modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 18:27:02
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-06 18:26:43
| card
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|---|---|---|---|---|---|---|---|---|---|
sungwoo1/xlm-roberta-base-finetuned-panx-fr
|
sungwoo1
| 2023-03-28T04:58:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-27T07:48:29Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8430085815244825
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2719
- F1: 0.8430
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5444 | 1.0 | 191 | 0.3215 | 0.7772 |
| 0.2604 | 2.0 | 382 | 0.2840 | 0.8366 |
| 0.1756 | 3.0 | 573 | 0.2719 | 0.8430 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
KtheFISH/q-Taxi-v3
|
KtheFISH
| 2023-03-28T04:47:41Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T04:47:38Z |
---
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: 7.52 +/- 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="KtheFISH/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"])
```
|
juanmi1234/rl_course_vizdoom_health_gathering_supreme
|
juanmi1234
| 2023-03-28T04:42:59Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T04:33:01Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 19.83 +/- 3.52
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r juanmi1234/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
madoe001/dqn-SpaceInvadersNoFrameskip-v4
|
madoe001
| 2023-03-28T04:37:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T04:35:10Z |
---
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: 677.00 +/- 167.81
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 madoe001 -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 madoe001 -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 madoe001
```
## 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)])
```
|
vocabtrimmer/xlm-roberta-base-trimmed-es-30000-tweet-sentiment-es
|
vocabtrimmer
| 2023-03-28T04:28:51Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T02:22:12Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-es-30000-tweet-sentiment-es`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-30000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-30000) 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 | 66.44 | 66.44 | 66.44 | 65.86 | 66.44 | 65.8 | 66.44 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es-30000-tweet-sentiment-es/raw/main/eval.json).
|
OttoYu/Tree-ConditionHK
|
OttoYu
| 2023-03-28T04:17:47Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"vision",
"object-detection",
"zh",
"en",
"dataset:OttoYu/Tree-ConditionHK",
"doi:10.57967/hf/0481",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-03-24T05:55:34Z |
---
tags:
- vision
- image-classification
datasets:
- OttoYu/Tree-ConditionHK
widget:
license: afl-3.0
language:
- zh
- en
pipeline_tag: object-detection
---
# 🌳 Tree Condition Classification 樹況分類 (bilingual)
### Model Description
This online application covers 22 most typical tree disease over 290+ images. If you find any trees that has hidden injures, you can classifies with our model and report the tree condition via this form (https://rb.gy/c1sfja). 此在線程式涵蓋22種官方部門樹況分類的標準，超過290張圖像。如果您發現任何樹木有隱傷，您可以使用我們的模型進行分類並通過此表格報告樹木狀況。
- **Developed by:** Yu Kai Him Otto
- **Shared via:** Huggingface.co
- **Model type:** Opensource
## Uses
You can use the this model for tree condition image classification.
## Training Details
### Training Data
- Loss: 0.355
- Accuracy: 0.852
- Macro F1: 0.787
- Micro F1: 0.852
- Weighted F1: 0.825
- Macro Precision: 0.808
- Micro Precision: 0.852
- Weighted Precision: 0.854
- Macro Recall: 0.811
- Micro Recall: 0.852
- Weighted Recall: 0.852
|
YiYiXu/fill-circle-controlnet
|
YiYiXu
| 2023-03-28T04:00:25Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-27T20:16:08Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet- yiyixu/fill-circle-controlnet
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following.
prompt: red circle with blue background
prompt: cyan circle with brown floral background
|
Ganu3010/Taxi-v3
|
Ganu3010
| 2023-03-28T03:53:39Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T03:53:36Z |
---
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: 7.52 +/- 2.62
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="Ganu3010/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-es-10000-tweet-sentiment-es
|
vocabtrimmer
| 2023-03-28T03:47:54Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T01:43:57Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-es-10000-tweet-sentiment-es`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-10000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-10000) 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 | 66.09 | 66.09 | 66.09 | 65.62 | 66.09 | 65.64 | 66.09 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es-10000-tweet-sentiment-es/raw/main/eval.json).
