type: mrr_at_3
value: 29.26
- type: mrr_at_5
value: 30.328
- type: ndcg_at_1
value: 23.694000000000003
- type: ndcg_at_10
value: 32.456
- type: ndcg_at_100
value: 37.667
- type: ndcg_at_1000
value: 40.571
- type: ndcg_at_3
value: 28.283
- type: ndcg_at_5
value: 29.986
- type: precision_at_1
value: 23.694000000000003
- type: precision_at_10
value: 5.448
- type: precision_at_100
value: 0.9119999999999999
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 12.717999999999998
- type: precision_at_5
value: 8.843
- type: recall_at_1
value: 20.594
- type: recall_at_10
value: 43.004999999999995
- type: recall_at_100
value: 66.228
- type: recall_at_1000
value: 87.17099999999999
- type: recall_at_3
value: 31.554
- type: recall_at_5
value: 35.838
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.855999999999998
- type: map_at_10
value: 28.372000000000003
- type: map_at_100
value: 29.87
- type: map_at_1000
value: 30.075000000000003
- type: map_at_3
value: 26.054
- type: map_at_5
value: 27.128999999999998
- type: mrr_at_1
value: 25.494
- type: mrr_at_10
value: 32.735
- type: mrr_at_100
value: 33.794000000000004
- type: mrr_at_1000
value: 33.85
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 31.897
- type: ndcg_at_1
value: 25.494
- type: ndcg_at_10
value: 33.385
- type: ndcg_at_100
value: 39.436
- type: ndcg_at_1000
value: 42.313
- type: ndcg_at_3
value: 29.612
- type: ndcg_at_5
value: 31.186999999999998
- type: precision_at_1
value: 25.494
- type: precision_at_10
value: 6.422999999999999
- type: precision_at_100
value: 1.383
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 13.834
- type: precision_at_5
value: 10.0
- type: recall_at_1
value: 20.855999999999998
- type: recall_at_10
value: 42.678
- type: recall_at_100
value: 70.224
- type: recall_at_1000
value: 89.369
- type: recall_at_3
value: 31.957
- type: recall_at_5
value: 36.026
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.519000000000002
- type: map_at_10
value: 22.15
- type: map_at_100
value: 23.180999999999997
- type: map_at_1000
value: 23.291999999999998
- type: map_at_3
value: 20.132
- type: map_at_5
value: 21.346
- type: mrr_at_1
value: 17.93
- type: mrr_at_10
value: 23.506
- type: mrr_at_100
value: 24.581
- type: mrr_at_1000
value: 24.675
- type: mrr_at_3
value: 21.503
- type: mrr_at_5
value: 22.686
- type: ndcg_at_1
value: 17.93
- type: ndcg_at_10
value: 25.636
- type: ndcg_at_100
value: 30.736
- type: ndcg_at_1000
value: 33.841
- type: ndcg_at_3
value: 21.546000000000003
- type: ndcg_at_5
value: 23.658
- type: precision_at_1
value: 17.93
- type: precision_at_10
value: 3.993
- type: precision_at_100
value: 0.6890000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 9.057
- type: precision_at_5
value: 6.58
- type: recall_at_1
value: 16.519000000000002
- type: recall_at_10
value: 35.268
- type: recall_at_100
value: 58.17
- type: recall_at_1000
value: 81.66799999999999
- type: recall_at_3
value: 24.165
- type: recall_at_5
value: 29.254
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 10.363
- type: map_at_10
value: 18.301000000000002
- type: map_at_100
value: 20.019000000000002
- type: map_at_1000
value: 20.207
- type: map_at_3
value: 14.877
- type: map_at_5
value: 16.544
- type: mrr_at_1
value: 22.866
- type: mrr_at_10
value: 34.935
- type: mrr_at_100
value: 35.802
- type: mrr_at_1000
value: 35.839999999999996
- type: mrr_at_3
value: 30.965999999999998
- type: mrr_at_5
value: 33.204
- type: ndcg_at_1
value: 22.866
- type: ndcg_at_10
value: 26.595000000000002
- type: ndcg_at_100
value: 33.513999999999996
- type: ndcg_at_1000
value: 36.872
- type: ndcg_at_3
value: 20.666999999999998
- type: ndcg_at_5
value: 22.728
- type: precision_at_1
value: 22.866
- type: precision_at_10
value: 8.632
- type: precision_at_100
value: 1.6119999999999999
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 15.504999999999999
- type: precision_at_5
value: 12.404
- type: recall_at_1
value: 10.363
- type: recall_at_10
value: 33.494
- type: recall_at_100
value: 57.593
- type: recall_at_1000
value: 76.342
- type: recall_at_3
value: 19.157
- type: recall_at_5
value: 24.637999999999998
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 7.436
- type: map_at_10
value: 14.760000000000002
- type: map_at_100
value: 19.206
- type: map_at_1000
value: 20.267
- type: map_at_3
value: 10.894
- type: map_at_5
value: 12.828999999999999
- type: mrr_at_1
value: 54.25
- type: mrr_at_10
value: 63.769
- type: mrr_at_100
value: 64.193
- type: mrr_at_1000
value: 64.211
- type: mrr_at_3
value: 61.458
- type: mrr_at_5
value: 63.096
- type: ndcg_at_1
value: 42.875
- type: ndcg_at_10
value: 31.507
- type: ndcg_at_100
value: 34.559
- type: ndcg_at_1000
value: 41.246
- type: ndcg_at_3
value: 35.058
- type: ndcg_at_5
value: 33.396
- type: precision_at_1
value: 54.25
- type: precision_at_10
value: 24.45
- type: precision_at_100
value: 7.383000000000001
- type: precision_at_1000
value: 1.582
- type: precision_at_3
value: 38.083
- type: precision_at_5
value: 32.6
- type: recall_at_1
value: 7.436
- type: recall_at_10
value: 19.862
- type: recall_at_100
value: 38.981
- type: recall_at_1000
value: 61.038000000000004
- type: recall_at_3
value: 11.949
- type: recall_at_5
value: 15.562000000000001
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 46.39
- type: f1
value: 42.26424885856703
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 50.916
- type: map_at_10
value: 62.258
- type: map_at_100
value: 62.741
- type: map_at_1000
value: 62.763000000000005
- type: map_at_3
value: 60.01800000000001
- type: map_at_5
value: 61.419999999999995
- type: mrr_at_1
value: 54.964999999999996
- type: mrr_at_10
value: 66.554
- type: mrr_at_100
value: 66.96600000000001
- type: mrr_at_1000
value: 66.97800000000001
- type: mrr_at_3
value: 64.414
- type: mrr_at_5
value: 65.77
- type: ndcg_at_1
value: 54.964999999999996
- type: ndcg_at_10
value: 68.12
- type: ndcg_at_100
value: 70.282
- type: ndcg_at_1000
value: 70.788
- type: ndcg_at_3
value: 63.861999999999995
- type: ndcg_at_5
value: 66.216
- type: precision_at_1
value: 54.964999999999996
- type: precision_at_10
value: 8.998000000000001
- type: precision_at_100
value: 1.016
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 25.618000000000002
- type: precision_at_5
value: 16.676
- type: recall_at_1
value: 50.916
- type: recall_at_10
value: 82.04
- type: recall_at_100
value: 91.689
- type: recall_at_1000
value: 95.34899999999999
- type: recall_at_3
value: 70.512
- type: recall_at_5
value: 76.29899999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 13.568
- type: map_at_10
value: 23.264000000000003
- type: map_at_100
value: 24.823999999999998
- type: map_at_1000
value: 25.013999999999996
- type: map_at_3
value: 19.724
- type: map_at_5
value: 21.772
- type: mrr_at_1
value: 27.315
- type: mrr_at_10
value: 35.935
- type: mrr_at_100
value: 36.929
- type: mrr_at_1000
value: 36.985
- type: mrr_at_3
value: 33.591
- type: mrr_at_5
value: 34.848
- type: ndcg_at_1
value: 27.315
- type: ndcg_at_10
value: 29.988
- type: ndcg_at_100
value: 36.41
- type: ndcg_at_1000
value: 40.184999999999995
- type: ndcg_at_3
value: 26.342
- type: ndcg_at_5
value: 27.68
- type: precision_at_1
value: 27.315
- type: precision_at_10
value: 8.565000000000001
- type: precision_at_100
value: 1.508
- type: precision_at_1000
value: 0.219
- type: precision_at_3
value: 17.849999999999998
- type: precision_at_5
value: 13.672999999999998
- type: recall_at_1
value: 13.568
- type: recall_at_10
value: 37.133
- type: recall_at_100
value: 61.475
- type: recall_at_1000
value: 84.372
- type: recall_at_3
value: 24.112000000000002
- type: recall_at_5
value: 29.507
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 30.878
- type: map_at_10
value: 40.868
- type: map_at_100
value: 41.693999999999996
- type: map_at_1000
value: 41.775
- type: map_at_3
value: 38.56
- type: map_at_5
value: 39.947
- type: mrr_at_1
value: 61.756
- type: mrr_at_10
value: 68.265
- type: mrr_at_100
value: 68.671
- type: mrr_at_1000
value: 68.694
- type: mrr_at_3
value: 66.78399999999999
- type: mrr_at_5
value: 67.704
- type: ndcg_at_1
value: 61.756
- type: ndcg_at_10
value: 49.931
- type: ndcg_at_100
value: 53.179
- type: ndcg_at_1000
value: 54.94799999999999
- type: ndcg_at_3
value: 46.103
- type: ndcg_at_5
value: 48.147
- type: precision_at_1
value: 61.756
- type: precision_at_10
value: 10.163
- type: precision_at_100
value: 1.2710000000000001
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 28.179
- type: precision_at_5
value: 18.528
- type: recall_at_1
value: 30.878
- type: recall_at_10
value: 50.817
- type: recall_at_100
value: 63.544999999999995
- type: recall_at_1000
value: 75.361
- type: recall_at_3
value: 42.269
- type: recall_at_5
value: 46.32
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 64.04799999999999
- type: ap
value: 59.185251455339284
- type: f1
value: 63.947123181349255
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 18.9
- type: map_at_10
value: 29.748
- type: map_at_100
value: 30.976
- type: map_at_1000
value: 31.041
- type: map_at_3
value: 26.112999999999996
- type: map_at_5
value: 28.197
- type: mrr_at_1
value: 19.413
- type: mrr_at_10
value: 30.322
- type: mrr_at_100
value: 31.497000000000003
- type: mrr_at_1000
value: 31.555
- type: mrr_at_3
value: 26.729000000000003
- type: mrr_at_5
value: 28.788999999999998
- type: ndcg_at_1
value: 19.413
- type: ndcg_at_10
value: 36.048
- type: ndcg_at_100
value: 42.152
- type: ndcg_at_1000
value: 43.772
- type: ndcg_at_3
value: 28.642
- type: ndcg_at_5
value: 32.358
- type: precision_at_1
value: 19.413
- type: precision_at_10
value: 5.785
- type: precision_at_100
value: 0.8869999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.192
- type: precision_at_5
value: 9.189
- type: recall_at_1
value: 18.9
- type: recall_at_10
value: 55.457
- type: recall_at_100
value: 84.09100000000001
- type: recall_at_1000
value: 96.482
- type: recall_at_3
value: 35.359
- type: recall_at_5
value: 44.275
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 92.07706338349293
- type: f1
value: 91.56680443236652
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 71.18559051527589
- type: f1
value: 52.42887061726789
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 68.64828513786148
- type: f1
value: 66.54281381596097
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.04236718224612
- type: f1
value: 75.89170458655639
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 32.0840369055247
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 29.448729560244537
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.340856463122375
- type: mrr
value: 32.398547669840916
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 5.526
- type: map_at_10
value: 11.745
- type: map_at_100
value: 14.831
- type: map_at_1000
value: 16.235
- type: map_at_3
value: 8.716
- type: map_at_5
value: 10.101
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 51.06699999999999
- type: mrr_at_100
value: 51.881
- type: mrr_at_1000
value: 51.912000000000006
- type: mrr_at_3
value: 49.02
- type: mrr_at_5
value: 50.288999999999994
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 32.083
- type: ndcg_at_100
value: 30.049999999999997
- type: ndcg_at_1000
value: 38.661
- type: ndcg_at_3
value: 37.940000000000005
- type: ndcg_at_5
value: 35.455999999999996
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 23.437
- type: precision_at_100
value: 7.829999999999999
- type: precision_at_1000
value: 2.053
- type: precision_at_3
value: 35.501
- type: precision_at_5
value: 30.464000000000002
- type: recall_at_1
value: 5.526
- type: recall_at_10
value: 15.445999999999998
- type: recall_at_100
value: 31.179000000000002
- type: recall_at_1000
value: 61.578
- type: recall_at_3
value: 9.71
- type: recall_at_5
value: 12.026
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 23.467
- type: map_at_10
value: 36.041000000000004
- type: map_at_100
value: 37.268
- type: map_at_1000
value: 37.322
- type: map_at_3
value: 32.09
- type: map_at_5
value: 34.414
- type: mrr_at_1
value: 26.738
- type: mrr_at_10
value: 38.665
- type: mrr_at_100
value: 39.64
- type: mrr_at_1000
value: 39.681
- type: mrr_at_3
value: 35.207
- type: mrr_at_5
value: 37.31
- type: ndcg_at_1
value: 26.709
- type: ndcg_at_10
value: 42.942
- type: ndcg_at_100
value: 48.296
- type: ndcg_at_1000
value: 49.651
- type: ndcg_at_3
value: 35.413
- type: ndcg_at_5
value: 39.367999999999995
- type: precision_at_1
value: 26.709
- type: precision_at_10
value: 7.306
- type: precision_at_100
value: 1.0290000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 16.348
- type: precision_at_5
value: 12.068
- type: recall_at_1
value: 23.467
- type: recall_at_10
value: 61.492999999999995
- type: recall_at_100
value: 85.01100000000001
- type: recall_at_1000
value: 95.261
- type: recall_at_3
value: 41.952
- type: recall_at_5
value: 51.105999999999995
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 67.51700000000001
- type: map_at_10
value: 81.054
- type: map_at_100
value: 81.727
- type: map_at_1000
value: 81.75200000000001
- type: map_at_3
value: 78.018
- type: map_at_5
value: 79.879
- type: mrr_at_1
value: 77.52
- type: mrr_at_10
value: 84.429
- type: mrr_at_100
value: 84.58200000000001
- type: mrr_at_1000
value: 84.584
- type: mrr_at_3
value: 83.268
- type: mrr_at_5
value: 84.013
- type: ndcg_at_1
value: 77.53
- type: ndcg_at_10
value: 85.277
- type: ndcg_at_100
value: 86.80499999999999
- type: ndcg_at_1000
value: 87.01
- type: ndcg_at_3
value: 81.975
- type: ndcg_at_5
value: 83.723
- type: precision_at_1
value: 77.53
- type: precision_at_10
value: 12.961
- type: precision_at_100
value: 1.502
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.713
- type: precision_at_5
value: 23.574
- type: recall_at_1
value: 67.51700000000001
- type: recall_at_10
value: 93.486
- type: recall_at_100
value: 98.9
- type: recall_at_1000
value: 99.92999999999999
- type: recall_at_3
value: 84.17999999999999
- type: recall_at_5
value: 88.97500000000001
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 48.225994608749915
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 53.17635557157765
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 3.988
- type: map_at_10
value: 9.4
- type: map_at_100
value: 10.968
- type: map_at_1000
value: 11.257
- type: map_at_3
value: 7.123
- type: map_at_5
value: 8.221
- type: mrr_at_1
value: 19.7
- type: mrr_at_10
value: 29.098000000000003
- type: mrr_at_100
value: 30.247
- type: mrr_at_1000
value: 30.318
- type: mrr_at_3
value: 26.55
- type: mrr_at_5
value: 27.915
- type: ndcg_at_1
value: 19.7
- type: ndcg_at_10
value: 16.176
- type: ndcg_at_100
value: 22.931
- type: ndcg_at_1000
value: 28.301
- type: ndcg_at_3
value: 16.142
- type: ndcg_at_5
value: 13.633999999999999
- type: precision_at_1
value: 19.7
- type: precision_at_10
value: 8.18
- type: precision_at_100
value: 1.8010000000000002
