modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
samaresh55/autotrain-finance_data_classification-2694580522 | samaresh55 | 2023-01-06T08:53:08Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:samaresh55/autotrain-data-finance_data_classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-03T05:28:40Z | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- samaresh55/autotrain-data-finance_data_classification
co2_eq_emissions:
emissions: 4.221526489857838
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2694580522
- CO2 Emissions (in grams): 4.2215
## Validation Metrics
- Loss: 0.227
- Accuracy: 0.950
- Macro F1: 0.931
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.956
- Micro Precision: 0.950
- Weighted Precision: 0.950
- Macro Recall: 0.914
- Micro Recall: 0.950
- Weighted Recall: 0.950
## 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/samaresh55/autotrain-finance_data_classification-2694580522
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("samaresh55/autotrain-finance_data_classification-2694580522", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("samaresh55/autotrain-finance_data_classification-2694580522", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt",truncation=True)
outputs = model(**inputs)
``` |
muhtasham/small-mlm-glue-rte-custom-tokenizer | muhtasham | 2023-01-06T08:46:57Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-06T08:35:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-rte-custom-tokenizer
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. -->
# small-mlm-glue-rte-custom-tokenizer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7825
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0178 | 1.6 | 500 | 6.5548 |
| 6.2645 | 3.21 | 1000 | 6.7443 |
| 6.1376 | 4.81 | 1500 | 6.6115 |
| 5.9631 | 6.41 | 2000 | 6.7825 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
jungwhank/ppo-LunarLander-v2 | jungwhank | 2023-01-06T08:28:06Z | 6 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T08:27:44Z | ---
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: 261.17 +/- 19.70
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
...
```
|
PaddlePaddle/ernie-layoutx-base-uncased | PaddlePaddle | 2023-01-06T07:58:48Z | 0 | 13 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie_layout",
"en",
"zh",
"arxiv:2210.06155",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T07:45:21Z | ---
library_name: paddlenlp
license: apache-2.0
language:
- en
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/ernie-layoutx-base-uncased
## Introduction
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.
However, most existing methods lack the systematic mining and utilization of layout-centered knowledge, leading to sub-optimal performances.
In this paper, we propose ERNIE-Layout, a novel document pre-training solution with layout knowledge enhancement in the whole workflow,
to learn better representations that combine the features from text, layout, and image. Specifically, we first rearrange input sequences
in the serialization stage, and then present a correlative pre-training task, reading order prediction, to learn the proper reading order of documents.
To improve the layout awareness of the model, we integrate a spatial-aware disentangled attention into the multi-modal transformer and
a replaced regions prediction task into the pre-training phase. Experimental results show that ERNIE-Layout achieves superior performance
on various downstream tasks, setting new state-of-the-art on key information extraction, document image classification, and document question answering datasets.
More detail: https://arxiv.org/abs/2210.06155
## Available Models
- ernie-layoutx-base-uncased
## How to Use?
Click on the *Use in paddlenlp* button on the top right!
## Citation Info
```text
@article{ernie2.0,
title = {ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding},
author = {Peng, Qiming and Pan, Yinxu and Wang, Wenjin and Luo, Bin and Zhang, Zhenyu and Huang, Zhengjie and Hu, Teng and Yin, Weichong and Chen, Yongfeng and Zhang, Yin and Feng, Shikun and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:2210.06155},
year = {2022},
}
``` |
cleanrl/DoubleDunk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1 | cleanrl | 2023-01-06T07:43:05Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"DoubleDunk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T07:43:01Z | ---
tags:
- DoubleDunk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DoubleDunk-v5
type: DoubleDunk-v5
metrics:
- type: mean_reward
value: -0.20 +/- 0.60
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DoubleDunk-v5**
This is a trained model of a PPO agent playing DoubleDunk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id DoubleDunk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/DoubleDunk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/DoubleDunk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DoubleDunk-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id DoubleDunk-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'DoubleDunk-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
takapy/xlm-roberta-base-finetuned-panx-de | takapy | 2023-01-06T07:41:57Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-06T07:14:52Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- 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-de
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: 0.1358
- F1: 0.8638
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
PaddlePaddle/uie-senta-medium | PaddlePaddle | 2023-01-06T07:38:07Z | 0 | 0 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T04:08:33Z | ---
library_name: paddlenlp
license: apache-2.0
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-senta-medium
Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision making, public opinion mining, personalized recommendation and so on.
According to the analysis granularity, it can be roughly divided into three categories: document-level sentiment analysis, sentence-level sentiment analysis and aspect-level sentiment analysis. Among them, aspect-level sentiment analysis includes multiple subtasks, such as aspect term extraction, opinion term extraction, aspect-opinion-sentiment triplet extraction, etc.
UIE-Senta is a type of Chinese sentiment analysis model, which uses UIE as backbone and further trained based on large amount of samples related to sentiment analysis. So it has a stronger ability to understand sentiment knowledge and handle the related samples. Currently, UIE-Senta supports most of basic sentiment analysis capabilities, including sentiment-level sentiment classification, aspect-term extraction, opinion-term extraction, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion-sentiment triple extraction. You could perform sentiment analysis with UIE-Senta to improve your business analysis capabilities.