|
Chattiori/BeryllMix
|
Chattiori
| 2023-03-28T03:45:04Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T06:59:41Z |
---
license: creativeml-openrail-m
---
(Chilloutmix-Ni-pruned-fp32-fix (0.5) + LOFI V2 (0.5) Weighted Sum) (0.6) + RetMix (0.4) Weighted Sum
|
Furuhata-du/alpaca-classify
|
Furuhata-du
| 2023-03-28T03:43:20Z | 0 | 0 | null |
[
"dataset:alpaca_data.json",
"region:us"
] | null | 2023-03-28T03:25:46Z |
---
datasets:
- alpaca_data.json
---
|
vocabtrimmer/xlm-roberta-base-trimmed-es-5000-tweet-sentiment-es
|
vocabtrimmer
| 2023-03-28T03:28:09Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T01:25:19Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-es-5000-tweet-sentiment-es`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-5000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-es-5000) 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 | 61.61 | 61.61 | 61.61 | 60.38 | 61.61 | 61.51 | 61.61 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-es-5000-tweet-sentiment-es/raw/main/eval.json).
|
alaahussein/t5-small-finetuned-subset-billsum-tutorial
|
alaahussein
| 2023-03-28T03:23:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-28T03:18:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1436
---
<!-- 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. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5578
- Rouge1: 0.1436
- Rouge2: 0.0514
- Rougel: 0.1188
- Rougelsum: 0.119
- Gen Len: 19.0
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8555 | 0.1281 | 0.0376 | 0.1074 | 0.1075 | 19.0 |
| No log | 2.0 | 124 | 2.6410 | 0.137 | 0.0468 | 0.113 | 0.1131 | 19.0 |
| No log | 3.0 | 186 | 2.5749 | 0.141 | 0.0496 | 0.116 | 0.1163 | 19.0 |
| No log | 4.0 | 248 | 2.5578 | 0.1436 | 0.0514 | 0.1188 | 0.119 | 19.0 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
MinMan85/Kanpiro_v10
|
MinMan85
| 2023-03-28T03:20:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-28T03:01:17Z |
---
license: creativeml-openrail-m
---
|
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
---
|
jasmeeetsingh/twitter-depression-classification-sentiment140
|
jasmeeetsingh
| 2023-03-28T02:59:37Z | 107 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"twitter",
"depression",
"sentiment140",
"en",
"dataset:sentiment140",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T06:46:30Z |
---
datasets:
- sentiment140
metrics:
- f1
license: apache-2.0
language:
- en
pipeline_tag: text-classification
tags:
- twitter
- depression
- sentiment140
---
# Model Card for Model ID
jasmeeetsingh/twitter-depression-classification-sentiment140 is a deep learning model trained to classify whether a given tweet is suicidal or not.
The model is based on a transformer architecture and fine-tuned on a large corpus of tweets annotated as suicidal or non-suicidal.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Jasmeet Singh Sandhu
- **Finetuned from model:** paulagarciaserrano/roberta-depression-detection
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The model is intended to be used to classify tweets automatically as suicidal or non-suicidal.
It can be used to analyze large volumes of tweets and identify users who may be at risk of depression, as well as to monitor the prevalence of depression-related discussions on social media platforms.
<!-- This section describes the evaluation protocols and provides the results. -->
#### Metrics
<img 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">
## Technical Specifications
The model was trained on a 6GB RTX 3060
|
swl-models/Anything-v5.0-PRT
|
swl-models
| 2023-03-28T02:58:54Z | 0 | 10 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T04:09:28Z |
---
license: creativeml-openrail-m
---
|
artbreguez/a2c-PandaReachDense-v2
|
artbreguez
| 2023-03-28T02:12:22Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:38:40Z |
---
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: -2.65 +/- 0.67
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
...