- type: precision_at_1000
value: 0.309
- type: precision_at_3
value: 15.1
- type: precision_at_5
value: 11.74
- type: recall_at_1
value: 3.988
- type: recall_at_10
value: 16.625
- type: recall_at_100
value: 36.61
- type: recall_at_1000
value: 62.805
- type: recall_at_3
value: 9.168
- type: recall_at_5
value: 11.902
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 77.29330379162072
- type: cos_sim_spearman
value: 67.22953551111448
- type: euclidean_pearson
value: 71.44682700059415
- type: euclidean_spearman
value: 66.33178012153247
- type: manhattan_pearson
value: 71.46941734657887
- type: manhattan_spearman
value: 66.43234359835814
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 75.40943196466576
- type: cos_sim_spearman
value: 66.59241013465915
- type: euclidean_pearson
value: 71.32500540796616
- type: euclidean_spearman
value: 67.86667467202591
- type: manhattan_pearson
value: 71.48209832089134
- type: manhattan_spearman
value: 67.94511626964879
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 77.08302398877518
- type: cos_sim_spearman
value: 77.33151317062642
- type: euclidean_pearson
value: 76.77020279715008
- type: euclidean_spearman
value: 77.13893776083225
- type: manhattan_pearson
value: 76.76732290707477
- type: manhattan_spearman
value: 77.14500877396631
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 77.46886184932168
- type: cos_sim_spearman
value: 71.82815265534886
- type: euclidean_pearson
value: 75.19783284299076
- type: euclidean_spearman
value: 71.36479611710412
- type: manhattan_pearson
value: 75.30375233959337
- type: manhattan_spearman
value: 71.46280266488021
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 80.093017609484
- type: cos_sim_spearman
value: 80.65931167868882
- type: euclidean_pearson
value: 80.36786337117047
- type: euclidean_spearman
value: 81.30521389642827
- type: manhattan_pearson
value: 80.37922433220973
- type: manhattan_spearman
value: 81.30496664496285
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 77.98998347238742
- type: cos_sim_spearman
value: 78.91151365939403
- type: euclidean_pearson
value: 76.40510899217841
- type: euclidean_spearman
value: 76.8551459824213
- type: manhattan_pearson
value: 76.3986079603294
- type: manhattan_spearman
value: 76.8848053254288
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 85.63510653472044
- type: cos_sim_spearman
value: 86.98674844768605
- type: euclidean_pearson
value: 85.205080538809
- type: euclidean_spearman
value: 85.53630494151886
- type: manhattan_pearson
value: 85.48612469885626
- type: manhattan_spearman
value: 85.81741413931921
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 66.7257987615171
- type: cos_sim_spearman
value: 67.30387805090024
- type: euclidean_pearson
value: 69.46877227885867
- type: euclidean_spearman
value: 69.33161798704344
- type: manhattan_pearson
value: 69.82773311626424
- type: manhattan_spearman
value: 69.57199940498796
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 79.37322139418472
- type: cos_sim_spearman
value: 77.5887175717799
- type: euclidean_pearson
value: 78.23006410562164
- type: euclidean_spearman
value: 77.18470385673044
- type: manhattan_pearson
value: 78.40868369362455
- type: manhattan_spearman
value: 77.36675823897656
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 77.21233007730808
- type: mrr
value: 93.0502386139641
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 54.567
- type: map_at_10
value: 63.653000000000006
- type: map_at_100
value: 64.282
- type: map_at_1000
value: 64.31099999999999
- type: map_at_3
value: 60.478
- type: map_at_5
value: 62.322
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 64.759
- type: mrr_at_100
value: 65.274
- type: mrr_at_1000
value: 65.301
- type: mrr_at_3
value: 62.333000000000006
- type: mrr_at_5
value: 63.817
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 68.28699999999999
- type: ndcg_at_100
value: 70.98400000000001
- type: ndcg_at_1000
value: 71.695
- type: ndcg_at_3
value: 62.656
- type: ndcg_at_5
value: 65.523
- type: precision_at_1
value: 56.99999999999999
- type: precision_at_10
value: 9.232999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.333000000000002
- type: recall_at_1
value: 54.567
- type: recall_at_10
value: 81.45599999999999
- type: recall_at_100
value: 93.5
- type: recall_at_1000
value: 99.0
- type: recall_at_3
value: 66.228
- type: recall_at_5
value: 73.489
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.74455445544554
- type: cos_sim_ap
value: 92.57836032673468
- type: cos_sim_f1
value: 87.0471464019851
- type: cos_sim_precision
value: 86.4039408866995
- type: cos_sim_recall
value: 87.7
- type: dot_accuracy
value: 99.56039603960396
- type: dot_ap
value: 82.47233353407186
- type: dot_f1
value: 76.78207739307537
- type: dot_precision
value: 78.21576763485477
- type: dot_recall
value: 75.4
- type: euclidean_accuracy
value: 99.73069306930694
- type: euclidean_ap
value: 91.70507666665775
- type: euclidean_f1
value: 86.26262626262626
- type: euclidean_precision
value: 87.14285714285714
- type: euclidean_recall
value: 85.39999999999999
- type: manhattan_accuracy
value: 99.73861386138614
- type: manhattan_ap
value: 91.96809459281754
- type: manhattan_f1
value: 86.6
- type: manhattan_precision
value: 86.6
- type: manhattan_recall
value: 86.6
- type: max_accuracy
value: 99.74455445544554
- type: max_ap
value: 92.57836032673468
- type: max_f1
value: 87.0471464019851
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 60.85593925770172
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 32.356772998237496
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 49.320607035290735
- type: mrr
value: 50.09196481622952
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 31.17573968015504
- type: cos_sim_spearman
value: 30.43371643155132
- type: dot_pearson
value: 30.164319483092744
- type: dot_spearman
value: 29.207082242868754
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.22100000000000003
- type: map_at_10
value: 1.7229999999999999
- type: map_at_100
value: 9.195
- type: map_at_1000
value: 21.999
- type: map_at_3
value: 0.6479999999999999
- type: map_at_5
value: 0.964
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 90.667
- type: mrr_at_100
value: 90.858
- type: mrr_at_1000
value: 90.858
- type: mrr_at_3
value: 90.667
- type: mrr_at_5
value: 90.667
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 72.98
- type: ndcg_at_100
value: 52.868
- type: ndcg_at_1000
value: 46.541
- type: ndcg_at_3
value: 80.39699999999999
- type: ndcg_at_5
value: 76.303
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 75.8
- type: precision_at_100
value: 53.5
- type: precision_at_1000
value: 20.946
- type: precision_at_3
value: 85.333
- type: precision_at_5
value: 79.2
- type: recall_at_1
value: 0.22100000000000003
- type: recall_at_10
value: 1.9109999999999998
- type: recall_at_100
value: 12.437
- type: recall_at_1000
value: 43.606
- type: recall_at_3
value: 0.681
- type: recall_at_5
value: 1.023
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 2.5
- type: map_at_10
value: 9.568999999999999
- type: map_at_100
value: 15.653
- type: map_at_1000
value: 17.188
- type: map_at_3
value: 5.335999999999999
- type: map_at_5
value: 6.522
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 49.184
- type: mrr_at_100
value: 50.512
- type: mrr_at_1000
value: 50.512
- type: mrr_at_3
value: 46.259
- type: mrr_at_5
value: 48.299
- type: ndcg_at_1
value: 30.612000000000002
- type: ndcg_at_10
value: 24.45
- type: ndcg_at_100
value: 35.870999999999995
- type: ndcg_at_1000
value: 47.272999999999996
- type: ndcg_at_3
value: 28.528
- type: ndcg_at_5
value: 25.768
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 21.429000000000002
- type: precision_at_100
value: 7.265000000000001
- type: precision_at_1000
value: 1.504
- type: precision_at_3
value: 29.252
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 2.5
- type: recall_at_10
value: 15.844
- type: recall_at_100
value: 45.469
- type: recall_at_1000
value: 81.148
- type: recall_at_3
value: 6.496
- type: recall_at_5
value: 8.790000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 68.7272
- type: ap
value: 13.156450706152686
- type: f1
value: 52.814703437064395
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 55.6677985285795
- type: f1
value: 55.9373937514999
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 40.05809562275603
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.76807534124099
- type: cos_sim_ap
value: 62.37052608803734
- type: cos_sim_f1
value: 59.077414934916646
- type: cos_sim_precision
value: 52.07326892109501
- type: cos_sim_recall
value: 68.25857519788919
- type: dot_accuracy
value: 80.56267509089825
- type: dot_ap
value: 54.75349561321037
- type: dot_f1
value: 54.75483794372552
- type: dot_precision
value: 49.77336499028707
- type: dot_recall
value: 60.844327176781
- type: euclidean_accuracy
value: 82.476008821601
- type: euclidean_ap
value: 61.17417554210511
- type: euclidean_f1
value: 57.80318696022382
- type: euclidean_precision
value: 53.622207176709544
- type: euclidean_recall
value: 62.69129287598945
- type: manhattan_accuracy
value: 82.48792990403528
- type: manhattan_ap
value: 61.044816292966544
- type: manhattan_f1
value: 58.03033951360462
- type: manhattan_precision
value: 53.36581045172719
- type: manhattan_recall
value: 63.58839050131926
- type: max_accuracy
value: 82.76807534124099
- type: max_ap
value: 62.37052608803734
- type: max_f1
value: 59.077414934916646
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.97881010594946
- type: cos_sim_ap
value: 83.78748636891035
- type: cos_sim_f1
value: 75.94113995691386
- type: cos_sim_precision
value: 72.22029307590805
- type: cos_sim_recall
value: 80.06621496766245
- type: dot_accuracy
value: 85.69294058291614
- type: dot_ap
value: 78.15363722278026
- type: dot_f1
value: 72.08894926888564
- type: dot_precision
value: 67.28959487419075
- type: dot_recall
value: 77.62550046196489
- type: euclidean_accuracy
value: 87.73625179493149
- type: euclidean_ap
value: 83.19012184470559
- type: euclidean_f1
value: 75.5148064623461
- type: euclidean_precision
value: 72.63352535381551
- type: euclidean_recall
value: 78.6341238065907
- type: manhattan_accuracy
value: 87.74013272790779
- type: manhattan_ap
value: 83.23305405113403
- type: manhattan_f1
value: 75.63960775639607
- type: manhattan_precision
value: 72.563304569246
- type: manhattan_recall
value: 78.9882968894364
- type: max_accuracy
value: 87.97881010594946
- type: max_ap
value: 83.78748636891035
- type: max_f1
value: 75.94113995691386
---
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 62398 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
alesthehuman/ppo-LunarLander-v2
|
alesthehuman
| 2023-03-27T22:21:26Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T22:21:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.19 +/- 14.82
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit
|
Muennighoff
| 2023-03-27T22:19:34Z | 1,244 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2202.08904",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: SGPT-125M-weightedmean-msmarco-specb-bitfit
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 61.23880597014926
- type: ap
value: 25.854431650388644
- type: f1
value: 55.751862762818604
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 56.88436830835117
- type: ap
value: 72.67279104379772
- type: f1
value: 54.449840243786404
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 58.27586206896551
- type: ap
value: 14.067357642500387
- type: f1
value: 48.172318518691334
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
metrics:
- type: accuracy
value: 54.64668094218415
- type: ap
value: 11.776694555054965
- type: f1
value: 44.526622834078765
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
metrics:
- type: accuracy
value: 65.401225
- type: ap
value: 60.22809958678552
- type: f1
value: 65.0251824898292
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 31.165999999999993
- type: f1
value: 30.908870050167437
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 24.79
- type: f1
value: 24.5833598854121
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 26.643999999999995
- type: f1
value: 26.39012792213563
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 26.386000000000003
- type: f1
value: 26.276867791454873
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 22.078000000000003
- type: f1
value: 21.797960290226843
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: c379a6705fec24a2493fa68e011692605f44e119
metrics:
- type: accuracy
value: 24.274
- type: f1
value: 23.887054434822627
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
metrics:
- type: map_at_1
value: 22.404
- type: map_at_10
value: 36.845
- type: map_at_100
value: 37.945
- type: map_at_1000
value: 37.966
- type: map_at_3
value: 31.78
- type: map_at_5
value: 34.608
- type: mrr_at_1
value: 22.902
- type: mrr_at_10
value: 37.034
- type: mrr_at_100
value: 38.134
- type: mrr_at_1000
value: 38.155
- type: mrr_at_3
value: 31.935000000000002
- type: mrr_at_5
value: 34.812
- type: ndcg_at_1
value: 22.404
- type: ndcg_at_10
value: 45.425
- type: ndcg_at_100
value: 50.354
- type: ndcg_at_1000
value: 50.873999999999995
- type: ndcg_at_3
value: 34.97
- type: ndcg_at_5
value: 40.081
- type: precision_at_1
value: 22.404
- type: precision_at_10
value: 7.303999999999999
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.746
- type: precision_at_5
value: 11.337
- type: recall_at_1
value: 22.404
- type: recall_at_10
value: 73.044
- type: recall_at_100
value: 95.092
- type: recall_at_1000
value: 99.075
- type: recall_at_3
value: 44.239
- type: recall_at_5
value: 56.686
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
metrics:
- type: v_measure
value: 39.70858340673288
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
metrics:
- type: v_measure
value: 28.242847713721048
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
metrics:
- type: map
value: 55.83700395192393
- type: mrr
value: 70.3891307215407
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: 9ee918f184421b6bd48b78f6c714d86546106103
metrics:
- type: cos_sim_pearson
value: 79.25366801756223
- type: cos_sim_spearman
value: 75.20954502580506
- type: euclidean_pearson
value: 78.79900722991617
- type: euclidean_spearman
value: 77.79996549607588
- type: manhattan_pearson
value: 78.18408109480399
- type: manhattan_spearman
value: 76.85958262303106
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
metrics:
- type: accuracy
value: 77.70454545454545
- type: f1
value: 77.6929000113803
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
metrics:
- type: v_measure
value: 33.63260395543984
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
metrics:
- type: v_measure
value: 27.038042665369925
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 22.139