<div align="center">
<img src="https://user-images.githubusercontent.com/35913314/199965793-f0933baa-5b82-47da-9271-ba36642119f8.png" />
</div>
## Available Models
| Model Name | Model Config |
| :---------------: | :-----------------------------: |
| `uie-senta-base` | 12-layers, 768-hidden, 12-heads |
| `uie-senta-medium` | 6-layers, 768-hidden, 12-heads |
| `uie-senta-mini` | 6-layers, 384-hidden, 12-heads |
| `uie-senta-micro` | 4-layers, 384-hidden, 12-heads |
| `uie-senta-nano` | 4-layers, 312-hidden, 12-heads |
## Performance on Text Dataset
We conducted experiments to compare the performance different Models based on a in-house test set, which containing samples from multiple fields, such as hotel, restaurant，clothes and so. The comparison results are as follows.
| Model Name | Precision | Recall | F1 |
| :----------------: | :--------: | :--------: | :--------: |
| `uie-senta-base` | 0.93403 | 0.92795 | 0.93098 |
| `uie-senta-medium` | 0.93146 | 0.92137 | 0.92639 |
| `uie-senta-mini` | 0.91799 | 0.92028 | 0.91913 |
| `uie-senta-micro` | 0.91542 | 0.90957 | 0.91248 |
| `uie-senta-nano` | 0.90817 | 0.90878 | 0.90847 |
> Detailed Info: https://github.com/1649759610/PaddleNLP/tree/develop/applications/sentiment_analysis/unified_sentiment_extraction
|
PaddlePaddle/ernie-2.0-base-zh | PaddlePaddle | 2023-01-06T07:35:11Z | 0 | 0 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"arxiv:1907.12412",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T03:06:45Z | ---
library_name: paddlenlp
license: apache-2.0
language:
- zh
---
# PaddlePaddle/ernie-2.0-base-zh
## Introduction
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring,
there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness and discourse relations.
In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese.
More detail: https://arxiv.org/abs/1907.12412
## Available Models
- ernie-2.0-base-en
- ernie-2.0-large-en
- ernie-2.0-base-zh
- ernie-2.0-large-zh
## How to Use?
Click on the *Use in paddlenlp* button on the top right!
## Citation Info
```text
@article{ernie2.0,
title = {ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
author = {Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:1907.12412},
year = {2019},
}
``` |
PaddlePaddle/ernie-2.0-base-en | PaddlePaddle | 2023-01-06T07:34:32Z | 0 | 1 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"en",
"arxiv:1907.12412",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T03:14:56Z | ---
library_name: paddlenlp
license: apache-2.0
language:
- en
---
# PaddlePaddle/ernie-2.0-base-en
## Introduction
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring,
there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness and discourse relations.
In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese.
More detail: https://arxiv.org/abs/1907.12412
## Available Models
- ernie-2.0-base-en
- ernie-2.0-large-en
- ernie-2.0-base-zh
- ernie-2.0-large-zh
## How to Use?
Click on the *Use in paddlenlp* button on the top right!
## Citation Info
```text
@article{ernie2.0,
title = {ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
author = {Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:1907.12412},
year = {2019},
}
``` |
PaddlePaddle/uie-senta-base | PaddlePaddle | 2023-01-06T07:16:04Z | 0 | 2 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T04:07:09Z | ---
library_name: paddlenlp
license: apache-2.0
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-senta-base
Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision making, public opinion mining, personalized recommendation and so on.
According to the analysis granularity, it can be roughly divided into three categories: document-level sentiment analysis, sentence-level sentiment analysis and aspect-level sentiment analysis. Among them, aspect-level sentiment analysis includes multiple subtasks, such as aspect term extraction, opinion term extraction, aspect-opinion-sentiment triplet extraction, etc.
UIE-Senta is a type of Chinese sentiment analysis model, which uses UIE as backbone and further trained based on large amount of samples related to sentiment analysis. So it has a stronger ability to understand sentiment knowledge and handle the related samples. Currently, UIE-Senta supports most of basic sentiment analysis capabilities, including sentiment-level sentiment classification, aspect-term extraction, opinion-term extraction, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion-sentiment triple extraction. You could perform sentiment analysis with UIE-Senta to improve your business analysis capabilities.
<div align="center">
<img src="https://user-images.githubusercontent.com/35913314/199965793-f0933baa-5b82-47da-9271-ba36642119f8.png" />
</div>
## Available Models
| Model Name | Model Config |
| :---------------: | :-----------------------------: |
| `uie-senta-base` | 12-layers, 768-hidden, 12-heads |
| `uie-senta-medium` | 6-layers, 768-hidden, 12-heads |
| `uie-senta-mini` | 6-layers, 384-hidden, 12-heads |
| `uie-senta-micro` | 4-layers, 384-hidden, 12-heads |
| `uie-senta-nano` | 4-layers, 312-hidden, 12-heads |
## Performance on Text Dataset
We conducted experiments to compare the performance different Models based on a in-house test set, which containing samples from multiple fields, such as hotel, restaurant，clothes and so. The comparison results are as follows.