```
|
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
---
|
sauriopqno/autotrain-enfermedadespt2-44370111920
|
sauriopqno
| 2023-03-28T01:57:12Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"vision",
"medical",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-28T01:44:49Z |
---
tags:
- autotrain
- vision
- image-classification
- medical
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: 0.7148635752326786
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44370111920
- CO2 Emissions (in grams): 0.7149
## Validation Metrics
- Loss: 0.180
- Accuracy: 0.950
- Macro F1: 0.950
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.950
- Micro Precision: 0.950
- Weighted Precision: 0.950
- Macro Recall: 0.950
- Micro Recall: 0.950
- Weighted Recall: 0.950
|
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",
}
```
|
sohm/ppo-LunarLander-v2-Lunar200Kv5
|
sohm
| 2023-03-28T01:50:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:50:29Z |
---
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: -172.05 +/- 24.86
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
...
```
|
PolyRocketMatt/polyrocketmatt_irse_model_2
|
PolyRocketMatt
| 2023-03-28T01:47:24Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-28T01:35:34Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: polyrocketmatt_irse_model_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# polyrocketmatt_irse_model_2
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 4.26.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
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
...
```
|
ryanaspen/reinforce-pixelcopter
|
ryanaspen
| 2023-03-28T01:18:49Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-17T22:38:37Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 26.10 +/- 12.37
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
|
vocabtrimmer/xlm-roberta-base-trimmed-it-15000-tweet-sentiment-it
|
vocabtrimmer
| 2023-03-28T01:18:32Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T10:43:57Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-it-15000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-15000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-15000) 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 | 68.39 | 68.39 | 68.39 | 68.11 | 68.39 | 71.02 | 68.39 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-it-15000-tweet-sentiment-it/raw/main/eval.json).
|
sohm/ppo-LunarLander-v2-Lunar200Kv2
|
sohm
| 2023-03-28T01:18:04Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T01:17:58Z |
---
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: -144.59 +/- 24.33
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 |
|
artbreguez/a2c-AntBulletEnv-v0
|
artbreguez
| 2023-03-28T01:04:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-28T00:13:54Z |
---
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: 2077.10 +/- 45.05
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-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 |
|
vocabtrimmer/xlm-roberta-base-trimmed-it-10000-tweet-sentiment-it
|
vocabtrimmer
| 2023-03-28T00:49:43Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T10:15:25Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-it-10000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-10000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-10000) 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 | 67.7 | 67.7 | 67.7 | 67.09 | 67.7 | 69.61 | 67.7 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-it-10000-tweet-sentiment-it/raw/main/eval.json).
|
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
|
Unggi/hate_speech_classifier_KcElectra
|
Unggi
| 2023-03-28T00:39:25Z | 143 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-23T22:17:59Z |
---
license: cc-by-nc-sa-4.0
---
|
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.
|
vocabtrimmer/mbart-large-cc25-ruquad-qa-trimmed-ru-15000
|
vocabtrimmer
| 2023-03-28T00:26:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-28T00:04:58Z |
# 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-15000`
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-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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 15000 | 2 |
|
braedennorris/consumer_enterprise_fintech
|
braedennorris
| 2023-03-28T00:21:53Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-25T02:07:19Z |
For anyone looking at consumer v enterprise fintech rankings I'd refer to this model: braedennorris/autotrain-enterprise_v_consumer-3052187265. It was trained on dberta2 and has higher accuracy than this model.
|
vocabtrimmer/xlm-roberta-base-trimmed-it-5000-tweet-sentiment-it
|
vocabtrimmer
| 2023-03-28T00:20:14Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-20T09:46:36Z |
# `vocabtrimmer/xlm-roberta-base-trimmed-it-5000-tweet-sentiment-it`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-5000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-it-5000) 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 | 66.32 | 66.32 | 66.32 | 66.2 | 66.32 | 67.33 | 66.32 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-it-5000-tweet-sentiment-it/raw/main/eval.json).
|
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"])
```
|
Tavy/Weight_Control_AI
|
Tavy
| 2023-03-27T23:50:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-27T23:49:30Z |
The body gets energy from food. If this energy is not used, it will be stored. If the energy input from
food continuously exceeds the energy outputs of the body, this energy will be stored in the form of
fat under the skin and around the organs.
This project is about designing a program (maybe an Android App for extra credits) that takes the
consumed food as its input, and then provides the user with certain practical exercises that will burn
off the extra energy.