- type: map_at_10
value: 28.839
- type: map_at_100
value: 30.023
- type: map_at_1000
value: 30.153000000000002
- type: map_at_3
value: 26.521
- type: map_at_5
value: 27.775
- type: mrr_at_1
value: 26.466
- type: mrr_at_10
value: 33.495000000000005
- type: mrr_at_100
value: 34.416999999999994
- type: mrr_at_1000
value: 34.485
- type: mrr_at_3
value: 31.402
- type: mrr_at_5
value: 32.496
- type: ndcg_at_1
value: 26.466
- type: ndcg_at_10
value: 33.372
- type: ndcg_at_100
value: 38.7
- type: ndcg_at_1000
value: 41.696
- type: ndcg_at_3
value: 29.443
- type: ndcg_at_5
value: 31.121
- type: precision_at_1
value: 26.466
- type: precision_at_10
value: 6.037
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 13.782
- type: precision_at_5
value: 9.757
- type: recall_at_1
value: 22.139
- type: recall_at_10
value: 42.39
- type: recall_at_100
value: 65.427
- type: recall_at_1000
value: 86.04899999999999
- type: recall_at_3
value: 31.127
- type: recall_at_5
value: 35.717999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 20.652
- type: map_at_10
value: 27.558
- type: map_at_100
value: 28.473
- type: map_at_1000
value: 28.577
- type: map_at_3
value: 25.402
- type: map_at_5
value: 26.68
- type: mrr_at_1
value: 25.223000000000003
- type: mrr_at_10
value: 31.966
- type: mrr_at_100
value: 32.664
- type: mrr_at_1000
value: 32.724
- type: mrr_at_3
value: 30.074
- type: mrr_at_5
value: 31.249
- type: ndcg_at_1
value: 25.223000000000003
- type: ndcg_at_10
value: 31.694
- type: ndcg_at_100
value: 35.662
- type: ndcg_at_1000
value: 38.092
- type: ndcg_at_3
value: 28.294000000000004
- type: ndcg_at_5
value: 30.049
- type: precision_at_1
value: 25.223000000000003
- type: precision_at_10
value: 5.777
- type: precision_at_100
value: 0.9730000000000001
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 13.397
- type: precision_at_5
value: 9.605
- type: recall_at_1
value: 20.652
- type: recall_at_10
value: 39.367999999999995
- type: recall_at_100
value: 56.485
- type: recall_at_1000
value: 73.292
- type: recall_at_3
value: 29.830000000000002
- type: recall_at_5
value: 34.43
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 25.180000000000003
- type: map_at_10
value: 34.579
- type: map_at_100
value: 35.589999999999996
- type: map_at_1000
value: 35.68
- type: map_at_3
value: 31.735999999999997
- type: map_at_5
value: 33.479
- type: mrr_at_1
value: 29.467
- type: mrr_at_10
value: 37.967
- type: mrr_at_100
value: 38.800000000000004
- type: mrr_at_1000
value: 38.858
- type: mrr_at_3
value: 35.465
- type: mrr_at_5
value: 37.057
- type: ndcg_at_1
value: 29.467
- type: ndcg_at_10
value: 39.796
- type: ndcg_at_100
value: 44.531
- type: ndcg_at_1000
value: 46.666000000000004
- type: ndcg_at_3
value: 34.676
- type: ndcg_at_5
value: 37.468
- type: precision_at_1
value: 29.467
- type: precision_at_10
value: 6.601999999999999
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 15.568999999999999
- type: precision_at_5
value: 11.172
- type: recall_at_1
value: 25.180000000000003
- type: recall_at_10
value: 52.269
- type: recall_at_100
value: 73.574
- type: recall_at_1000
value: 89.141
- type: recall_at_3
value: 38.522
- type: recall_at_5
value: 45.323
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.303
- type: map_at_10
value: 21.629
- type: map_at_100
value: 22.387999999999998
- type: map_at_1000
value: 22.489
- type: map_at_3
value: 19.608
- type: map_at_5
value: 20.774
- type: mrr_at_1
value: 17.740000000000002
- type: mrr_at_10
value: 23.214000000000002
- type: mrr_at_100
value: 23.97
- type: mrr_at_1000
value: 24.054000000000002
- type: mrr_at_3
value: 21.243000000000002
- type: mrr_at_5
value: 22.322
- type: ndcg_at_1
value: 17.740000000000002
- type: ndcg_at_10
value: 25.113000000000003
- type: ndcg_at_100
value: 29.287999999999997
- type: ndcg_at_1000
value: 32.204
- type: ndcg_at_3
value: 21.111
- type: ndcg_at_5
value: 23.061999999999998
- type: precision_at_1
value: 17.740000000000002
- type: precision_at_10
value: 3.955
- type: precision_at_100
value: 0.644
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 8.851
- type: precision_at_5
value: 6.418
- type: recall_at_1
value: 16.303
- type: recall_at_10
value: 34.487
- type: recall_at_100
value: 54.413999999999994
- type: recall_at_1000
value: 77.158
- type: recall_at_3
value: 23.733
- type: recall_at_5
value: 28.381
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 10.133000000000001
- type: map_at_10
value: 15.665999999999999
- type: map_at_100
value: 16.592000000000002
- type: map_at_1000
value: 16.733999999999998
- type: map_at_3
value: 13.625000000000002
- type: map_at_5
value: 14.721
- type: mrr_at_1
value: 12.562000000000001
- type: mrr_at_10
value: 18.487000000000002
- type: mrr_at_100
value: 19.391
- type: mrr_at_1000
value: 19.487
- type: mrr_at_3
value: 16.418
- type: mrr_at_5
value: 17.599999999999998
- type: ndcg_at_1
value: 12.562000000000001
- type: ndcg_at_10
value: 19.43
- type: ndcg_at_100
value: 24.546
- type: ndcg_at_1000
value: 28.193
- type: ndcg_at_3
value: 15.509999999999998
- type: ndcg_at_5
value: 17.322000000000003
- type: precision_at_1
value: 12.562000000000001
- type: precision_at_10
value: 3.794
- type: precision_at_100
value: 0.74
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 7.546
- type: precision_at_5
value: 5.721
- type: recall_at_1
value: 10.133000000000001
- type: recall_at_10
value: 28.261999999999997
- type: recall_at_100
value: 51.742999999999995
- type: recall_at_1000
value: 78.075
- type: recall_at_3
value: 17.634
- type: recall_at_5
value: 22.128999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 19.991999999999997
- type: map_at_10
value: 27.346999999999998
- type: map_at_100
value: 28.582
- type: map_at_1000
value: 28.716
- type: map_at_3
value: 24.907
- type: map_at_5
value: 26.1
- type: mrr_at_1
value: 23.773
- type: mrr_at_10
value: 31.647
- type: mrr_at_100
value: 32.639
- type: mrr_at_1000
value: 32.706
- type: mrr_at_3
value: 29.195
- type: mrr_at_5
value: 30.484
- type: ndcg_at_1
value: 23.773
- type: ndcg_at_10
value: 32.322
- type: ndcg_at_100
value: 37.996
- type: ndcg_at_1000
value: 40.819
- type: ndcg_at_3
value: 27.876
- type: ndcg_at_5
value: 29.664
- type: precision_at_1
value: 23.773
- type: precision_at_10
value: 5.976999999999999
- type: precision_at_100
value: 1.055
- type: precision_at_1000
value: 0.15
- type: precision_at_3
value: 13.122
- type: precision_at_5
value: 9.451
- type: recall_at_1
value: 19.991999999999997
- type: recall_at_10
value: 43.106
- type: recall_at_100
value: 67.264
- type: recall_at_1000
value: 86.386
- type: recall_at_3
value: 30.392000000000003
- type: recall_at_5
value: 34.910999999999994
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 17.896
- type: map_at_10
value: 24.644
- type: map_at_100
value: 25.790000000000003
- type: map_at_1000
value: 25.913999999999998
- type: map_at_3
value: 22.694
- type: map_at_5
value: 23.69
- type: mrr_at_1
value: 21.346999999999998
- type: mrr_at_10
value: 28.594
- type: mrr_at_100
value: 29.543999999999997
- type: mrr_at_1000
value: 29.621
- type: mrr_at_3
value: 26.807
- type: mrr_at_5
value: 27.669
- type: ndcg_at_1
value: 21.346999999999998
- type: ndcg_at_10
value: 28.833
- type: ndcg_at_100
value: 34.272000000000006
- type: ndcg_at_1000
value: 37.355
- type: ndcg_at_3
value: 25.373
- type: ndcg_at_5
value: 26.756
- type: precision_at_1
value: 21.346999999999998
- type: precision_at_10
value: 5.2170000000000005
- type: precision_at_100
value: 0.954
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 11.948
- type: precision_at_5
value: 8.425
- type: recall_at_1
value: 17.896
- type: recall_at_10
value: 37.291000000000004
- type: recall_at_100
value: 61.138000000000005
- type: recall_at_1000
value: 83.212
- type: recall_at_3
value: 27.705999999999996
- type: recall_at_5
value: 31.234
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 17.195166666666665
- type: map_at_10
value: 23.329083333333333
- type: map_at_100
value: 24.30308333333333
- type: map_at_1000
value: 24.422416666666667
- type: map_at_3
value: 21.327416666666664
- type: map_at_5
value: 22.419999999999998
- type: mrr_at_1
value: 19.999916666666667
- type: mrr_at_10
value: 26.390166666666666
- type: mrr_at_100
value: 27.230999999999998
- type: mrr_at_1000
value: 27.308333333333334
- type: mrr_at_3
value: 24.4675
- type: mrr_at_5
value: 25.541083333333336
- type: ndcg_at_1
value: 19.999916666666667
- type: ndcg_at_10
value: 27.248666666666665
- type: ndcg_at_100
value: 32.00258333333334
- type: ndcg_at_1000
value: 34.9465
- type: ndcg_at_3
value: 23.58566666666667
- type: ndcg_at_5
value: 25.26341666666666
- type: precision_at_1
value: 19.999916666666667
- type: precision_at_10
value: 4.772166666666666
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.12741666666666668
- type: precision_at_3
value: 10.756166666666669
- type: precision_at_5
value: 7.725416666666667
- type: recall_at_1
value: 17.195166666666665
- type: recall_at_10
value: 35.99083333333334
- type: recall_at_100
value: 57.467999999999996
- type: recall_at_1000
value: 78.82366666666667
- type: recall_at_3
value: 25.898499999999995
- type: recall_at_5
value: 30.084333333333333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.779
- type: map_at_10
value: 21.557000000000002
- type: map_at_100
value: 22.338
- type: map_at_1000
value: 22.421
- type: map_at_3
value: 19.939
- type: map_at_5
value: 20.903
- type: mrr_at_1
value: 18.404999999999998
- type: mrr_at_10
value: 23.435
- type: mrr_at_100
value: 24.179000000000002
- type: mrr_at_1000
value: 24.25
- type: mrr_at_3
value: 21.907
- type: mrr_at_5
value: 22.781000000000002
- type: ndcg_at_1
value: 18.404999999999998
- type: ndcg_at_10
value: 24.515
- type: ndcg_at_100
value: 28.721000000000004
- type: ndcg_at_1000
value: 31.259999999999998
- type: ndcg_at_3
value: 21.508
- type: ndcg_at_5
value: 23.01
- type: precision_at_1
value: 18.404999999999998
- type: precision_at_10
value: 3.834
- type: precision_at_100
value: 0.641
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 9.151
- type: precision_at_5
value: 6.503
- type: recall_at_1
value: 16.779
- type: recall_at_10
value: 31.730000000000004
- type: recall_at_100
value: 51.673
- type: recall_at_1000
value: 71.17599999999999
- type: recall_at_3
value: 23.518
- type: recall_at_5
value: 27.230999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 9.279
- type: map_at_10
value: 13.822000000000001
- type: map_at_100
value: 14.533
- type: map_at_1000
value: 14.649999999999999
- type: map_at_3
value: 12.396
- type: map_at_5
value: 13.214
- type: mrr_at_1
value: 11.149000000000001
- type: mrr_at_10
value: 16.139
- type: mrr_at_100
value: 16.872
- type: mrr_at_1000
value: 16.964000000000002
- type: mrr_at_3
value: 14.613000000000001
- type: mrr_at_5
value: 15.486
- type: ndcg_at_1
value: 11.149000000000001
- type: ndcg_at_10
value: 16.82
- type: ndcg_at_100
value: 20.73
- type: ndcg_at_1000
value: 23.894000000000002
- type: ndcg_at_3
value: 14.11
- type: ndcg_at_5
value: 15.404000000000002
- type: precision_at_1
value: 11.149000000000001
- type: precision_at_10
value: 3.063
- type: precision_at_100
value: 0.587
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 6.699
- type: precision_at_5
value: 4.928
- type: recall_at_1
value: 9.279
- type: recall_at_10
value: 23.745
- type: recall_at_100
value: 41.873
- type: recall_at_1000
value: 64.982
- type: recall_at_3
value: 16.152
- type: recall_at_5
value: 19.409000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 16.36
- type: map_at_10
value: 21.927
- type: map_at_100
value: 22.889
- type: map_at_1000
value: 22.994
- type: map_at_3
value: 20.433
- type: map_at_5
value: 21.337
- type: mrr_at_1
value: 18.75
- type: mrr_at_10
value: 24.859
- type: mrr_at_100
value: 25.746999999999996
- type: mrr_at_1000
value: 25.829
- type: mrr_at_3
value: 23.383000000000003
- type: mrr_at_5
value: 24.297
- type: ndcg_at_1
value: 18.75
- type: ndcg_at_10
value: 25.372
- type: ndcg_at_100
value: 30.342999999999996
- type: ndcg_at_1000
value: 33.286
- type: ndcg_at_3
value: 22.627
- type: ndcg_at_5
value: 24.04
- type: precision_at_1
value: 18.75
- type: precision_at_10
value: 4.1419999999999995
- type: precision_at_100
value: 0.738
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 10.261000000000001
- type: precision_at_5
value: 7.164
- type: recall_at_1
value: 16.36
- type: recall_at_10
value: 32.949
- type: recall_at_100
value: 55.552
- type: recall_at_1000
value: 77.09899999999999
- type: recall_at_3
value: 25.538
- type: recall_at_5
value: 29.008
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 17.39
- type: map_at_10
value: 23.058
- type: map_at_100
value: 24.445
- type: map_at_1000
value: 24.637999999999998
- type: map_at_3
value: 21.037
- type: map_at_5
value: 21.966
- type: mrr_at_1
value: 19.96
- type: mrr_at_10
value: 26.301000000000002
- type: mrr_at_100
value: 27.297
- type: mrr_at_1000
value: 27.375
- type: mrr_at_3
value: 24.340999999999998
- type: mrr_at_5
value: 25.339
- type: ndcg_at_1
value: 19.96
- type: ndcg_at_10
value: 27.249000000000002
- type: ndcg_at_100
value: 32.997
- type: ndcg_at_1000
value: 36.359
- type: ndcg_at_3
value: 23.519000000000002
- type: ndcg_at_5
value: 24.915000000000003
- type: precision_at_1
value: 19.96
- type: precision_at_10
value: 5.356000000000001
- type: precision_at_100
value: 1.198
- type: precision_at_1000
value: 0.20400000000000001
- type: precision_at_3
value: 10.738
- type: precision_at_5
value: 7.904999999999999
- type: recall_at_1
value: 17.39
- type: recall_at_10
value: 35.254999999999995
- type: recall_at_100
value: 61.351
- type: recall_at_1000
value: 84.395
- type: recall_at_3
value: 25.194
- type: recall_at_5
value: 28.546
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
metrics:
- type: map_at_1
value: 14.238999999999999
- type: map_at_10
value: 19.323
- type: map_at_100
value: 19.994
- type: map_at_1000
value: 20.102999999999998
- type: map_at_3
value: 17.631
- type: map_at_5
value: 18.401
- type: mrr_at_1
value: 15.157000000000002
- type: mrr_at_10
value: 20.578
- type: mrr_at_100
value: 21.252
- type: mrr_at_1000
value: 21.346999999999998
- type: mrr_at_3
value: 18.762
- type: mrr_at_5
value: 19.713
- type: ndcg_at_1
value: 15.157000000000002
- type: ndcg_at_10
value: 22.468
- type: ndcg_at_100
value: 26.245
- type: ndcg_at_1000
value: 29.534
- type: ndcg_at_3
value: 18.981
- type: ndcg_at_5
value: 20.349999999999998
- type: precision_at_1
value: 15.157000000000002
- type: precision_at_10
value: 3.512
- type: precision_at_100
value: 0.577
- type: precision_at_1000
value: 0.091
- type: precision_at_3
value: 8.01
- type: precision_at_5
value: 5.656
- type: recall_at_1
value: 14.238999999999999
- type: recall_at_10
value: 31.038
- type: recall_at_100
value: 49.122
- type: recall_at_1000
value: 74.919
- type: recall_at_3