| Model Name | Precision | Recall | F1 |
| :----------------: | :--------: | :--------: | :--------: |
| `uie-senta-base` | 0.93403 | 0.92795 | 0.93098 |
| `uie-senta-medium` | 0.93146 | 0.92137 | 0.92639 |
| `uie-senta-mini` | 0.91799 | 0.92028 | 0.91913 |
| `uie-senta-micro` | 0.91542 | 0.90957 | 0.91248 |
| `uie-senta-nano` | 0.90817 | 0.90878 | 0.90847 |
> Detailed Info: https://github.com/1649759610/PaddleNLP/tree/develop/applications/sentiment_analysis/unified_sentiment_extraction
|
muhtasham/small-mlm-glue-qnli-custom-tokenizer | muhtasham | 2023-01-06T07:14:46Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-06T05:17:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-qnli-custom-tokenizer
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. -->
# small-mlm-glue-qnli-custom-tokenizer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5974
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 7.2117 | 0.4 | 500 | 6.7997 |
| 6.5734 | 0.8 | 1000 | 6.6026 |
| 6.4063 | 1.2 | 1500 | 6.5514 |
| 6.2622 | 1.6 | 2000 | 6.4092 |
| 6.2126 | 2.0 | 2500 | 6.3653 |
| 6.1191 | 2.4 | 3000 | 6.3054 |
| 6.0669 | 2.8 | 3500 | 6.2685 |
| 5.9877 | 3.2 | 4000 | 6.2077 |
| 5.8901 | 3.6 | 4500 | 6.1328 |
| 5.8306 | 4.0 | 5000 | 6.1574 |
| 5.8053 | 4.4 | 5500 | 6.0958 |
| 5.7117 | 4.8 | 6000 | 6.0377 |
| 5.7372 | 5.2 | 6500 | 6.0045 |
| 5.6595 | 5.6 | 7000 | 5.9655 |
| 5.6579 | 6.0 | 7500 | 5.9410 |
| 5.6323 | 6.4 | 8000 | 5.9121 |
| 5.5978 | 6.8 | 8500 | 5.8435 |
| 5.5634 | 7.2 | 9000 | 5.9205 |
| 5.4642 | 7.6 | 9500 | 5.8433 |
| 5.4851 | 8.0 | 10000 | 5.8122 |
| 5.4272 | 8.4 | 10500 | 5.8350 |
| 5.357 | 8.8 | 11000 | 5.7860 |
| 5.3638 | 9.2 | 11500 | 5.7262 |
| 5.3088 | 9.6 | 12000 | 5.7529 |
| 5.3052 | 10.0 | 12500 | 5.7783 |
| 5.2628 | 10.4 | 13000 | 5.7124 |
| 5.2923 | 10.8 | 13500 | 5.7053 |
| 5.1727 | 11.2 | 14000 | 5.7031 |
| 5.1474 | 11.6 | 14500 | 5.6445 |
| 5.145 | 12.0 | 15000 | 5.6299 |
| 5.102 | 12.4 | 15500 | 5.6858 |
| 5.0612 | 12.8 | 16000 | 5.6089 |
| 5.0928 | 13.2 | 16500 | 5.6404 |
| 4.9953 | 13.6 | 17000 | 5.5769 |
| 5.0163 | 14.0 | 17500 | 5.5935 |
| 4.9591 | 14.4 | 18000 | 5.5862 |
| 5.0046 | 14.8 | 18500 | 5.5974 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
ranajoy98/adverseevent_bertclassifier | ranajoy98 | 2023-01-06T06:48:06Z | 0 | 0 | null | [
"pytorch",
"text-classification",
"en",
"region:us"
] | text-classification | 2023-01-06T06:33:18Z | ---
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
--- |
PaddlePaddle/uie-base | PaddlePaddle | 2023-01-06T06:01:00Z | 3 | 19 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"arxiv:2203.12277",
"license:apache-2.0",
"region:us"
] | null | 2022-12-13T06:15:40Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-base
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.
UIE Paper: https://arxiv.org/abs/2203.12277
PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.
## Available Models
| Model Name | Usage Scenarios | Supporting Tasks |
| :----------------------------------------------------------: | :--------------------------------------------------------- | :--------------------------------------------------- |
| `uie-base`<br />`uie-medium`<br />`uie-mini`<br />`uie-micro`<br />`uie-nano` | For **plain text** The **extractive** model of the scene supports **Chinese** | Supports entity, relation, event, opinion extraction |
| `uie-base-en` | An **extractive** model for **plain text** scenarios, supports **English** | Supports entity, relation, event, opinion extraction |
| `uie-m-base`<br />`uie-m-large` | An **extractive** model for **plain text** scenarios, supporting **Chinese and English** | Supports entity, relation, event, opinion extraction |
| <b>`uie-x-base`</b> | An **extractive** model for **plain text** and **document** scenarios, supports **Chinese and English** | Supports entity, relation, event, opinion extraction on both plain text and documents/pictures/tables |
## Performance on Text Dataset
We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance:
<table>
<tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet
<tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-shot
<tr><td>uie-base (12L768H)<td>46.43<td>70.92<td><b>71.83</b><td>85.72<td>78.33<td>81.86
<tr><td>uie-medium (6L768H)<td>41.11<td>64.53<td>65.40<td>75.72<td>78.32<td>79.68
<tr><td>uie-mini (6L384H)<td>37.04<td>64.65<td>60.50<td>78.36<td>72.09<td>76.38
<tr><td>uie-micro (4L384H)<td>37.53<td>62.11<td>57.04<td>75.92<td>66.00<td>70.22
<tr><td>uie-nano (4L312H)<td>38.94<td>66.83<td>48.29<td>76.74<td>62.86<td>72.35
<tr><td>uie-m-large (24L1024H)<td><b>49.35</b><td><b>74.55</b><td>70.50<td><b>92.66</b ><td>78.49<td><b>83.02</b>
<tr><td>uie-m-base (12L768H)<td>38.46<td>74.31<td>63.37<td>87.32<td>76.27<td>80.13
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>48.84<td>73.87<td>65.60<td>88.81<td><b>79.36</b> <td>81.65
</table>
0-shot means that no training data is directly used for prediction through paddlenlp.Taskflow, and 5-shot means that each category contains 5 pieces of labeled data for model fine-tuning. Experiments show that UIE can further improve the performance with a small amount of data (few-shot).
> Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/README_en.md |
PaddlePaddle/uie-medium | PaddlePaddle | 2023-01-06T06:00:24Z | 0 | 0 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"arxiv:2203.12277",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T04:12:28Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-medium
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.