---
license: openrail
---
|
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
|
PhilSad
| 2023-03-27T23:41:13Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T23:41:12Z |
---
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="PhilSad/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"])
```
|
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/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)
|
vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-60000
|
vocabtrimmer
| 2023-03-27T23:19:20Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T22:31:10Z |
# 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-60000`
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-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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 60000 | 2 |
|
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)])
```
|
adamluc/neoxt
|
adamluc
| 2023-03-27T23:02:18Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-27T16:13:08Z |
---
license: apache-2.0
language:
- en
duplicated_from: togethercomputer/GPT-NeoXT-Chat-Base-20B
---
***<p style="font-size: 24px">Feel free to try out our [OpenChatKit feedback app](https://huggingface.co/spaces/togethercomputer/OpenChatKit)!</p>***
# GPT-NeoXT-Chat-Base-20B
> TLDR: As part of OpenChatKit (codebase available [here](https://github.com/togethercomputer/OpenChaT)),
> GPT-NeoXT-Chat-Base-20B is a 20B parameter language model, fine-tuned from EleutherAI’s GPT-NeoX with over 40 million instructions on 100% carbon negative compute.
GPT-NeoXT-Chat-Base-20B is based on ElutherAI’s GPT-NeoX model, and is fine-tuned with data focusing on dialog-style interactions.
We focused the tuning on several tasks such as question answering, classification, extraction, and summarization.
We’ve fine-tuned the model with a collection of 43 million high-quality instructions.
Together partnered with LAION and Ontocord.ai, who both helped curate the dataset the model is based on.
You can read more about this process and the availability of this dataset in LAION’s blog post [here](https://laion.ai/blog/oig-dataset/).
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 20B parameter open source chat model, fine-tuned from EleutherAI’s NeoX with over 40M instructions on 100% carbon negative compute
- **Resources for more information**: [GitHub Repository](https://github.com/togethercomputer/OpenChaT).
# Quick Start
```python
from transformers import pipeline
pipe = pipeline(model='togethercomputer/GPT-NeoXT-Chat-Base-20B')
pipe('''<human>: Hello!\n<bot>:''')
```
or
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
```
## Strengths of the model
There are several tasks that OpenChatKit excels at out of the box. This includes:
- Example 1: Summarization and question answering within context.
```markdown
**Summarize a long document into a single sentence and conduct question answering related to the document, with multiple rounds**
<human>: Last year, the travel industry saw a big rebound in demand — and that demand is showing no signs of slowing down this spring break travel season. Planes and hotels will be full, travelers will likely face long queues, cancellations, massive crowds and plenty of other travel nightmares. But perhaps the most frustrating thing you’ll have to prepare for this spring break is if your luggage goes missing. You’ve got to somehow track down your belongings that were in the airline’s care and then wait to be reunited — all while trying to enjoy the start of your long-awaited vacation. As far as travel nightmares go, lost luggage is up there as one of the worst.
To be honest up front, I am great at strategically losing things — or rather, losing stuff with style. (I hope you appreciate the “Toy Story” reference.) So when a product comes along that’s a perfect way to keep track of my belongings — especially when traveling — I’m adding it to my travel bag.
I’ve been a big fan of Apple’s $29 item tracker, dubbed AirTag. The long-rumored AirTag product finally launched in 2021, and while it’s not the first item tracker to exist, it has two key features that set it apart from the rest to make it the ultimate travel companion: access to the Find My network and Precision Finding technology.
The Federal Aviation Administration has even ruled that having an AirTag in your luggage is permitted and, in fact, not a safety hazard. The devices only contain 0.1 grams of lithium, which falls below the FAA’s limit.
“Luggage tracking devices powered by lithium metal cells that have 0.3 grams or less of lithium can be used on checked baggage,” the FAA said in a statement. “Apple AirTags meet this threshold; other luggage tracking devices may not.”
But, you may be wondering what makes them so special and why you should carry one in your checked luggage this spring break? Let’s explain why.
Summarize the above into a single paragraph.
<bot>: If you're traveling this spring break, you might want to consider investing in an item tracker to help you find your lost luggage. Apple's AirTag is the best option, according to a new report.
<human>: But isn't it unsafe to put AirTag in a luggage?