value: 21.436
- type: recall_at_5
value: 24.692
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce
metrics:
- type: map_at_1
value: 8.828
- type: map_at_10
value: 14.982000000000001
- type: map_at_100
value: 16.495
- type: map_at_1000
value: 16.658
- type: map_at_3
value: 12.366000000000001
- type: map_at_5
value: 13.655000000000001
- type: mrr_at_1
value: 19.088
- type: mrr_at_10
value: 29.29
- type: mrr_at_100
value: 30.291
- type: mrr_at_1000
value: 30.342000000000002
- type: mrr_at_3
value: 25.907000000000004
- type: mrr_at_5
value: 27.840999999999998
- type: ndcg_at_1
value: 19.088
- type: ndcg_at_10
value: 21.858
- type: ndcg_at_100
value: 28.323999999999998
- type: ndcg_at_1000
value: 31.561
- type: ndcg_at_3
value: 17.175
- type: ndcg_at_5
value: 18.869
- type: precision_at_1
value: 19.088
- type: precision_at_10
value: 6.9190000000000005
- type: precision_at_100
value: 1.376
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 12.703999999999999
- type: precision_at_5
value: 9.993
- type: recall_at_1
value: 8.828
- type: recall_at_10
value: 27.381
- type: recall_at_100
value: 50.0
- type: recall_at_1000
value: 68.355
- type: recall_at_3
value: 16.118
- type: recall_at_5
value: 20.587
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: f097057d03ed98220bc7309ddb10b71a54d667d6
metrics:
- type: map_at_1
value: 5.586
- type: map_at_10
value: 10.040000000000001
- type: map_at_100
value: 12.55
- type: map_at_1000
value: 13.123999999999999
- type: map_at_3
value: 7.75
- type: map_at_5
value: 8.835999999999999
- type: mrr_at_1
value: 42.25
- type: mrr_at_10
value: 51.205999999999996
- type: mrr_at_100
value: 51.818
- type: mrr_at_1000
value: 51.855
- type: mrr_at_3
value: 48.875
- type: mrr_at_5
value: 50.488
- type: ndcg_at_1
value: 32.25
- type: ndcg_at_10
value: 22.718
- type: ndcg_at_100
value: 24.359
- type: ndcg_at_1000
value: 29.232000000000003
- type: ndcg_at_3
value: 25.974000000000004
- type: ndcg_at_5
value: 24.291999999999998
- type: precision_at_1
value: 42.25
- type: precision_at_10
value: 17.75
- type: precision_at_100
value: 5.032
- type: precision_at_1000
value: 1.117
- type: precision_at_3
value: 28.833
- type: precision_at_5
value: 24.25
- type: recall_at_1
value: 5.586
- type: recall_at_10
value: 14.16
- type: recall_at_100
value: 28.051
- type: recall_at_1000
value: 45.157000000000004
- type: recall_at_3
value: 8.758000000000001
- type: recall_at_5
value: 10.975999999999999
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 829147f8f75a25f005913200eb5ed41fae320aa1
metrics:
- type: accuracy
value: 39.075
- type: f1
value: 35.01420354708222
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: 1429cf27e393599b8b359b9b72c666f96b2525f9
metrics:
- type: map_at_1
value: 43.519999999999996
- type: map_at_10
value: 54.368
- type: map_at_100
value: 54.918
- type: map_at_1000
value: 54.942
- type: map_at_3
value: 51.712
- type: map_at_5
value: 53.33599999999999
- type: mrr_at_1
value: 46.955000000000005
- type: mrr_at_10
value: 58.219
- type: mrr_at_100
value: 58.73500000000001
- type: mrr_at_1000
value: 58.753
- type: mrr_at_3
value: 55.518
- type: mrr_at_5
value: 57.191
- type: ndcg_at_1
value: 46.955000000000005
- type: ndcg_at_10
value: 60.45
- type: ndcg_at_100
value: 63.047
- type: ndcg_at_1000
value: 63.712999999999994
- type: ndcg_at_3
value: 55.233
- type: ndcg_at_5
value: 58.072
- type: precision_at_1
value: 46.955000000000005
- type: precision_at_10
value: 8.267
- type: precision_at_100
value: 0.962
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 22.326999999999998
- type: precision_at_5
value: 14.940999999999999
- type: recall_at_1
value: 43.519999999999996
- type: recall_at_10
value: 75.632
- type: recall_at_100
value: 87.41600000000001
- type: recall_at_1000
value: 92.557
- type: recall_at_3
value: 61.597
- type: recall_at_5
value: 68.518
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be
metrics:
- type: map_at_1
value: 9.549000000000001
- type: map_at_10
value: 15.762
- type: map_at_100
value: 17.142
- type: map_at_1000
value: 17.329
- type: map_at_3
value: 13.575000000000001
- type: map_at_5
value: 14.754000000000001
- type: mrr_at_1
value: 19.753
- type: mrr_at_10
value: 26.568
- type: mrr_at_100
value: 27.606
- type: mrr_at_1000
value: 27.68
- type: mrr_at_3
value: 24.203
- type: mrr_at_5
value: 25.668999999999997
- type: ndcg_at_1
value: 19.753
- type: ndcg_at_10
value: 21.118000000000002
- type: ndcg_at_100
value: 27.308
- type: ndcg_at_1000
value: 31.304
- type: ndcg_at_3
value: 18.319
- type: ndcg_at_5
value: 19.414
- type: precision_at_1
value: 19.753
- type: precision_at_10
value: 6.08
- type: precision_at_100
value: 1.204
- type: precision_at_1000
value: 0.192
- type: precision_at_3
value: 12.191
- type: precision_at_5
value: 9.383
- type: recall_at_1
value: 9.549000000000001
- type: recall_at_10
value: 26.131
- type: recall_at_100
value: 50.544999999999995
- type: recall_at_1000
value: 74.968
- type: recall_at_3
value: 16.951
- type: recall_at_5
value: 20.95
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: 766870b35a1b9ca65e67a0d1913899973551fc6c
metrics:
- type: map_at_1
value: 25.544
- type: map_at_10
value: 32.62
- type: map_at_100
value: 33.275
- type: map_at_1000
value: 33.344
- type: map_at_3
value: 30.851
- type: map_at_5
value: 31.868999999999996
- type: mrr_at_1
value: 51.087
- type: mrr_at_10
value: 57.704
- type: mrr_at_100
value: 58.175
- type: mrr_at_1000
value: 58.207
- type: mrr_at_3
value: 56.106
- type: mrr_at_5
value: 57.074000000000005
- type: ndcg_at_1
value: 51.087
- type: ndcg_at_10
value: 40.876000000000005
- type: ndcg_at_100
value: 43.762
- type: ndcg_at_1000
value: 45.423
- type: ndcg_at_3
value: 37.65
- type: ndcg_at_5
value: 39.305
- type: precision_at_1
value: 51.087
- type: precision_at_10
value: 8.304
- type: precision_at_100
value: 1.059
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 22.875999999999998
- type: precision_at_5
value: 15.033
- type: recall_at_1
value: 25.544
- type: recall_at_10
value: 41.519
- type: recall_at_100
value: 52.957
- type: recall_at_1000
value: 64.132
- type: recall_at_3
value: 34.315
- type: recall_at_5
value: 37.583
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4
metrics:
- type: accuracy
value: 58.6696
- type: ap
value: 55.3644880984279
- type: f1
value: 58.07942097405652
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: validation
revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849
metrics:
- type: map_at_1
value: 14.442
- type: map_at_10
value: 22.932
- type: map_at_100
value: 24.132
- type: map_at_1000
value: 24.213
- type: map_at_3
value: 20.002
- type: map_at_5
value: 21.636
- type: mrr_at_1
value: 14.841999999999999
- type: mrr_at_10
value: 23.416
- type: mrr_at_100
value: 24.593999999999998
- type: mrr_at_1000
value: 24.669
- type: mrr_at_3
value: 20.494
- type: mrr_at_5
value: 22.14
- type: ndcg_at_1
value: 14.841999999999999
- type: ndcg_at_10
value: 27.975
- type: ndcg_at_100
value: 34.143
- type: ndcg_at_1000
value: 36.370000000000005
- type: ndcg_at_3
value: 21.944
- type: ndcg_at_5
value: 24.881
- type: precision_at_1
value: 14.841999999999999
- type: precision_at_10
value: 4.537
- type: precision_at_100
value: 0.767
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 9.322
- type: precision_at_5
value: 7.074
- type: recall_at_1
value: 14.442
- type: recall_at_10
value: 43.557
- type: recall_at_100
value: 72.904
- type: recall_at_1000
value: 90.40700000000001
- type: recall_at_3
value: 27.088
- type: recall_at_5
value: 34.144000000000005
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 86.95622435020519
- type: f1
value: 86.58363130708494
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 62.73034657650043
- type: f1
value: 60.78623915840713
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 67.54503002001334
- type: f1
value: 65.34879794116112
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 65.35233322893829
- type: f1
value: 62.994001882446646
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 45.37110075295806
- type: f1
value: 44.26285860740745
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
metrics:
- type: accuracy
value: 55.276672694394215
- type: f1
value: 53.28388179869587
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 62.25262197902417
- type: f1
value: 43.44084037148853
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 49.56043956043956
- type: f1
value: 32.86333673498598
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 49.93995997331555
- type: f1
value: 34.726671876888126
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 46.32947071719386
- type: f1
value: 32.325273615982795
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 32.208676945141626
- type: f1
value: 21.32185122815139
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: 6299947a7777084cc2d4b64235bf7190381ce755
metrics:
- type: accuracy
value: 43.627486437613015
- type: f1
value: 27.04872922347508
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (af)
config: af
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 40.548083389374575
- type: f1
value: 39.490307545239716
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (am)
config: am
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 24.18291862811029
- type: f1
value: 23.437620034727473
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ar)
config: ar
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 30.134498991257562
- type: f1
value: 28.787175191531283
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (az)
config: az
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 35.88433086751849
- type: f1
value: 36.264500398782126
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (bn)
config: bn
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 29.17283120376597
- type: f1
value: 27.8101616531901
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (cy)
config: cy
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 41.788836583725626
- type: f1
value: 39.71413181054801
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (da)
config: da
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 44.176193678547406
- type: f1
value: 42.192499826552286
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (de)
config: de
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 42.07464694014795
- type: f1
value: 39.44188259183162
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (el)
config: el
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 36.254203093476804
- type: f1
value: 34.46592715936761
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 61.40887693342301
- type: f1
value: 59.79854802683996
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (es)
config: es
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 42.679892400807
- type: f1
value: 42.04801248338172
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fa)
config: fa
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 35.59179556153329
- type: f1
value: 34.045862930486166
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fi)
config: fi
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 40.036987222595826
- type: f1
value: 38.117703439362785
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 43.43981170141224
- type: f1
value: 42.7084388987865
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (he)
config: he
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 31.593813046402154
- type: f1
value: 29.98550522450782
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hi)
config: hi
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 27.044384667114997
- type: f1
value: 27.313059184832667
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hu)
config: hu
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
- type: accuracy
value: 38.453261600538
- type: f1
value: 37.309189326110435
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hy)
config: hy
split: test
revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
metrics:
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name: MTEB MassiveScenarioClassification (nb)
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 39.018157363819775
- type: f1
value: 37.641949339321854
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nl)
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 45.35978480161399
- type: f1
value: 42.6851176096831
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pl)
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 41.89307330195023
- type: f1
value: 40.888710642615024
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pt)
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 45.901143241425686
- type: f1
value: 44.496942353920545
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ro)
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 44.11566913248151
- type: f1
value: 41.953945105870616
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ru)
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 32.76395427034297
- type: f1
value: 31.436372571600934
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sl)
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 40.504371217215876
- type: f1
value: 39.322752749628165
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sq)
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 42.51849361129792
- type: f1
value: 41.4139297118463
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sv)
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 42.293207800941495
- type: f1
value: 40.50409536806683
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sw)
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 42.9993275050437
- type: f1
value: 41.045416224973266
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ta)
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 28.32548755884331
- type: f1
value: 27.276841995561867
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (te)
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 26.593813046402154
- type: f1
value: 25.483878616197586
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (th)
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 36.788836583725626
- type: f1
value: 34.603932909177686
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tl)
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 42.5689307330195
- type: f1
value: 40.924469309079825
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 37.09482178883658
- type: f1
value: 37.949628822857164
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ur)
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 28.836583725622063
- type: f1
value: 27.806558655512344
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 37.357094821788834
- type: f1
value: 37.507918961038165
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 49.37794216543375
- type: f1
value: 47.20421153697707