UIE Paper: https://arxiv.org/abs/2203.12277
PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.
## Available Models
| Model Name | Usage Scenarios | Supporting Tasks |
| :----------------------------------------------------------: | :--------------------------------------------------------- | :--------------------------------------------------- |
| `uie-base`<br />`uie-medium`<br />`uie-mini`<br />`uie-micro`<br />`uie-nano` | For **plain text** The **extractive** model of the scene supports **Chinese** | Supports entity, relation, event, opinion extraction |
| `uie-base-en` | An **extractive** model for **plain text** scenarios, supports **English** | Supports entity, relation, event, opinion extraction |
| `uie-m-base`<br />`uie-m-large` | An **extractive** model for **plain text** scenarios, supporting **Chinese and English** | Supports entity, relation, event, opinion extraction |
| <b>`uie-x-base`</b> | An **extractive** model for **plain text** and **document** scenarios, supports **Chinese and English** | Supports entity, relation, event, opinion extraction on both plain text and documents/pictures/tables |
## Performance on Text Dataset
We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance:
<table>
<tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet
<tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-shot
<tr><td>uie-base (12L768H)<td>46.43<td>70.92<td><b>71.83</b><td>85.72<td>78.33<td>81.86
<tr><td>uie-medium (6L768H)<td>41.11<td>64.53<td>65.40<td>75.72<td>78.32<td>79.68
<tr><td>uie-mini (6L384H)<td>37.04<td>64.65<td>60.50<td>78.36<td>72.09<td>76.38
<tr><td>uie-micro (4L384H)<td>37.53<td>62.11<td>57.04<td>75.92<td>66.00<td>70.22
<tr><td>uie-nano (4L312H)<td>38.94<td>66.83<td>48.29<td>76.74<td>62.86<td>72.35
<tr><td>uie-m-large (24L1024H)<td><b>49.35</b><td><b>74.55</b><td>70.50<td><b>92.66</b ><td>78.49<td><b>83.02</b>
<tr><td>uie-m-base (12L768H)<td>38.46<td>74.31<td>63.37<td>87.32<td>76.27<td>80.13
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>48.84<td>73.87<td>65.60<td>88.81<td><b>79.36</b> <td>81.65
</table>
0-shot means that no training data is directly used for prediction through paddlenlp.Taskflow, and 5-shot means that each category contains 5 pieces of labeled data for model fine-tuning. Experiments show that UIE can further improve the performance with a small amount of data (few-shot).
> Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/README_en.md |
PaddlePaddle/uie-micro | PaddlePaddle | 2023-01-06T05:59:39Z | 0 | 0 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"en",
"zh",
"arxiv:2203.12277",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T04:14:04Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- en
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-micro
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.
UIE Paper: https://arxiv.org/abs/2203.12277
PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.
## Available Models
| Model Name | Usage Scenarios | Supporting Tasks |
| :----------------------------------------------------------: | :--------------------------------------------------------- | :--------------------------------------------------- |
| `uie-base`<br />`uie-medium`<br />`uie-mini`<br />`uie-micro`<br />`uie-nano` | For **plain text** The **extractive** model of the scene supports **Chinese** | Supports entity, relation, event, opinion extraction |
| `uie-base-en` | An **extractive** model for **plain text** scenarios, supports **English** | Supports entity, relation, event, opinion extraction |
| `uie-m-base`<br />`uie-m-large` | An **extractive** model for **plain text** scenarios, supporting **Chinese and English** | Supports entity, relation, event, opinion extraction |
| <b>`uie-x-base`</b> | An **extractive** model for **plain text** and **document** scenarios, supports **Chinese and English** | Supports entity, relation, event, opinion extraction on both plain text and documents/pictures/tables |
## Performance on Text Dataset
We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance:
<table>
<tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet
<tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-shot
<tr><td>uie-base (12L768H)<td>46.43<td>70.92<td><b>71.83</b><td>85.72<td>78.33<td>81.86
<tr><td>uie-medium (6L768H)<td>41.11<td>64.53<td>65.40<td>75.72<td>78.32<td>79.68
<tr><td>uie-mini (6L384H)<td>37.04<td>64.65<td>60.50<td>78.36<td>72.09<td>76.38
<tr><td>uie-micro (4L384H)<td>37.53<td>62.11<td>57.04<td>75.92<td>66.00<td>70.22
<tr><td>uie-nano (4L312H)<td>38.94<td>66.83<td>48.29<td>76.74<td>62.86<td>72.35
<tr><td>uie-m-large (24L1024H)<td><b>49.35</b><td><b>74.55</b><td>70.50<td><b>92.66</b ><td>78.49<td><b>83.02</b>
<tr><td>uie-m-base (12L768H)<td>38.46<td>74.31<td>63.37<td>87.32<td>76.27<td>80.13
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>48.84<td>73.87<td>65.60<td>88.81<td><b>79.36</b> <td>81.65
</table>
0-shot means that no training data is directly used for prediction through paddlenlp.Taskflow, and 5-shot means that each category contains 5 pieces of labeled data for model fine-tuning. Experiments show that UIE can further improve the performance with a small amount of data (few-shot).
> Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/README_en.md |
PaddlePaddle/uie-m-base | PaddlePaddle | 2023-01-06T05:57:57Z | 2 | 0 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie_m",
"en",
"zh",
"arxiv:2203.12277",
"license:apache-2.0",
"region:us"
] | null | 2022-12-13T06:21:52Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- en
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-m-base
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.
UIE Paper: https://arxiv.org/abs/2203.12277
PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.