<bot>: The Federal Aviation Administration has ruled that having an AirTag in your luggage is permitted and, in fact, not a safety hazard.
<human>: Ah, why is that?
<bot>: The devices only contain 0.1 grams of lithium, which falls below the FAA’s limit.
```
- Example 2: Extraction.
```markdown
**Extract structured information from unstructured documents, as illustrated in the following example.**
<human>: Generate a table summarizing the options outlined in this email.
Team, we have to make a decision on the product plan for Goocrux v2.
The first option will take us 3 weeks of development, and will achieve performance of 80ms, but means that the UI has to change.
The second option will take us 4 weeks of development, and will achieve performance of 60ms, without any UI change.
The third option will take the longest -- 6 weeks of development, will achieve performance of 30ms, without any UI change.
A markdown table with four columns: (1) Option ID, (2) Development time, (3) Performance, (4) UI Change:
<bot>: #.
| ID | Option ID | Development time | Performance | UI Change |
| --------- | --------- | --------- | --------- | --------- |
| 1 | 1 | 3 weeks | 80ms | Yes |
| 2 | 2 | 4 weeks | 60ms | No |
| 3 | 3 | 6 weeks | 30ms | No |
```
- Example 3: Classification.
```markdown
**Classifying a sentence or paragraph into different categories.**
<human>: Classify the sentiment of the following sentence into Positive, Neutral, or Negative: Super excited about teaching Stanford’s first course on Large Language Models! Check the syllabus out here
<bot>: Positive
<human>: Classify the sentiment of the following sentence into Positive, Neutral, or Negative: How about the following sentence: It is raining outside and I feel so blue
<bot>: Negative
```
In addition, the model does well on few-shot prompts. For both classification and extraction, the model performs even better with few shots, as in most HELM tasks. [Contact us](https://www.together.xyz/contact) if you’re interested in trying few-shot prompts with the model.
## Weaknesses of the model
That said, there are several areas where we have more work to do, and we need your help! Some of these include:
- Knowledge-based closed question and answering: The chatbot may hallucinate and give incorrect results. Be sure to fact check, and if possible provide feedback with the corrected information.
- Coding tasks: The chatbot was not trained on a large enough corpus of source code to excel at writing code. We welcome contributions of additional datasets to improve this!
- Repetition: Sometimes the chatbot will repeat its response. We’re working to improve this, but in the meantime you can click the refresh button to start a new conversation.
- Context switching: If you change the topic in the middle of a conversation the chatbot often cannot make the switch automatically and will continue to give answers related to the prior topic.
- Creative writing and longer answers: The chatbot does not generate long, creative text such as an essay or story.
We are excited to work with you to address these weaknesses by getting your feedback, bolstering data sets, and improving accuracy.
# Uses
## Direct Use
The model is intended for research purposes. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of dialogue models or language models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on dialogue models or language models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
The OpenChatKit community provides GPT-NeoXT-Chat-Base-20B as an open source tool for building chatbots.
The community is not responsible for any misuse, malicious use, or out-of-scope use of the model.
It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
#### Out-of-Scope Use
GPT-NeoXT-Chat-Base-20B is designed for use in chatbot applications and may not perform well for other use cases outside of its intended scope.
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
It is important to consider the limitations of the model and to only use it for its intended purpose.
#### Misuse and Malicious Use
GPT-NeoXT-Chat-Base-20B is designed for use in chatbot applications and should not be used for any other purpose.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the OpenChatKit community project.
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
## Limitations
GPT-NeoXT-Chat-Base-20B, like other language model-based chatbots, has limitations that should be taken into consideration.