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 44.42165433759248
- type: f1
value: 44.34741861198931
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: dcefc037ef84348e49b0d29109e891c01067226b
metrics:
- type: v_measure
value: 31.374938993074252
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc
metrics:
- type: v_measure
value: 26.871455379644093
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.402396942935333
- type: mrr
value: 31.42600938803256
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610
metrics:
- type: map_at_1
value: 3.7740000000000005
- type: map_at_10
value: 7.614999999999999
- type: map_at_100
value: 9.574
- type: map_at_1000
value: 10.711
- type: map_at_3
value: 5.7540000000000004
- type: map_at_5
value: 6.6659999999999995
- type: mrr_at_1
value: 33.127
- type: mrr_at_10
value: 40.351
- type: mrr_at_100
value: 41.144
- type: mrr_at_1000
value: 41.202
- type: mrr_at_3
value: 38.029
- type: mrr_at_5
value: 39.190000000000005
- type: ndcg_at_1
value: 31.579
- type: ndcg_at_10
value: 22.792
- type: ndcg_at_100
value: 21.698999999999998
- type: ndcg_at_1000
value: 30.892999999999997
- type: ndcg_at_3
value: 26.828999999999997
- type: ndcg_at_5
value: 25.119000000000003
- type: precision_at_1
value: 33.127
- type: precision_at_10
value: 16.718
- type: precision_at_100
value: 5.7090000000000005
- type: precision_at_1000
value: 1.836
- type: precision_at_3
value: 24.768
- type: precision_at_5
value: 21.3
- type: recall_at_1
value: 3.7740000000000005
- type: recall_at_10
value: 10.302999999999999
- type: recall_at_100
value: 23.013
- type: recall_at_1000
value: 54.864999999999995
- type: recall_at_3
value: 6.554
- type: recall_at_5
value: 8.087
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c
metrics:
- type: map_at_1
value: 15.620999999999999
- type: map_at_10
value: 24.519
- type: map_at_100
value: 25.586
- type: map_at_1000
value: 25.662000000000003
- type: map_at_3
value: 21.619
- type: map_at_5
value: 23.232
- type: mrr_at_1
value: 17.497
- type: mrr_at_10
value: 26.301000000000002
- type: mrr_at_100
value: 27.235
- type: mrr_at_1000
value: 27.297
- type: mrr_at_3
value: 23.561
- type: mrr_at_5
value: 25.111
- type: ndcg_at_1
value: 17.497
- type: ndcg_at_10
value: 29.725
- type: ndcg_at_100
value: 34.824
- type: ndcg_at_1000
value: 36.907000000000004
- type: ndcg_at_3
value: 23.946
- type: ndcg_at_5
value: 26.739
- type: precision_at_1
value: 17.497
- type: precision_at_10
value: 5.2170000000000005
- type: precision_at_100
value: 0.8099999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 11.114
- type: precision_at_5
value: 8.285
- type: recall_at_1
value: 15.620999999999999
- type: recall_at_10
value: 43.999
- type: recall_at_100
value: 67.183
- type: recall_at_1000
value: 83.174
- type: recall_at_3
value: 28.720000000000002
- type: recall_at_5
value: 35.154
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: 6205996560df11e3a3da9ab4f926788fc30a7db4
metrics:
- type: map_at_1
value: 54.717000000000006
- type: map_at_10
value: 67.514
- type: map_at_100
value: 68.484
- type: map_at_1000
value: 68.523
- type: map_at_3
value: 64.169
- type: map_at_5
value: 66.054
- type: mrr_at_1
value: 62.46000000000001
- type: mrr_at_10
value: 71.503
- type: mrr_at_100
value: 71.91499999999999
- type: mrr_at_1000
value: 71.923
- type: mrr_at_3
value: 69.46799999999999
- type: mrr_at_5
value: 70.677
- type: ndcg_at_1
value: 62.480000000000004
- type: ndcg_at_10
value: 72.98
- type: ndcg_at_100
value: 76.023
- type: ndcg_at_1000
value: 76.512
- type: ndcg_at_3
value: 68.138
- type: ndcg_at_5
value: 70.458
- type: precision_at_1
value: 62.480000000000004
- type: precision_at_10
value: 11.373
- type: precision_at_100
value: 1.437
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 29.622999999999998
- type: precision_at_5
value: 19.918
- type: recall_at_1
value: 54.717000000000006
- type: recall_at_10
value: 84.745
- type: recall_at_100
value: 96.528
- type: recall_at_1000
value: 99.39
- type: recall_at_3
value: 71.60600000000001
- type: recall_at_5
value: 77.511
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: b2805658ae38990172679479369a78b86de8c390
metrics:
- type: v_measure
value: 40.23390747226228
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 49.090518272935626
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5
metrics:
- type: map_at_1
value: 3.028
- type: map_at_10
value: 6.968000000000001
- type: map_at_100
value: 8.200000000000001
- type: map_at_1000
value: 8.432
- type: map_at_3
value: 5.3069999999999995
- type: map_at_5
value: 6.099
- type: mrr_at_1
value: 14.799999999999999
- type: mrr_at_10
value: 22.425
- type: mrr_at_100
value: 23.577
- type: mrr_at_1000
value: 23.669999999999998
- type: mrr_at_3
value: 20.233
- type: mrr_at_5
value: 21.318
- type: ndcg_at_1
value: 14.799999999999999
- type: ndcg_at_10
value: 12.206
- type: ndcg_at_100
value: 17.799
- type: ndcg_at_1000
value: 22.891000000000002
- type: ndcg_at_3
value: 12.128
- type: ndcg_at_5
value: 10.212
- type: precision_at_1
value: 14.799999999999999
- type: precision_at_10
value: 6.17
- type: precision_at_100
value: 1.428
- type: precision_at_1000
value: 0.266
- type: precision_at_3
value: 11.333
- type: precision_at_5
value: 8.74
- type: recall_at_1
value: 3.028
- type: recall_at_10
value: 12.522
- type: recall_at_100
value: 28.975
- type: recall_at_1000
value: 54.038
- type: recall_at_3
value: 6.912999999999999
- type: recall_at_5
value: 8.883000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 76.62983928119752
- type: cos_sim_spearman
value: 65.92910683118656
- type: euclidean_pearson
value: 71.10290039690963
- type: euclidean_spearman
value: 64.80076622426652
- type: manhattan_pearson
value: 70.8944726230188
- type: manhattan_spearman
value: 64.75082576033986
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f
metrics:
- type: cos_sim_pearson
value: 74.42679147085553
- type: cos_sim_spearman
value: 66.52980061546658
- type: euclidean_pearson
value: 74.87039477408763
- type: euclidean_spearman
value: 70.63397666902786
- type: manhattan_pearson
value: 74.97015137513088
- type: manhattan_spearman
value: 70.75951355434326
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9
metrics:
- type: cos_sim_pearson
value: 75.62472426599543
- type: cos_sim_spearman
value: 76.1662886374236
- type: euclidean_pearson
value: 76.3297128081315
- type: euclidean_spearman
value: 77.19385151966563
- type: manhattan_pearson
value: 76.50363291423257
- type: manhattan_spearman
value: 77.37081896355399
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b
metrics:
- type: cos_sim_pearson
value: 74.48227705407035
- type: cos_sim_spearman
value: 69.04572664009687
- type: euclidean_pearson
value: 71.76138185714849
- type: euclidean_spearman
value: 68.93415452043307
- type: manhattan_pearson
value: 71.68010915543306
- type: manhattan_spearman
value: 68.99176321262806
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6
metrics:
- type: cos_sim_pearson
value: 78.1566527175902
- type: cos_sim_spearman
value: 79.23677712825851
- type: euclidean_pearson
value: 76.29138438696417
- type: euclidean_spearman
value: 77.20108266215374
- type: manhattan_pearson
value: 76.27464935799118
- type: manhattan_spearman
value: 77.15286174478099
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd
metrics:
- type: cos_sim_pearson
value: 75.068454465977
- type: cos_sim_spearman
value: 76.06792422441929
- type: euclidean_pearson
value: 70.64605440627699
- type: euclidean_spearman
value: 70.21776051117844
- type: manhattan_pearson
value: 70.32479295054918
- type: manhattan_spearman
value: 69.89782458638528
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 39.43327289939437
- type: cos_sim_spearman
value: 52.386010275505654
- type: euclidean_pearson
value: 46.40999904885745
- type: euclidean_spearman
value: 51.00333465175934
- type: manhattan_pearson
value: 46.55753533133655
- type: manhattan_spearman
value: 51.07550440519388
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 55.54431928210687
- type: cos_sim_spearman
value: 55.61674586076298
- type: euclidean_pearson
value: 58.07442713714088
- type: euclidean_spearman
value: 55.74066216931719
- type: manhattan_pearson
value: 57.84021675638542
- type: manhattan_spearman
value: 55.20365812536853
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 11.378463868809098
- type: cos_sim_spearman
value: 8.209569244801065
- type: euclidean_pearson
value: 1.07041700730406
- type: euclidean_spearman
value: 2.2052197108931892
- type: manhattan_pearson
value: 0.7671300251104268
- type: manhattan_spearman
value: 3.430645020535567
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 32.71403560929013
- type: cos_sim_spearman
value: 30.18181775929109
- type: euclidean_pearson
value: 25.57368595910298
- type: euclidean_spearman
value: 23.316649115731376
- type: manhattan_pearson
value: 24.144200325329614
- type: manhattan_spearman
value: 21.64621546338457
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 83.36340470799158
- type: cos_sim_spearman
value: 84.95398260629699
- type: euclidean_pearson
value: 80.69876969911644
- type: euclidean_spearman
value: 80.97451731130427
- type: manhattan_pearson
value: 80.65869354146945
- type: manhattan_spearman
value: 80.8540858718528
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 1.9200044163754912
- type: cos_sim_spearman
value: 1.0393399782021342
- type: euclidean_pearson
value: 1.1376003191297994
- type: euclidean_spearman
value: 1.8947106671763914
- type: manhattan_pearson
value: 3.8362564474484335
- type: manhattan_spearman
value: 4.242750882792888
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 26.561262451099577
- type: cos_sim_spearman
value: 28.776666666659906
- type: euclidean_pearson
value: 14.640410196999088
- type: euclidean_spearman
value: 16.10557011701786
- type: manhattan_pearson
value: 15.019405495911272
- type: manhattan_spearman
value: 15.37192083104197
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 69.7544202001433
- type: cos_sim_spearman
value: 71.88444295144646
- type: euclidean_pearson
value: 73.84934185952773
- type: euclidean_spearman
value: 73.26911108021089
- type: manhattan_pearson
value: 74.04354196954574
- type: manhattan_spearman
value: 73.37650787943872
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 27.70511842301491
- type: cos_sim_spearman
value: 26.339466714066447
- type: euclidean_pearson
value: 9.323158236506385
- type: euclidean_spearman
value: 7.32083231520273
- type: manhattan_pearson
value: 7.807399527573071
- type: manhattan_spearman
value: 5.525546663067113
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 24.226521799447692
- type: cos_sim_spearman
value: 20.72992940458968
- type: euclidean_pearson
value: 6.753378617205011
- type: euclidean_spearman
value: 6.281654679029505
- type: manhattan_pearson
value: 7.087180250449323
- type: manhattan_spearman
value: 6.41611659259516
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0
metrics:
- type: cos_sim_pearson
value: 29.131412364061234
- type: cos_sim_spearman
value: 25.053429612793547
- type: euclidean_pearson
value: 10.657141303962
- type: euclidean_spearman
value: 9.712124819778452
- type: manhattan_pearson
value: 12.481782693315688
- type: manhattan_spearman
value: 11.287958480905973
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 64.04750650962879
- type: cos_sim_spearman
value: 65.66183708171826
- type: euclidean_pearson
value: 66.90887604405887
- type: euclidean_spearman
value: 66.89814072484552
- type: manhattan_pearson
value: 67.31627110509089
- type: manhattan_spearman
value: 67.01048176165322
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 19.26519187000913
- type: cos_sim_spearman
value: 21.987647321429005
- type: euclidean_pearson
value: 17.850618752342946
- type: euclidean_spearman
value: 22.86669392885474
- type: manhattan_pearson
value: 18.16183594260708
- type: manhattan_spearman
value: 23.637510352837907
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 34.221261828226936
- type: cos_sim_spearman
value: 49.811823238907664
- type: euclidean_pearson
value: 44.50394399762147
- type: euclidean_spearman
value: 50.959184495072876
- type: manhattan_pearson
value: 45.83191034038624
- type: manhattan_spearman
value: 50.190409866117946
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 3.620381732096531
- type: cos_sim_spearman
value: 23.30843951799194
- type: euclidean_pearson
value: 0.965453312113125
- type: euclidean_spearman
value: 24.235967620790316
- type: manhattan_pearson
value: 1.4408922275701606
- type: manhattan_spearman
value: 25.161920137046096
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 16.69489628726267
- type: cos_sim_spearman
value: 34.66348380997687
- type: euclidean_pearson
value: 29.415825529188606
- type: euclidean_spearman
value: 38.33011033170646
- type: manhattan_pearson
value: 31.23273195263394
- type: manhattan_spearman
value: 39.10055785755795
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 9.134927430889528
- type: cos_sim_spearman
value: 28.18922448944151
- type: euclidean_pearson
value: 19.86814169549051
- type: euclidean_spearman
value: 27.519588644948627
- type: manhattan_pearson
value: 21.80949221238945
- type: manhattan_spearman
value: 28.25217200494078
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 3.6386482942352085
- type: cos_sim_spearman
value: 9.068119621940966
- type: euclidean_pearson
value: 0.8123129118737714
- type: euclidean_spearman
value: 9.173672890166147
- type: manhattan_pearson
value: 0.754518899822658
- type: manhattan_spearman
value: 8.431719541986524
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 2.972091574908432
- type: cos_sim_spearman
value: 25.48511383289232
- type: euclidean_pearson
value: 12.751569670148918
- type: euclidean_spearman
value: 24.940721642439286
- type: manhattan_pearson
value: 14.310238482989826
- type: manhattan_spearman
value: 24.69821216148647
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 54.4745185734135
- type: cos_sim_spearman
value: 67.66493409568727
- type: euclidean_pearson
value: 60.13580336797049
- type: euclidean_spearman
value: 66.12319300814538
- type: manhattan_pearson
value: 60.816210368708155
- type: manhattan_spearman
value: 65.70010026716766
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 49.37865412588201
- type: cos_sim_spearman
value: 53.07135629778897
- type: euclidean_pearson
value: 49.29201416711091
- type: euclidean_spearman
value: 50.54523702399645
- type: manhattan_pearson
value: 51.265764141268534
- type: manhattan_spearman
value: 51.979086403193605
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 44.925652392562135
- type: cos_sim_spearman
value: 49.51253904767726
- type: euclidean_pearson
value: 48.79346518897415
- type: euclidean_spearman
value: 51.47957870101565
- type: manhattan_pearson