## Available Models
| Model Name | Usage Scenarios | Supporting Tasks |
| :----------------------------------------------------------: | :--------------------------------------------------------- | :--------------------------------------------------- |
| `uie-base`<br />`uie-medium`<br />`uie-mini`<br />`uie-micro`<br />`uie-nano` | For **plain text** The **extractive** model of the scene supports **Chinese** | Supports entity, relation, event, opinion extraction |
| `uie-base-en` | An **extractive** model for **plain text** scenarios, supports **English** | Supports entity, relation, event, opinion extraction |
| `uie-m-base`<br />`uie-m-large` | An **extractive** model for **plain text** scenarios, supporting **Chinese and English** | Supports entity, relation, event, opinion extraction |
| <b>`uie-x-base`</b> | An **extractive** model for **plain text** and **document** scenarios, supports **Chinese and English** | Supports entity, relation, event, opinion extraction on both plain text and documents/pictures/tables |
## Performance on Text Dataset
We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance:
<table>
<tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet
<tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-shot
<tr><td>uie-base (12L768H)<td>46.43<td>70.92<td><b>71.83</b><td>85.72<td>78.33<td>81.86
<tr><td>uie-medium (6L768H)<td>41.11<td>64.53<td>65.40<td>75.72<td>78.32<td>79.68
<tr><td>uie-mini (6L384H)<td>37.04<td>64.65<td>60.50<td>78.36<td>72.09<td>76.38
<tr><td>uie-micro (4L384H)<td>37.53<td>62.11<td>57.04<td>75.92<td>66.00<td>70.22
<tr><td>uie-nano (4L312H)<td>38.94<td>66.83<td>48.29<td>76.74<td>62.86<td>72.35
<tr><td>uie-m-large (24L1024H)<td><b>49.35</b><td><b>74.55</b><td>70.50<td><b>92.66</b ><td>78.49<td><b>83.02</b>
<tr><td>uie-m-base (12L768H)<td>38.46<td>74.31<td>63.37<td>87.32<td>76.27<td>80.13
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>48.84<td>73.87<td>65.60<td>88.81<td><b>79.36</b> <td>81.65
</table>
0-shot means that no training data is directly used for prediction through paddlenlp.Taskflow, and 5-shot means that each category contains 5 pieces of labeled data for model fine-tuning. Experiments show that UIE can further improve the performance with a small amount of data (few-shot).
## Performance on Multimodal Datasets**
We experimented on the zero-shot performance of UIE-X on the in-house multi-modal test sets in three different domains of general, financial, and medical:
<table>
<tr><th ><th>General <th>Financial<th colspan='2'>Medical
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>65.03<td>73.51<td>84.24
</table>
The general test set contains complex samples from different fields and is the most difficult task.
> Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/README_en.md |
aplnestrella/pegasus-samsum-2 | aplnestrella | 2023-01-06T05:57:42Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-01-06T04:16:52Z | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum-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. -->
# pegasus-samsum-2
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3928
## 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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8437 | 0.14 | 500 | 1.5538 |
| 1.6136 | 0.27 | 1000 | 1.4801 |
| 1.5287 | 0.41 | 1500 | 1.4405 |
| 1.6311 | 0.54 | 2000 | 1.4238 |
| 1.6707 | 0.68 | 2500 | 1.4052 |
| 1.7293 | 0.81 | 3000 | 1.3998 |
| 1.5427 | 0.95 | 3500 | 1.3928 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
PaddlePaddle/uie-x-base | PaddlePaddle | 2023-01-06T05:54:58Z | 2 | 18 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie_layout",
"en",
"zh",
"arxiv:2203.12277",
"license:apache-2.0",
"region:us"
] | null | 2022-12-13T06:40:12Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- en
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-x-base
**Try out our space at [https://huggingface.co/spaces/PaddlePaddle/UIE-X](https://huggingface.co/spaces/PaddlePaddle/UIE-X)!**
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.
UIE Paper: https://arxiv.org/abs/2203.12277
PaddleNLP released UIE model series for Information Extraction of texts and multi-modal documents which use the ERNIE 3.0 models as the pre-trained language models and were finetuned on a large amount of information extraction data.
## Available Models
| Model Name | Usage Scenarios | Supporting Tasks |
| :----------------------------------------------------------: | :--------------------------------------------------------- | :--------------------------------------------------- |
| `uie-base`<br />`uie-medium`<br />`uie-mini`<br />`uie-micro`<br />`uie-nano` | For **plain text** The **extractive** model of the scene supports **Chinese** | Supports entity, relation, event, opinion extraction |
| `uie-base-en` | An **extractive** model for **plain text** scenarios, supports **English** | Supports entity, relation, event, opinion extraction |
| `uie-m-base`<br />`uie-m-large` | An **extractive** model for **plain text** scenarios, supporting **Chinese and English** | Supports entity, relation, event, opinion extraction |
| <b>`uie-x-base`</b> | An **extractive** model for **plain text** and **document** scenarios, supports **Chinese and English** | Supports entity, relation, event, opinion extraction on both plain text and documents/pictures/tables |
## Performance on Text Dataset
We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance:
<table>
<tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet
<tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-shot
<tr><td>uie-base (12L768H)<td>46.43<td>70.92<td><b>71.83</b><td>85.72<td>78.33<td>81.86
<tr><td>uie-medium (6L768H)<td>41.11<td>64.53<td>65.40<td>75.72<td>78.32<td>79.68
<tr><td>uie-mini (6L384H)<td>37.04<td>64.65<td>60.50<td>78.36<td>72.09<td>76.38
<tr><td>uie-micro (4L384H)<td>37.53<td>62.11<td>57.04<td>75.92<td>66.00<td>70.22
<tr><td>uie-nano (4L312H)<td>38.94<td>66.83<td>48.29<td>76.74<td>62.86<td>72.35
<tr><td>uie-m-large (24L1024H)<td><b>49.35</b><td><b>74.55</b><td>70.50<td><b>92.66</b ><td>78.49<td><b>83.02</b>
<tr><td>uie-m-base (12L768H)<td>38.46<td>74.31<td>63.37<td>87.32<td>76.27<td>80.13
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>48.84<td>73.87<td>65.60<td>88.81<td><b>79.36</b> <td>81.65
</table>
0-shot means that no training data is directly used for prediction through paddlenlp.Taskflow, and 5-shot means that each category contains 5 pieces of labeled data for model fine-tuning. Experiments show that UIE can further improve the performance with a small amount of data (few-shot).