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
## Training
**Training Data**
Please refer to [togethercomputer/OpenDataHub](https://github.com/togethercomputer/OpenDataHub)
**Training Procedure**
- **Hardware:** 2 x 8 x A100 GPUs
- **Optimizer:** [8bit-AdamW](https://github.com/TimDettmers/bitsandbytes)
- **Gradient Accumulations**: 2
- **Batch:** 2 x 2 x 64 x 2048 = 524288 tokens
- **Learning rate:** warmup to 1e-6 for 100 steps and then kept constant
## Community
Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
|
vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-60000
|
vocabtrimmer
| 2023-03-27T22:46:36Z | 99 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T22:24:17Z |
# 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-60000`
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-60000 |
|:---------------------------|:----------------------------------|:-----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 416,268,288 |
| parameter_size_embedding | 512,057,344 | 122,888,192 |
| vocab_size | 250,028 | 60,004 |
| 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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 60000 | 2 |
|
yemoncad/distilbert-base-uncased-finetuned-clinc
|
yemoncad
| 2023-03-27T22:34:16Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-27T22:28:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 |
| 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 |
| 1.5483 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.015 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.7953 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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}
}
```
|
vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-30000
|
vocabtrimmer
| 2023-03-27T22:22:01Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T21:56:32Z |
# 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-30000`
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-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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 30000 | 2 |
|
SharpNLight/q-taxi-v3-simple
|
SharpNLight
| 2023-03-27T22:13:37Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T22:13:36Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3-simple
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 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="SharpNLight/q-taxi-v3-simple", 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
|
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 |
|
hongdoubao/flan-t5-base-samsum
|
hongdoubao
| 2023-03-27T21:53:07Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T21:26:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: test
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 46.8948
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3794
- Rouge1: 46.8948
- Rouge2: 23.4445
- Rougel: 39.5763
- Rougelsum: 43.209
- Gen Len: 17.2540
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 231 | 1.3935 | 46.6142 | 23.0937 | 39.1018 | 42.8696 | 17.2076 |
| No log | 2.0 | 462 | 1.3848 | 46.5553 | 23.0122 | 39.1493 | 42.764 | 17.1465 |
| 1.4249 | 3.0 | 693 | 1.3813 | 46.8705 | 23.5239 | 39.6689 | 43.2545 | 17.2930 |
| 1.4249 | 4.0 | 924 | 1.3801 | 46.9726 | 23.6143 | 39.6028 | 43.3278 | 17.2112 |
| 1.3528 | 5.0 | 1155 | 1.3794 | 46.8948 | 23.4445 | 39.5763 | 43.209 | 17.2540 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.10.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vorcefulbeans/NeapGPT
|
vorcefulbeans
| 2023-03-27T21:25:11Z | 0 | 0 | null |
[
"en",
"dataset:tencups/gpt2",
"dataset:pietrolesci/gpt3_nli",
"region:us"
] | null | 2023-03-27T21:19:04Z |
---
datasets:
- tencups/gpt2
- pietrolesci/gpt3_nli
language:
- en
---
|
Melanit/dreambooth_eighties_cars
|
Melanit
| 2023-03-27T21:24:40Z | 5 | 0 |
keras
|
[
"keras",
"tf-keras",
"keras-dreambooth",
"wildcard",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-03-27T20:19:31Z |
---
library_name: keras
tags:
- keras-dreambooth
- wildcard
license: cc-by-nc-4.0
---
## Model description
This Stable-Diffusion Model has been fine-tuned on images of Cars from Start to End of the 1980s.
Here are some examples using the following Hyperparameters:
Prompt: photo of eighties_cars car, high quality, 8k
Negative Prompt: bad, ugly, malformed, deformed, out of frame, blurry, cropped, noisy
Denoising Steps: 50
Guidance Scale: 7.5
## Intended uses & limitations
Anyone may use this model for non-commercial usecases under the Linked License, as long as Paragraph 5 of the [Open RAIL-M License](https://raw.githubusercontent.com/CompVis/stable-diffusion/main/LICENSE) are respected as well. The original Model adheres under Open RAIL-M.