value: 49.51314553898044
- type: manhattan_spearman
value: 51.895207893189166
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 45.241690321111875
- type: cos_sim_spearman
value: 48.24795739512037
- type: euclidean_pearson
value: 49.22719494399897
- type: euclidean_spearman
value: 49.64102442042809
- type: manhattan_pearson
value: 49.497887732970256
- type: manhattan_spearman
value: 49.940515338096304
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 36.42138324083909
- type: cos_sim_spearman
value: 36.79867489417801
- type: euclidean_pearson
value: 27.760612942610084
- type: euclidean_spearman
value: 29.140966500287625
- type: manhattan_pearson
value: 28.456674031350115
- type: manhattan_spearman
value: 27.46356370924497
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 26.55350664089358
- type: cos_sim_spearman
value: 28.681707196975008
- type: euclidean_pearson
value: 12.613577889195138
- type: euclidean_spearman
value: 13.589493311702933
- type: manhattan_pearson
value: 11.640157427420958
- type: manhattan_spearman
value: 10.345223941212415
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 38.54682179114309
- type: cos_sim_spearman
value: 45.782560880405704
- type: euclidean_pearson
value: 46.496857002368486
- type: euclidean_spearman
value: 48.21270426410012
- type: manhattan_pearson
value: 46.871839119374044
- type: manhattan_spearman
value: 47.556987773851525
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 35.12956772546032
- type: cos_sim_spearman
value: 32.96920218281008
- type: euclidean_pearson
value: 34.23140384382136
- type: euclidean_spearman
value: 32.19303153191447
- type: manhattan_pearson
value: 34.189468276600635
- type: manhattan_spearman
value: 34.887065709732376
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 30.507667380509634
- type: cos_sim_spearman
value: 20.447284723752716
- type: euclidean_pearson
value: 29.662041381794474
- type: euclidean_spearman
value: 20.939990379746757
- type: manhattan_pearson
value: 32.5112080506328
- type: manhattan_spearman
value: 23.773047901712495
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
metrics:
- type: cos_sim_pearson
value: 71.10820459712156
- type: cos_sim_spearman
value: 61.97797868009122
- type: euclidean_pearson
value: 60.30910689156633
- type: euclidean_spearman
value: 61.97797868009122
- type: manhattan_pearson
value: 66.3405176964038
- type: manhattan_spearman
value: 61.97797868009122
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: 8913289635987208e6e7c72789e4be2fe94b6abd
metrics:
- type: cos_sim_pearson
value: 76.53032504460737
- type: cos_sim_spearman
value: 75.33716094627373
- type: euclidean_pearson
value: 69.64662673290599
- type: euclidean_spearman
value: 67.30188896368857
- type: manhattan_pearson
value: 69.45096082050807
- type: manhattan_spearman
value: 67.0718727259371
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: 56a6d0140cf6356659e2a7c1413286a774468d44
metrics:
- type: map
value: 71.33941904192648
- type: mrr
value: 89.73766429648782
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: a75ae049398addde9b70f6b268875f5cbce99089
metrics:
- type: map_at_1
value: 43.333
- type: map_at_10
value: 52.364
- type: map_at_100
value: 53.184
- type: map_at_1000
value: 53.234
- type: map_at_3
value: 49.832
- type: map_at_5
value: 51.244
- type: mrr_at_1
value: 45.333
- type: mrr_at_10
value: 53.455
- type: mrr_at_100
value: 54.191
- type: mrr_at_1000
value: 54.235
- type: mrr_at_3
value: 51.556000000000004
- type: mrr_at_5
value: 52.622
- type: ndcg_at_1
value: 45.333
- type: ndcg_at_10
value: 56.899
- type: ndcg_at_100
value: 60.702
- type: ndcg_at_1000
value: 62.046
- type: ndcg_at_3
value: 52.451
- type: ndcg_at_5
value: 54.534000000000006
- type: precision_at_1
value: 45.333
- type: precision_at_10
value: 7.8
- type: precision_at_100
value: 0.987
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 20.778
- type: precision_at_5
value: 13.866999999999999
- type: recall_at_1
value: 43.333
- type: recall_at_10
value: 69.69999999999999
- type: recall_at_100
value: 86.9
- type: recall_at_1000
value: 97.6
- type: recall_at_3
value: 57.81699999999999
- type: recall_at_5
value: 62.827999999999996
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea
metrics:
- type: cos_sim_accuracy
value: 99.7
- type: cos_sim_ap
value: 89.88577913120001
- type: cos_sim_f1
value: 84.62694041061593
- type: cos_sim_precision
value: 84.7542627883651
- type: cos_sim_recall
value: 84.5
- type: dot_accuracy
value: 99.24752475247524
- type: dot_ap
value: 56.81855467290009
- type: dot_f1
value: 56.084126189283936
- type: dot_precision
value: 56.16850551654965
- type: dot_recall
value: 56.00000000000001
- type: euclidean_accuracy
value: 99.7059405940594
- type: euclidean_ap
value: 90.12451226491524
- type: euclidean_f1
value: 84.44211629125196
- type: euclidean_precision
value: 88.66886688668868
- type: euclidean_recall
value: 80.60000000000001
- type: manhattan_accuracy
value: 99.7128712871287
- type: manhattan_ap
value: 90.67590584183216
- type: manhattan_f1
value: 84.85436893203884
- type: manhattan_precision
value: 82.45283018867924
- type: manhattan_recall
value: 87.4
- type: max_accuracy
value: 99.7128712871287
- type: max_ap
value: 90.67590584183216
- type: max_f1
value: 84.85436893203884
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235
metrics:
- type: v_measure
value: 52.74481093815175
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0
metrics:
- type: v_measure
value: 32.65999453562101
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9
metrics:
- type: map
value: 44.74498464555465
- type: mrr
value: 45.333879764026825
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122
metrics:
- type: cos_sim_pearson
value: 29,603788751645216
- type: cos_sim_spearman
value: 29.705103354786033
- type: dot_pearson
value: 28.07425338095399
- type: dot_spearman
value: 26.841406359135367
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217
metrics:
- type: map_at_1
value: 0.241
- type: map_at_10
value: 1.672
- type: map_at_100
value: 7.858999999999999
- type: map_at_1000
value: 17.616
- type: map_at_3
value: 0.631
- type: map_at_5
value: 0.968
- type: mrr_at_1
value: 90.0
- type: mrr_at_10
value: 92.952
- type: mrr_at_100
value: 93.036
- type: mrr_at_1000
value: 93.036
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 92.667
- type: ndcg_at_1
value: 83.0
- type: ndcg_at_10
value: 70.30199999999999
- type: ndcg_at_100
value: 48.149
- type: ndcg_at_1000
value: 40.709
- type: ndcg_at_3
value: 79.173
- type: ndcg_at_5
value: 75.347
- type: precision_at_1
value: 90.0
- type: precision_at_10
value: 72.6
- type: precision_at_100
value: 48.46
- type: precision_at_1000
value: 18.093999999999998
- type: precision_at_3
value: 84.0
- type: precision_at_5
value: 78.8
- type: recall_at_1
value: 0.241
- type: recall_at_10
value: 1.814
- type: recall_at_100
value: 11.141
- type: recall_at_1000
value: 37.708999999999996
- type: recall_at_3
value: 0.647
- type: recall_at_5
value: 1.015
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
metrics:
- type: map_at_1
value: 2.782
- type: map_at_10
value: 9.06
- type: map_at_100
value: 14.571000000000002
- type: map_at_1000
value: 16.006999999999998
- type: map_at_3
value: 5.037
- type: map_at_5
value: 6.63
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 48.243
- type: mrr_at_100
value: 49.065
- type: mrr_at_1000
value: 49.065
- type: mrr_at_3
value: 44.897999999999996
- type: mrr_at_5
value: 46.428999999999995
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 22.972
- type: ndcg_at_100
value: 34.777
- type: ndcg_at_1000
value: 45.639
- type: ndcg_at_3
value: 26.398
- type: ndcg_at_5
value: 24.418
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.224
- type: precision_at_1000
value: 1.4449999999999998
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.782
- type: recall_at_10
value: 14.841
- type: recall_at_100
value: 44.86
- type: recall_at_1000
value: 78.227
- type: recall_at_3
value: 5.959
- type: recall_at_5
value: 8.969000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 62.657999999999994
- type: ap
value: 10.96353161716344
- type: f1
value: 48.294226423442645
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: 62146448f05be9e52a36b8ee9936447ea787eede
metrics:
- type: accuracy
value: 52.40803621958121
- type: f1
value: 52.61009636022186
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
metrics:
- type: v_measure
value: 32.12697126747911
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 80.69976753889253
- type: cos_sim_ap
value: 54.74680676121268
- type: cos_sim_f1
value: 53.18923998590391
- type: cos_sim_precision
value: 47.93563413084904
- type: cos_sim_recall
value: 59.73614775725594
- type: dot_accuracy
value: 79.3348036001669
- type: dot_ap
value: 48.46902128933627
- type: dot_f1
value: 50.480109739369006
- type: dot_precision
value: 42.06084051345173
- type: dot_recall
value: 63.113456464379944
- type: euclidean_accuracy
value: 79.78780473266973
- type: euclidean_ap
value: 50.258327255164815
- type: euclidean_f1
value: 49.655838666827684
- type: euclidean_precision
value: 45.78044978846582
- type: euclidean_recall
value: 54.24802110817942
- type: manhattan_accuracy
value: 79.76992310901831
- type: manhattan_ap
value: 49.89892485714363
- type: manhattan_f1
value: 49.330433787341185
- type: manhattan_precision
value: 43.56175459874672
- type: manhattan_recall
value: 56.86015831134564
- type: max_accuracy
value: 80.69976753889253
- type: max_ap
value: 54.74680676121268
- type: max_f1
value: 53.18923998590391
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 86.90573213800597
- type: cos_sim_ap
value: 81.05760818661524
- type: cos_sim_f1
value: 73.64688856729379
- type: cos_sim_precision
value: 69.46491946491946
- type: cos_sim_recall
value: 78.3646442870342
- type: dot_accuracy
value: 83.80680715644041
- type: dot_ap
value: 72.49774005947461
- type: dot_f1
value: 68.68460650173216
- type: dot_precision
value: 62.954647507858105
- type: dot_recall
value: 75.56205728364644
- type: euclidean_accuracy
value: 85.97430822369697
- type: euclidean_ap
value: 78.86101740829326
- type: euclidean_f1
value: 71.07960824663695
- type: euclidean_precision
value: 70.36897306270279
- type: euclidean_recall
value: 71.8047428395442
- type: manhattan_accuracy
value: 85.94132029339853
- type: manhattan_ap
value: 78.77876711171923
- type: manhattan_f1
value: 71.07869075515912
- type: manhattan_precision
value: 69.80697847067557
- type: manhattan_recall
value: 72.39759778256852
- type: max_accuracy
value: 86.90573213800597
- type: max_ap
value: 81.05760818661524
- type: max_f1
value: 73.64688856729379
---
# SGPT-125M-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
SharpNLight/q-FrozenLake-v1-4x4-noSlippery
|
SharpNLight
| 2023-03-27T22:08:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T22:08:11Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="SharpNLight/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
tcvrishank/histo_train_segformer
|
tcvrishank
| 2023-03-27T22:06:32Z | 206 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"segformer",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-25T03:34:01Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: histo_train_segformer
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.875
---
<!-- 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. -->
# histo_train_segformer
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3830
- Accuracy: 0.875
## 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2234 | 16.67 | 100 | 0.3830 | 0.875 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
u23429/headline-predictor
|
u23429
| 2023-03-27T22:05:18Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:u23429/autotrain-data-stock-distil",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T21:58:02Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- u23429/autotrain-data-stock-distil
co2_eq_emissions:
emissions: 2.960971697133151
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44339111846
- CO2 Emissions (in grams): 2.9610
## Validation Metrics
- Loss: 1.634
- Accuracy: 0.940
- Macro F1: 0.882
- Micro F1: 0.940
- Weighted F1: 0.924
- Macro Precision: 0.876
- Micro Precision: 0.940
- Weighted Precision: 0.914
- Macro Recall: 0.900
- Micro Recall: 0.940
- Weighted Recall: 0.940
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/u23429/autotrain-stock-distil-44339111846
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("u23429/autotrain-stock-distil-44339111846", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("u23429/autotrain-stock-distil-44339111846", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ROGRANMAR/que_funcione_que_funcione3
|
ROGRANMAR
| 2023-03-27T21:59:26Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-27T21:57:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: que_funcione_que_funcione3
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. -->
# que_funcione_que_funcione3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 26.7271
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.5 | 10 | 29.9242 |
| No log | 1.0 | 20 | 26.7271 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
SAL83/poca-SoccerTwos
|
SAL83
| 2023-03-27T21:58:36Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-27T21:58:18Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: SAL83/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-15000
|
vocabtrimmer
| 2023-03-27T21:55:14Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T21:20:24Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa): `vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-15000`
This model is a trimmed version of [lmqg/mbart-large-cc25-jaquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-jaquad-qa | vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-15000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 370,188,288 |
| parameter_size_embedding | 512,057,344 | 30,728,192 |
| vocab_size | 250,028 | 15,004 |
| compression_rate_full | 100.0 | 60.6 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 15000 | 2 |
|
ROGRANMAR/que_funcione_que_funcione2
|
ROGRANMAR
| 2023-03-27T21:46:14Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-27T21:41:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: que_funcione_que_funcione2
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. -->
# que_funcione_que_funcione2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 43.6653
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.5 | 10 | 50.2270 |
| No log | 1.0 | 20 | 43.6653 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
rocketai/company_fistherman_hat_bc_fisherman_hat
|
rocketai
| 2023-03-27T21:44:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-25T17:22:21Z |
fistherman_hat bc_fisherman_hat
fistherman_hat bc_fisherman_hat ...