## Performance on Multimodal Datasets**
We experimented on the zero-shot performance of UIE-X on the in-house multi-modal test sets in three different domains of general, financial, and medical:
<table>
<tr><th ><th>General <th>Financial<th colspan='2'>Medical
<tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>65.03<td>73.51<td>84.24
</table>
The general test set contains complex samples from different fields and is the most difficult task.
> Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/README_en.md |
PaddlePaddle/ernie-3.0-nano-zh | PaddlePaddle | 2023-01-06T05:35:40Z | 3 | 4 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"arxiv:2107.02137",
"arxiv:2106.02241",
"arxiv:2112.12731",
"license:apache-2.0",
"region:us"
] | null | 2022-11-16T08:03:30Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/ernie-3.0-nano-zh
## Intro
[ERNIE 3.0 Models](https://github.com/paddlepaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0) are lightweight models obtained from Wenxin large model ERNIE 3.0 using distillation technology. The model structure is consistent with ERNIE 2.0, and has a stronger Chinese effect than ERNIE 2.0.
For a detailed explanation of related technologies, please refer to the article [_解析全球最大中文单体模型鹏城-百度·文心技术细节_](https://www.jiqizhixin.com/articles/2021-12-08-9)
## How to Use
Click on the "Use in paddlenlp" on the top right corner!
## Performance
ERNIE 3.0 open sources six models: **ERNIE 3.0 _XBase_**, **ERNIE 3.0 _Base_**, **ERNIE 3.0 _Medium_**, **ERNIE 3.0 _Mini_**, **ERNIE 3.0 _Micro_**, **ERNIE 3.0 _Nano_**:
- **ERNIE 3.0-_XBase_** (_20-layer, 1024-hidden, 16-heads_)
- **ERNIE 3.0-_Base_** (_12-layer, 768-hidden, 12-heads_)
- **ERNIE 3.0-_Medium_** (_6-layer, 768-hidden, 12-heads_)
- **ERNIE 3.0-_Mini_** (_6-layer, 384-hidden, 12-heads_)
- **ERNIE 3.0-_Micro_** (_4-layer, 384-hidden, 12-heads_)
- **ERNIE 3.0-_Nano_** (_4-layer, 312-hidden, 12-heads_)
Below is the **precision-latency graph** of the small Chinese models in PaddleNLP. The abscissa represents the latency (unit: ms) tested on CLUE IFLYTEK dataset (maximum sequence length is set to 128), and the ordinate is the average accuracy on 10 CLUE tasks (including text classification, text matching, natural language inference, Pronoun disambiguation, machine reading comprehension and other tasks), among which the metric of CMRC2018 is Exact Match (EM), and the metric of other tasks is Accuracy. The closer the model to the top left in the figure, the higher the level of accuracy and performance.The top left model in the figure has the highest level of accuracy and performance.
The number of parameters of the model are marked under the model name in the figure. For the test environment, see [Performance Test](https://github.com/paddlepaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0#%E6%80%A7%E8%83%BD%E6%B5%8B%E8%AF%95) in details.
precision-latency graph under CPU (number of threads: 1 and 8), batch_size = 32:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852121-2798b5c9-d122-4ac0-b4c8-da46b89b5512.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852129-bbe58835-8eec-45d5-a4a9-cc2cf9a3db6a.png"></a></td>
</tr>
</table>
precision-latency graph under CPU (number of threads: 1 and 8), batch_size = 1:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852106-658e18e7-705b-4f53-bad0-027281163ae3.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852112-4b89d675-7c95-4d75-84b6-db5a6ea95e2c.png"></a></td>
</tr>
</table>
precision-latency graph under GPU, batch_size = 32, 1:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175854679-3247f42e-8716-4a36-b5c6-9ce4661b36c7.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175854670-57878b34-c213-47ac-b620-aaaec082f435.png"></a></td>
</tr>
</table>
As can be seen from the figure, the comprehensive performance of the ERNIE Tiny 3.0 models has been comprehensively ahead of UER-py, Huawei-Noah and HFL in terms of accuracy and performance. And when batch_size=1 and the precision mode is FP16, the inference performance of the wide and shallow model on the GPU is more advantageous.