It was made solely as an experiment for keras_cv Dreambooth Training.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
|
vocabtrimmer/mbart-large-cc25-jaquad-qa-trimmed-ja-10000
|
vocabtrimmer
| 2023-03-27T21:19:37Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T20:56:54Z |
# 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-10000`
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-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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 10000 | 2 |
|
vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-15000
|
vocabtrimmer
| 2023-03-27T21:18:32Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T20:22:28Z |
# 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-15000`
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-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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 15000 | 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
---
|
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.
|
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)])
```
|
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
|
drfdr/ProtogenNova_2GB
|
drfdr
| 2023-03-27T20:23:17Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T20:20:43Z |
---
license: creativeml-openrail-m
---
|
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/projectSekaiMizuki_mizukiAkiyamaVer4
|
LarryAIDraw
| 2023-03-27T19:54:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T19:32:52Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/8047/project-sekai-mizuki-akiyama-loha
|
LarryAIDraw/chitandaEruHyouka_v1
|
LarryAIDraw
| 2023-03-27T19:54:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T19:33:14Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/24787/chitanda-eru-hyouka
|
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
|
LarryAIDraw/SukoyaKana_v10
|
LarryAIDraw
| 2023-03-27T19:49:56Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-27T19:48:51Z |
---
license: creativeml-openrail-m
---
|
kasseev/dqn-SpaceInvadersNoFrameskip-v4
|
kasseev
| 2023-03-27T19:38:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T19:37:25Z |
---
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: 374.00 +/- 214.89
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 kasseev -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 kasseev -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 kasseev
```
## 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', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
vocabtrimmer/mbart-large-cc25-esquad-qa-trimmed-es-5000
|
vocabtrimmer
| 2023-03-27T19:29:55Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-27T18:27:33Z |
# 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-5000`
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-5000 |
|:---------------------------|:----------------------------------|:----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 359,948,288 |
| parameter_size_embedding | 512,057,344 | 10,248,192 |
| vocab_size | 250,028 | 5,004 |
| 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 |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 5000 | 2 |
|
Zaleks/ppo-LunarLander-v2
|
Zaleks
| 2023-03-27T19:02:23Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T16:47:19Z |
---
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: 283.04 +/- 15.33
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
...
```
|
Yureeh/ppo-Pyramids
|
Yureeh
| 2023-03-27T18:52:36Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-27T18:52:31Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** 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-Pyramids
2. Step 1: Find your model_id: Yureeh/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Pranjalya/lunar-lander-v2-ppo
|
Pranjalya
| 2023-03-27T18:47:03Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T18:46:32Z |
---
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: 252.15 +/- 44.85
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
...
```
|
DeathReaper0965/Huggy-the-Puppo
|
DeathReaper0965
| 2023-03-27T18:46:40Z | 19 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents",
"Puppo",
"region:us"
] |
reinforcement-learning
| 2023-03-27T06:19:31Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents
- Puppo
---
# **PPO** Agent playing **Huggy**
This is a trained model of a **PPO** agent playing **Huggy** 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
### Resume training
Below code can be used to resume the Agent training:
```
mlagents-learn <your_configuration_file_path.yaml> --run-id="Huggy-the-Puppo" --resume
```
### Watch Agent
You can watch the agent playing by following the below steps mentioned below:
1. Go to: https://huggingface.co/spaces/ThomasSimonini/Huggy
2. Step 1: Find the model_id: `deathReaper0965/Huggy-the-Puppo`
3. Step 2: Select the *.onnx file
4. Click on `Play with Huggy`
|
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
...
```
|
FBM/a2c-PandaReachDense-v2
|
FBM
| 2023-03-27T18:25:04Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-24T12:43:35Z |
---
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: -0.87 +/- 0.22
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
...
```
|
rickbox/test-c
|
rickbox
| 2023-03-27T18:00:24Z | 30 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-27T17:55:43Z |
---
license: creativeml-openrail-m
---
|
Noam-Shamir/rare-puppers
|
Noam-Shamir
| 2023-03-27T17:50:52Z | 223 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-27T17:50:41Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.939393937587738
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
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)
```
|
murkasad/detr-resnet-50_finetuned_cppe5
|
murkasad
| 2023-03-27T17:44:58Z | 188 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-03-27T16:21:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 10
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
SpookyWooky5/Reinforce-v2
|
SpookyWooky5
| 2023-03-27T17:28:44Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T14:57:03Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.60 +/- 27.26
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
|
AgentPublic/camembert-base-squadFR-fquad-piaf
|
AgentPublic
| 2023-03-27T17:24:27Z | 2,250 | 28 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"question-answering",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: fr
datasets:
- piaf
- FQuAD
- SQuAD-FR
widget:
- text: "Comment s'appelle le portail open data du gouvernement ?"
context: "Etalab est une administration publique française qui fait notamment office de Chief Data Officer de l'État et coordonne la conception et la mise en œuvre de sa stratégie dans le domaine de la donnée (ouverture et partage des données publiques ou open data, exploitation des données et intelligence artificielle...). Ainsi, Etalab développe et maintient le portail des données ouvertes du gouvernement français data.gouv.fr.