|
aegrif/CIS6930_DAAGR_Classification
|
aegrif
| 2023-03-27T21:30:47Z | 152 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T21:26:17Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: CIS6930_DAAGR_Classification
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# CIS6930_DAAGR_Classification
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
kingsley9494/ks
|
kingsley9494
| 2023-03-27T21:16:32Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-03-27T21:16:32Z |
---
license: bigscience-openrail-m
---
|
charlesbeale/vccp-avatar
|
charlesbeale
| 2023-03-27T21:12:38Z | 29 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T21:10:10Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: vccpavatar
---
### VCCP Avatar Dreambooth model trained by charlesbeale with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
vccpavatar (use that on your prompt)
|
shi-labs/versatile-diffusion
|
shi-labs
| 2023-03-27T21:10:36Z | 2,813 | 48 |
diffusers
|
[
"diffusers",
"image-to-text",
"image-to-image",
"text-to-image",
"text-to-text",
"image-editing",
"image-variation",
"generation",
"vision",
"dataset:Laion2B-en",
"arxiv:2211.08332",
"license:mit",
"diffusers:VersatileDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-11-22T22:47:21Z |
---
license: mit
tags:
- image-to-text
- image-to-image
- text-to-image
- text-to-text
- image-editing
- image-variation
- generation
- vision
datasets:
- Laion2B-en
widget:
- text: "A high tech solarpunk utopia in the Amazon rainforest"
example_title: Amazon rainforest
---
# Versatile Diffusion V1.0 Model Card
We built **Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework**, as a step towards **Universal Generative AI**. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D.
Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332).
# Model Details
One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder, and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram:
<p align="center">
<img src="https://huggingface.co/shi-labs/versatile-diffusion-model/resolve/main/assets/figures/vd_combined.png" width="99%">
</p>
- **Developed by:** Xingqian Xu, Atlas Wang, Eric Zhang, Kai Wang, and Humphrey Shi
- **Model type:** Diffusion-based multimodal generation model
- **Language(s):** English
- **License:** MIT
- **Resources for more information:** [GitHub Repository](https://github.com/SHI-Labs/Versatile-Diffusion), [Paper](https://arxiv.org/abs/2211.08332).
- **Cite as:**
```
@article{xu2022versatile,
title = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model},
author = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi},
year = 2022,
url = {https://arxiv.org/abs/2211.08332},
eprint = {2211.08332},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}
```
# Usage
You can use the model both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [SHI-Labs Versatile Diffusion codebase](https://github.com/SHI-Labs/Versatile-Diffusion).
## 🧨 Diffusers
Diffusers let's you both use a unified and more memory-efficient, task-specific pipelines.
**Make sure to install `transformers` from `"main"` in order to use this model.**:
```
pip install git+https://github.com/huggingface/transformers
```
## VersatileDiffusionPipeline
To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#diffusers.VersatileDiffusionPipeline)
```py
#! pip install git+https://github.com/huggingface/transformers diffusers torch
from diffusers import VersatileDiffusionPipeline
import torch
import requests
from io import BytesIO
from PIL import Image
pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
# prompt
prompt = "a red car"
# initial image
url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
# text to image
image = pipe.text_to_image(prompt).images[0]
# image variation
image = pipe.image_variation(image).images[0]
# image variation
image = pipe.dual_guided(prompt, image).images[0]
```
### Task Specific
The task specific pipelines load only the weights that are needed onto GPU.
You can find all task specific pipelines [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#versatilediffusion).
You can use them as follows:
### Text to Image
```py
from diffusers import VersatileDiffusionTextToImagePipeline
import torch
pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe.remove_unused_weights()
pipe = pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(0)
image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0]
image.save("./astronaut.png")
```
#### Image variations
```py
from diffusers import VersatileDiffusionImageVariationPipeline
import torch
import requests
from io import BytesIO
from PIL import Image
# download an initial image
url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(0)
image = pipe(image, generator=generator).images[0]
image.save("./car_variation.png")
```
#### Dual-guided generation
```py
from diffusers import VersatileDiffusionDualGuidedPipeline
import torch
import requests
from io import BytesIO
from PIL import Image
# download an initial image
url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
text = "a red car in the sun"
pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe.remove_unused_weights()
pipe = pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(0)
text_to_image_strength = 0.75
image = pipe(prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator).images[0]
image.save("./red_car.png")
```
### Original GitHub Repository
Follow the instructions [here](https://github.com/SHI-Labs/Versatile-Diffusion/#evaluation).
# Cautions, Biases, and Content Acknowledgment
We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors.
Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.
|
JfuentesR/a2c-PandaReachDense-v2
|
JfuentesR
| 2023-03-27T21:01:08Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T20:58:36Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.64 +/- 0.20
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jorgelzn/dqn-SpaceInvadersNoFrameskip-v4
|
jorgelzn
| 2023-03-27T20:57:31Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-02T12:59:51Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 403.00 +/- 148.73
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jorgelzn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jorgelzn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jorgelzn
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
pimentooliver/fungi-sd-diffusion
|
pimentooliver
| 2023-03-27T20:44:48Z | 32 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"en",
"dataset:pimentooliver/fungi",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T17:15:48Z |
---
datasets:
- pimentooliver/fungi
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
This is a fine-tune of CompVis/stable-diffusion-v1-4. It has been fine tuned on a dataset of fungi imagery which has been clustered to represent 'species'.
Each 'species' has been assigned a generated name in an attempt to fine-tune the model on nonexistent fungal species.
Unfortunately, this model has been impacted by catastrophic forgetting. It will be retrained soon, upload only for academic use.
|
kunishou/Japanese-Alpaca-LoRA-13b-v0
|
kunishou
| 2023-03-27T20:40:44Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2023-03-22T14:17:13Z |
---
license: mit
---
This repo contains a low-rank adapter for LLaMA-13b
fit on the Stanford Alpaca dataset translated into Japanese.
It doesn't contain the foundation model itself, so it's MIT licensed.
Instructions for running it can be found at https://github.com/kunishou/Japanese-Alpaca-LoRA.
|
joshnielsen876/LKD_Experience_CV5
|
joshnielsen876
| 2023-03-27T20:33:32Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T19:43:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: LKD_Experience_CV5
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. -->
# LKD_Experience_CV5
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: 0.1901
- Accuracy: 0.9328
- F1: 0.9306
- Precision: 0.9335
- Recall: 0.9283
## 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: 8
- eval_batch_size: 4
- 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 | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 48 | 0.5064 | 0.6555 | 0.5380 | 0.8136 | 0.59 |
| No log | 2.0 | 96 | 0.3327 | 0.9160 | 0.9114 | 0.9297 | 0.9028 |
| No log | 3.0 | 144 | 0.2398 | 0.9244 | 0.9212 | 0.9305 | 0.9155 |
| No log | 4.0 | 192 | 0.1995 | 0.9328 | 0.9306 | 0.9335 | 0.9283 |
| No log | 5.0 | 240 | 0.1901 | 0.9328 | 0.9306 | 0.9335 | 0.9283 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-10000
|
vocabtrimmer
| 2023-03-27T20:21:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T19:30:33Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa): `vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-10000`
This model is a trimmed version of [lmqg/mbart-large-cc25-esquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-esquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-esquad-qa | vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-10000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 365,068,288 |
| parameter_size_embedding | 512,057,344 | 20,488,192 |
| vocab_size | 250,028 | 10,004 |
| compression_rate_full | 100.0 | 59.76 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 10000 | 2 |
|
LarryAIDraw/SNKurskAzurLaneLora_beta
|
LarryAIDraw
| 2023-03-27T19:55:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T19:39:56Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/24748/sn-kursk-or-azur-lane-or-lora
|
LarryAIDraw/elysiaHohWithout_10
|
LarryAIDraw
| 2023-03-27T19:54:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T19:33:42Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/17798/elysia-hoh-without-bells
|
LarryAIDraw/morisakiAlesiaYuBlue_v10
|
LarryAIDraw
| 2023-03-27T19:53:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T19:34:03Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/24488/morisaki-alesia-yu-blue-reflection-sun
|
env-test/a2c-AntBulletEnv-v0
|
env-test
| 2023-03-27T19:41:06Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T19:40:00Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 943.38 +/- 41.30
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vocabtrimmer/mbart-large-cc25-frquad-qa-trimmed-fr-10000
|
vocabtrimmer
| 2023-03-27T19:24:18Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T19:00:52Z |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa): `vocabtrimmer/mbart-large-cc25-frquad-qa-trimmed-fr-10000`
This model is a trimmed version of [lmqg/mbart-large-cc25-frquad-qa](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-frquad-qa | vocabtrimmer/mbart-large-cc25-frquad-qa-trimmed-fr-10000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 365,068,288 |
| parameter_size_embedding | 512,057,344 | 20,488,192 |
| vocab_size | 250,028 | 10,004 |
| compression_rate_full | 100.0 | 59.76 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 10000 | 2 |
|
jeffwan/llama-7b-hf
|
jeffwan
| 2023-03-27T18:55:34Z | 2,686 | 5 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-27T18:37:46Z |
# LLama 7B Hugging Face model
This repo hosts model weights and it's for research purpose. If it against some policies that I don't know, feel free to reach out to me and I will delete it.
---
license: other
---
LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details.