The precision data on the CLUE **validation set** are shown in the following table:
<table style="width:100%;" cellpadding="2" cellspacing="0" border="1" bordercolor="#000000">
<tbody>
<tr>
<td style="text-align:center;vertical-align:middle">
<span style="font-size:18px;">Arch</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">Model</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">AVG</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">AFQMC</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">TNEWS</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">IFLYTEK</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CMNLI</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">OCNLI</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CLUEWSC2020</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CSL</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CMRC2018</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CHID</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">C<sup>3</sup></span>
</td>
</tr>
<tr>
<td rowspan=3 align=center> 24L1024H </td>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Large-cw</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.03</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.65</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>85.09</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.73</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>93.09</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.53</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>74.22/91.88</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>88.57</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.54</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 2.0-Large-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.23</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>59.33</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.85</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">89.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.95/90.31</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">86.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.12</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoBERTa-wwm-ext-large</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.61</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.88</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.81</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">90.79</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.58/89.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">85.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.26</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 20L1024H </td>
<td style="text-align:center">
<span style="font-size:18px"><b>ERNIE 3.0-Xbase-zh</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>78.39</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.16</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>59.55</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>61.87</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.40</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.73</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>88.82</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.60</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>75.99/93.00</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>86.78</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.98</b></span>
</td>
</tr>
<tr>
<td rowspan=9 align=center> 12L768H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh.pdparams">
ERNIE 3.0-Base-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>80.10</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">86.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.71/90.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">84.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>77.88</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Base-zh-cw</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.47</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.07</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.86</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.41</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>89.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.42</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>72.88/90.78</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.68</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.98</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE-Gram-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.28</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.88</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.87</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.82/90.38</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">84.04</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.69</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">Langboat/Mengzi-BERT-Base</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.69</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.76</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.16</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.04/88.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.74</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.70</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 2.0-Base-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.65</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.25</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.08/87.46</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.19</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Base-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.84</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>58.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.25</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.68</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">85.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.32/87.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.68</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoBERTa-wwm-ext</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.60</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.92</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.49</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.39/88.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.43</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.03</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">BERT-Base-Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.57</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.30/86.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.01</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.38</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Base</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.89</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.14</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.01</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.80</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.87/84.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.76</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 8L512H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Medium</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.06</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.10</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.09</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.63/78.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.84</span>
</td>
</tr>
<tr>
<td rowspan=5 align=center> 6L768H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh.pdparams">
ERNIE 3.0-Medium-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>72.49</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>73.37</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>57.00</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>80.64</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.88</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.28</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.60</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>65.83/87.30</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>69.73</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">HLF/RBT6, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.06</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.45</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.36</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.72/84.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.85</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">TinyBERT<sub>6</sub>, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.70</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.12</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.03/83.75</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.11</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoFormerV2 Small</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>60.72</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.37</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.97/83.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.66</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.41</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-L6-H768</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.09</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.54</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.49</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.04</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.74/75.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.73</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.40</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 6L384H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh.pdparams">
ERNIE 3.0-Mini-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.85</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.24</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.19</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.30</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.53/81.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.60</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L768H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBT4, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.42</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.34</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.30/81.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.45</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L512H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Small</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.25</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.21</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.552</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.80</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">46.75/69.69</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.59</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">50.92</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L384H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh.pdparams">
ERNIE 3.0-Micro-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">64.21</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.15</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.81</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.77/77.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.53</span>
</td>
</tr>
<tr>
<td rowspan=2 align=center> 4L312H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh.pdparams">
ERNIE 3.0-Nano-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.97</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.51</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>54.57</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>48.36</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>74.97</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.61</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.75</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>75.93</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>52.00/76.35</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>58.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>55.11</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">TinyBERT<sub>4</sub>, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">39.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.94</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.59</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.07</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">46.04/69.34</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">52.18</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L256H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Mini</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.40</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">41.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.40</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.36</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">5.96/17.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">51.19</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">39.68</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 3L1024H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBTL3, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.14</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.74</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.50/80.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.03</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.56</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 3L768H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBT3, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.73/78.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.93</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 2L128H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Tiny</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">44.45</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">51.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">20.28</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.73</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.43</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">3.08/14.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">23.57</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">28.12</span>
</td>
</tr>
<tbody>
</table>
<br />
## Citation Info
```text
@article{sun2021ernie,
title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
journal={arXiv preprint arXiv:2107.02137},
year={2021}
}
@article{su2021ernie,
title={Ernie-tiny: A progressive distillation framework for pretrained transformer compression},
author={Su, Weiyue and Chen, Xuyi and Feng, Shikun and Liu, Jiaxiang and Liu, Weixin and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:2106.02241},
year={2021}
}
@article{wang2021ernie,
title={Ernie 3.0 titan: Exploring larger-scale knowledge enhanced pre-training for language understanding and generation},
author={Wang, Shuohuan and Sun, Yu and Xiang, Yang and Wu, Zhihua and Ding, Siyu and Gong, Weibao and Feng, Shikun and Shang, Junyuan and Zhao, Yanbin and Pang, Chao and others},
journal={arXiv preprint arXiv:2112.12731},
year={2021}
}
``` |
speech31/wav2vec2-large-english-TIMIT-phoneme_v3 | speech31 | 2023-01-06T05:34:43Z | 1,627 | 2 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-10-15T05:37:58Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-english-TIMIT-phoneme_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base960-english-phoneme_v3
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the TIMIT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3697
- Cer: 0.0987
## Training and evaluation data
Training: TIMIT dataset training + validation set
Evaluation: TIMIT dataset test set
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.2678 | 6.94 | 500 | 0.2347 | 0.0874 |
| 0.25 | 13.88 | 1000 | 0.3358 | 0.1122 |
| 0.2126 | 20.83 | 1500 | 0.3865 | 0.1131 |
| 0.1397 | 27.77 | 2000 | 0.4162 | 0.1085 |
| 0.0916 | 34.72 | 2500 | 0.4429 | 0.1086 |
| 0.0594 | 41.66 | 3000 | 0.3697 | 0.0987 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1.post201
- Datasets 2.5.2.dev0
- Tokenizers 0.12.1
|
PaddlePaddle/ernie-3.0-medium-zh | PaddlePaddle | 2023-01-06T05:34:16Z | 0 | 2 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"arxiv:2107.02137",
"arxiv:2106.02241",
"arxiv:2112.12731",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T03:24:25Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/ernie-3.0-medium-zh
## Intro
[ERNIE 3.0 Models](https://github.com/paddlepaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0) are lightweight models obtained from Wenxin large model ERNIE 3.0 using distillation technology. The model structure is consistent with ERNIE 2.0, and has a stronger Chinese effect than ERNIE 2.0.