Etalab promeut également une plus grande ouverture l'administration sur la société (gouvernement ouvert) : transparence de l'action publique, innovation ouverte, participation citoyenne... elle promeut l’innovation, l’expérimentation, les méthodes de travail ouvertes, agiles et itératives, ainsi que les synergies avec la société civile pour décloisonner l’administration et favoriser l’adoption des meilleures pratiques professionnelles dans le domaine du numérique. À ce titre elle étudie notamment l’opportunité de recourir à des technologies en voie de maturation issues du monde de la recherche.
Cette entité chargée de l'innovation au sein de l'administration doit contribuer à l'amélioration du service public grâce au numérique. Elle est rattachée à la Direction interministérielle du numérique, dont les missions et l’organisation ont été fixées par le décret du 30 octobre 2019. Dirigé par Laure Lucchesi depuis 2016, elle rassemble une équipe pluridisciplinaire d'une trentaine de personnes."
---
# camembert-base-squadFR-fquad-piaf
## Description
Question-answering French model, using base [CamemBERT](https://camembert-model.fr/) fine-tuned on a combo of three French Q&A datasets:
1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/)
2. [FQuADv1.0](https://fquad.illuin.tech/)
3. [SQuAD-FR (SQuAD automatically translated to French)](https://github.com/Alikabbadj/French-SQuAD)
## Training hyperparameters
```shell
python run_squad.py \
--model_type camembert \
--model_name_or_path camembert-base \
--do_train --do_eval \
--train_file data/SQuAD+fquad+piaf.json \
--predict_file data/fquad_valid.json \
--per_gpu_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 4 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps 10000
```
## Evaluation results
### FQuAD v1.0 Evaluation
```shell
{"f1": 79.81, "exact_match": 55.14}
```
### SQuAD-FR Evaluation
```shell
{"f1": 80.61, "exact_match": 59.54}
```
## Usage
```python
from transformers import pipeline
nlp = pipeline('question-answering', model='etalab-ia/camembert-base-squadFR-fquad-piaf', tokenizer='etalab-ia/camembert-base-squadFR-fquad-piaf')
nlp({
'question': "Qui est Claude Monet?",
'context': "Claude Monet, né le 14 novembre 1840 à Paris et mort le 5 décembre 1926 à Giverny, est un peintre français et l’un des fondateurs de l'impressionnisme."
})
```
## Acknowledgments
This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224).
## Citations
### PIAF
```
@inproceedings{KeraronLBAMSSS20,
author = {Rachel Keraron and
Guillaume Lancrenon and
Mathilde Bras and
Fr{\'{e}}d{\'{e}}ric Allary and
Gilles Moyse and
Thomas Scialom and
Edmundo{-}Pavel Soriano{-}Morales and
Jacopo Staiano},
title = {Project {PIAF:} Building a Native French Question-Answering Dataset},
booktitle = {{LREC}},
pages = {5481--5490},
publisher = {European Language Resources Association},
year = {2020}
}
```
### FQuAD
```
@article{dHoffschmidt2020FQuADFQ,
title={FQuAD: French Question Answering Dataset},
author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich},
journal={ArXiv},
year={2020},
volume={abs/2002.06071}
}
```
### SQuAD-FR
```
@MISC{kabbadj2018,
author = "Kabbadj, Ali",
title = "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ",
editor = "linkedin.com",
month = "November",
year = "2018",
url = "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}",
note = "[Online; posted 11-November-2018]",
}
```
### CamemBERT
HF model card : [https://huggingface.co/camembert-base](https://huggingface.co/camembert-base)
```
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
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
|
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)
|
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
|
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