License Non-commercial bespoke license
> Note: I copied above statement from https://huggingface.co/decapoda-research/llama-7b-hf
|
BreadAi/MuseBread
|
BreadAi
| 2023-03-27T18:53:36Z | 190 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"dataset:breadlicker45/musenet-encoders-12k",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-25T21:45:18Z |
---
datasets:
- breadlicker45/musenet-encoders-12k
---
|
emmuzoo/a2c-PandaReachDense-v2
|
emmuzoo
| 2023-03-27T18:30:03Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T14:01:19Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.69 +/- 0.51
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
miki030/PPO-Lunar-v5
|
miki030
| 2023-03-27T18:13:35Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T18:07:39Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 302.89 +/- 13.61
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
silentkebab/ppo-LunarLander-v2
|
silentkebab
| 2023-03-27T18:12:38Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T17:47:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.25 +/- 21.84
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
pinaggle/q-FrozenLake-v1-4x4-noSlippery
|
pinaggle
| 2023-03-27T17:54:12Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T17:54:10Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pinaggle/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
MilaNLProc/xlm-emo-t
|
MilaNLProc
| 2023-03-27T17:52:36Z | 4,763 | 4 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"emotion",
"emotion-analysis",
"multilingual",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-06T08:56:26Z |
---
language: multilingual
tags:
- emotion
- emotion-analysis
- multilingual
widget:
- text: "Guarda! ci sono dei bellissimi capibara!"
example_title: "Emotion Classification 1"
- text: "Sei una testa di cazzo!!"
example_title: "Emotion Classification 2"
- text: "Quelle bonne nouvelle!"
example_title: "Emotion Classification 3"
arxiv: ""
---
#
[Federico Bianchi](https://federicobianchi.io/) •
[Debora Nozza](http://dnozza.github.io/) •
[Dirk Hovy](http://www.dirkhovy.com/)
## Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.
## Model
This model is the fine-tuned version of the [XLM-T](https://aclanthology.org/2022.lrec-1.27/) model.
### Intended Use
The model is intended as a research output for research communities.
#### Primary intended uses
The primary intended users of these models are AI researchers.
## Results
This model had an F1 of 0.85 on the test set.
## License
For models, restrictions may apply to the data (which are derived from existing datasets) or Twitter (main data source).
We refer users to the original licenses accompanying each dataset and Twitter regulations.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
## Citation
Please use the following BibTeX entry if you use this model in your project:
```
@inproceedings{bianchi2021feel,
title = "{XLM-EMO: Multilingual Emotion Prediction in Social Media Text}",
author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
year = "2022",
publisher = "Association for Computational Linguistics",
}
```
|
Cleighton071/autotrain-detection-for-product-location-44269111681
|
Cleighton071
| 2023-03-27T17:50:11Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:Cleighton071/autotrain-data-detection-for-product-location",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T17:44:20Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Cleighton071/autotrain-data-detection-for-product-location
co2_eq_emissions:
emissions: 2.30199726014708
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44269111681
- CO2 Emissions (in grams): 2.3020
## Validation Metrics
- Loss: 0.005
- Accuracy: 0.999
- Macro F1: 0.999
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.999
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Cleighton071/autotrain-detection-for-product-location-44269111681
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Cleighton071/autotrain-detection-for-product-location-44269111681", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Cleighton071/autotrain-detection-for-product-location-44269111681", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Cleighton071/autotrain-detection-for-product-location-44269111684
|
Cleighton071
| 2023-03-27T17:49:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:Cleighton071/autotrain-data-detection-for-product-location",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T17:44:38Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Cleighton071/autotrain-data-detection-for-product-location
co2_eq_emissions:
emissions: 1.9511985418671698
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44269111684
- CO2 Emissions (in grams): 1.9512
## Validation Metrics
- Loss: 0.038
- Accuracy: 0.988
- Macro F1: 0.988
- Micro F1: 0.988
- Weighted F1: 0.988
- Macro Precision: 0.988
- Micro Precision: 0.988
- Weighted Precision: 0.988
- Macro Recall: 0.987
- Micro Recall: 0.988
- Weighted Recall: 0.988
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Cleighton071/autotrain-detection-for-product-location-44269111684
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Cleighton071/autotrain-detection-for-product-location-44269111684", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Cleighton071/autotrain-detection-for-product-location-44269111684", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ViditRaj/Distil_BERT_Hindi_Ads_Classifier
|
ViditRaj
| 2023-03-27T17:43:16Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T17:34:44Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ViditRaj/Distil_BERT_Hindi_Ads_Classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ViditRaj/Distil_BERT_Hindi_Ads_Classifier
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1092
- Validation Loss: 0.1996
- Train Accuracy: 0.9347
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 480, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3621 | 0.2848 | 0.9012 | 0 |
| 0.2283 | 0.2223 | 0.9210 | 1 |
| 0.1774 | 0.2084 | 0.9255 | 2 |
| 0.1389 | 0.2367 | 0.9073 | 3 |
| 0.1092 | 0.1996 | 0.9347 | 4 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ghassenhannachi/Reinforce-Pixelcopter-PLE-v0
|
ghassenhannachi
| 2023-03-27T17:23:27Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T17:23:24Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.90 +/- 25.66
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ViditRaj/XLM_Roberta_Hindi_Ads_Classifier
|
ViditRaj
| 2023-03-27T17:22:05Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T17:08:00Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ViditRaj/XLM_Roberta_Hindi_Ads_Classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ViditRaj/XLM_Roberta_Hindi_Ads_Classifier
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3258
- Validation Loss: 0.2867
- Train Accuracy: 0.9149
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3738 | 0.2117 | 0.9301 | 0 |
| 0.2323 | 0.1927 | 0.9347 | 1 |
| 0.2013 | 0.1739 | 0.9377 | 2 |
| 0.4551 | 0.5800 | 0.7219 | 3 |
| 0.3258 | 0.2867 | 0.9149 | 4 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
nflechas/spanish-multilingualBERT-sts
|
nflechas
| 2023-03-27T17:19:46Z | 184 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-17T11:25:10Z |
Please, visit https://github.com/nflechas/sentence_similarity for more information on this model.
|
therealcyberlord/bigcatvit
|
therealcyberlord
| 2023-03-27T17:19:13Z | 237 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-26T16:27:24Z |
---
license: apache-2.0
---
Fine-tuning a Vision Transformer on the Big Cats Dataset
In this project, we fine-tuned a vision transformer on the Big Cats dataset to perform image classification. The Big Cats dataset consists of 2339 images of 10 different types of big cats, including lions, tigers, jaguars, and more.
Our goal was to train a model that could accurately classify these images with high accuracy. After fine-tuning a pre-trained Vision Transformer, we were able to achieve an accuracy of 98%.
Kaggle dataset: https://www.kaggle.com/datasets/gpiosenka/cats-in-the-wild-image-classification
<img src="https://images.pexels.com/photos/8521833/pexels-photo-8521833.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1">
|
vocabtrimmer/mt5-small-esquad-qa-trimmed-es-120000
|
vocabtrimmer
| 2023-03-27T17:09:18Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T16:33:10Z |
# Vocabulary Trimmed [lmqg/mt5-small-esquad-qa](https://huggingface.co/lmqg/mt5-small-esquad-qa): `vocabtrimmer/mt5-small-esquad-qa-trimmed-es-120000`
This model is a trimmed version of [lmqg/mt5-small-esquad-qa](https://huggingface.co/lmqg/mt5-small-esquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-esquad-qa | vocabtrimmer/mt5-small-esquad-qa-trimmed-es-120000 |
|:---------------------------|:---------------------------|:-----------------------------------------------------|
| parameter_size_full | 300,165,504 | 166,944,128 |
| parameter_size_embedding | 256,103,424 | 122,882,048 |
| vocab_size | 250,101 | 120,002 |
| compression_rate_full | 100.0 | 55.62 |
| compression_rate_embedding | 100.0 | 47.98 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 120000 | 2 |
|
huggingtweets/hackscsslife
|
huggingtweets
| 2023-03-27T17:03:53Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-27T15:54:43Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1636581917536468995/EbnzZvIL_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">gert {</div>
<div style="text-align: center; font-size: 14px;">@hackscsslife</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from gert {.
| Data | gert { |
| --- | --- |
| Tweets downloaded | 1671 |
| Retweets | 20 |
| Short tweets | 374 |
| Tweets kept | 1277 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/0k2h69dm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hackscsslife's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/w5818qp8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/w5818qp8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/hackscsslife')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
arbts/taxi-v3-initial
|
arbts
| 2023-03-27T17:00:07Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T13:43:11Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-initial
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="arbts/taxi-v3-initial", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ViditRaj/BERT_Hindi_Ads_Classifier
|
ViditRaj
| 2023-03-27T16:59:49Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-22T19:53:19Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ViditRaj/BERT_Hindi_Ads_Classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ViditRaj/BERT_Hindi_Ads_Classifier
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0534
- Validation Loss: 0.1984
- Train Accuracy: 0.9377
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 480, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3284 | 0.2441 | 0.9164 | 0 |
| 0.1970 | 0.1913 | 0.9316 | 1 |
| 0.1183 | 0.1870 | 0.9438 | 2 |
| 0.0825 | 0.1853 | 0.9392 | 3 |
| 0.0534 | 0.1984 | 0.9377 | 4 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
joshnielsen876/LKD_Experience_CV4
|
joshnielsen876
| 2023-03-27T16:48:48Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-24T19:27:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: LKD_Experience_CV4
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. -->
# LKD_Experience_CV4
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: 0.2443
- Accuracy: 0.9244
- F1: 0.9158
- Precision: 0.9240
- Recall: 0.9091
## 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: 8
- eval_batch_size: 4
- 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 | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 48 | 0.4809 | 0.7311 | 0.6063 | 0.8532 | 0.6190 |
| No log | 2.0 | 96 | 0.3551 | 0.8908 | 0.8716 | 0.9157 | 0.8506 |
| No log | 3.0 | 144 | 0.2712 | 0.9244 | 0.9158 | 0.9240 | 0.9091 |
| No log | 4.0 | 192 | 0.2508 | 0.9244 | 0.9158 | 0.9240 | 0.9091 |
| No log | 5.0 | 240 | 0.2443 | 0.9244 | 0.9158 | 0.9240 | 0.9091 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
SebasV/autotrain-tableros_factibilidad-44246111621
|
SebasV
| 2023-03-27T16:46:06Z | 221 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:SebasV/autotrain-data-tableros_factibilidad",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-27T16:44:26Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- SebasV/autotrain-data-tableros_factibilidad
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.6678858266803156
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44246111621
- CO2 Emissions (in grams): 0.6679
## Validation Metrics
- Loss: 1.097
- Accuracy: 0.200
- Macro F1: 0.167
- Micro F1: 0.200
- Weighted F1: 0.133
- Macro Precision: 0.125
- Micro Precision: 0.200
- Weighted Precision: 0.100
- Macro Recall: 0.250
- Micro Recall: 0.200
- Weighted Recall: 0.200
|
SebasV/autotrain-tableros_factibilidad-44246111624
|
SebasV
| 2023-03-27T16:45:41Z | 171 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"autotrain",
"vision",
"dataset:SebasV/autotrain-data-tableros_factibilidad",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-27T16:44:54Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- SebasV/autotrain-data-tableros_factibilidad
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.36296304345687347
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44246111624
- CO2 Emissions (in grams): 0.3630
## Validation Metrics
- Loss: 1.413
- Accuracy: 0.400
- Macro F1: 0.375
- Micro F1: 0.400
- Weighted F1: 0.400
- Macro Precision: 0.375
- Micro Precision: 0.400
- Weighted Precision: 0.400
- Macro Recall: 0.375
- Micro Recall: 0.400
- Weighted Recall: 0.400
|
TerryYH/q-FrozenLake-v1-4x4-noSlippery
|
TerryYH
| 2023-03-27T16:33:41Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T16:33:38Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TerryYH/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
rageSpin/ppo-LunarLander-v2
|
rageSpin
| 2023-03-27T16:25:13Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T16:24:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.77 +/- 14.19
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vocabtrimmer/mt5-small-esquad-qa-trimmed-es-60000
|
vocabtrimmer
| 2023-03-27T16:09:44Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T17:29:37Z |
# Vocabulary Trimmed [lmqg/mt5-small-esquad-qa](https://huggingface.co/lmqg/mt5-small-esquad-qa): `vocabtrimmer/mt5-small-esquad-qa-trimmed-es-60000`
This model is a trimmed version of [lmqg/mt5-small-esquad-qa](https://huggingface.co/lmqg/mt5-small-esquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-esquad-qa | vocabtrimmer/mt5-small-esquad-qa-trimmed-es-60000 |
|:---------------------------|:---------------------------|:----------------------------------------------------|
| parameter_size_full | 300,165,504 | 105,504,128 |
| parameter_size_embedding | 256,103,424 | 61,442,048 |
| vocab_size | 250,101 | 60,002 |
| compression_rate_full | 100.0 | 35.15 |
| compression_rate_embedding | 100.0 | 23.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 60000 | 2 |
|
rickbox/test-mu
|
rickbox
| 2023-03-27T16:07:45Z | 30 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T16:04:47Z |
---
license: creativeml-openrail-m
---
|
kmposkid1/a2c-PandaReachDense-v2
|
kmposkid1
| 2023-03-27T16:04:34Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T16:02:10Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.53 +/- 0.40
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
pmgautam/Taxi-v3
|
pmgautam
| 2023-03-27T16:00:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T16:00:43Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="pmgautam/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ckao1030/bloomify2
|
ckao1030
| 2023-03-27T15:57:34Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T15:55:04Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: bloomify2
---
### bloomify2 Dreambooth model trained by ckao1030 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
bloomify2 (use that on your prompt)
|
vocabtrimmer/mt5-small-esquad-qa-trimmed-es-30000
|
vocabtrimmer
| 2023-03-27T15:45:05Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T17:14:14Z |
# Vocabulary Trimmed [lmqg/mt5-small-esquad-qa](https://huggingface.co/lmqg/mt5-small-esquad-qa): `vocabtrimmer/mt5-small-esquad-qa-trimmed-es-30000`
This model is a trimmed version of [lmqg/mt5-small-esquad-qa](https://huggingface.co/lmqg/mt5-small-esquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-esquad-qa | vocabtrimmer/mt5-small-esquad-qa-trimmed-es-30000 |
|:---------------------------|:---------------------------|:----------------------------------------------------|
| parameter_size_full | 300,165,504 | 74,784,128 |
| parameter_size_embedding | 256,103,424 | 30,722,048 |
| vocab_size | 250,101 | 30,002 |
| compression_rate_full | 100.0 | 24.91 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 30000 | 2 |
|
rickbox/test
|
rickbox
| 2023-03-27T15:43:25Z | 29 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T15:40:54Z |
---
license: creativeml-openrail-m
---
|
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