For a detailed explanation of related technologies, please refer to the article [_解析全球最大中文单体模型鹏城-百度·文心技术细节_](https://www.jiqizhixin.com/articles/2021-12-08-9)
## How to Use
Click on the "Use in paddlenlp" on the top right corner!
## Performance
ERNIE 3.0 open sources six models: **ERNIE 3.0 _XBase_**, **ERNIE 3.0 _Base_**, **ERNIE 3.0 _Medium_**, **ERNIE 3.0 _Mini_**, **ERNIE 3.0 _Micro_**, **ERNIE 3.0 _Nano_**:
- **ERNIE 3.0-_XBase_** (_20-layer, 1024-hidden, 16-heads_)
- **ERNIE 3.0-_Base_** (_12-layer, 768-hidden, 12-heads_)
- **ERNIE 3.0-_Medium_** (_6-layer, 768-hidden, 12-heads_)
- **ERNIE 3.0-_Mini_** (_6-layer, 384-hidden, 12-heads_)
- **ERNIE 3.0-_Micro_** (_4-layer, 384-hidden, 12-heads_)
- **ERNIE 3.0-_Nano_** (_4-layer, 312-hidden, 12-heads_)
Below is the **precision-latency graph** of the small Chinese models in PaddleNLP. The abscissa represents the latency (unit: ms) tested on CLUE IFLYTEK dataset (maximum sequence length is set to 128), and the ordinate is the average accuracy on 10 CLUE tasks (including text classification, text matching, natural language inference, Pronoun disambiguation, machine reading comprehension and other tasks), among which the metric of CMRC2018 is Exact Match (EM), and the metric of other tasks is Accuracy. The closer the model to the top left in the figure, the higher the level of accuracy and performance.The top left model in the figure has the highest level of accuracy and performance.
The number of parameters of the model are marked under the model name in the figure. For the test environment, see [Performance Test](https://github.com/paddlepaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0#%E6%80%A7%E8%83%BD%E6%B5%8B%E8%AF%95) in details.
precision-latency graph under CPU (number of threads: 1 and 8), batch_size = 32:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852121-2798b5c9-d122-4ac0-b4c8-da46b89b5512.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852129-bbe58835-8eec-45d5-a4a9-cc2cf9a3db6a.png"></a></td>
</tr>
</table>
precision-latency graph under CPU (number of threads: 1 and 8), batch_size = 1:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852106-658e18e7-705b-4f53-bad0-027281163ae3.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852112-4b89d675-7c95-4d75-84b6-db5a6ea95e2c.png"></a></td>
</tr>
</table>
precision-latency graph under GPU, batch_size = 32, 1:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175854679-3247f42e-8716-4a36-b5c6-9ce4661b36c7.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175854670-57878b34-c213-47ac-b620-aaaec082f435.png"></a></td>
</tr>
</table>
As can be seen from the figure, the comprehensive performance of the ERNIE Tiny 3.0 models has been comprehensively ahead of UER-py, Huawei-Noah and HFL in terms of accuracy and performance. And when batch_size=1 and the precision mode is FP16, the inference performance of the wide and shallow model on the GPU is more advantageous.
The precision data on the CLUE **validation set** are shown in the following table:
<table style="width:100%;" cellpadding="2" cellspacing="0" border="1" bordercolor="#000000">
<tbody>
<tr>
<td style="text-align:center;vertical-align:middle">
<span style="font-size:18px;">Arch</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">Model</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">AVG</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">AFQMC</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">TNEWS</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">IFLYTEK</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CMNLI</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">OCNLI</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CLUEWSC2020</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CSL</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CMRC2018</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CHID</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">C<sup>3</sup></span>
</td>
</tr>
<tr>
<td rowspan=3 align=center> 24L1024H </td>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Large-cw</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.03</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.65</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>85.09</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.73</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>93.09</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.53</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>74.22/91.88</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>88.57</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.54</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 2.0-Large-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.23</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>59.33</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.85</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">89.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.95/90.31</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">86.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.12</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoBERTa-wwm-ext-large</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.61</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.88</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.81</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">90.79</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.58/89.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">85.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.26</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 20L1024H </td>
<td style="text-align:center">
<span style="font-size:18px"><b>ERNIE 3.0-Xbase-zh</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>78.39</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.16</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>59.55</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>61.87</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.40</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.73</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>88.82</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.60</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>75.99/93.00</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>86.78</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.98</b></span>
</td>
</tr>
<tr>
<td rowspan=9 align=center> 12L768H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh.pdparams">
ERNIE 3.0-Base-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>80.10</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">86.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.71/90.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">84.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>77.88</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Base-zh-cw</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.47</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.07</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.86</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.41</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>89.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.42</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>72.88/90.78</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.68</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.98</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE-Gram-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.28</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.88</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.87</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.82/90.38</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">84.04</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.69</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">Langboat/Mengzi-BERT-Base</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.69</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.76</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.16</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.04/88.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.74</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.70</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 2.0-Base-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.65</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.25</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.08/87.46</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.19</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Base-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.84</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>58.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.25</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.68</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">85.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.32/87.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.68</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoBERTa-wwm-ext</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.60</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.92</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.49</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.39/88.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.43</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.03</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">BERT-Base-Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.57</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.30/86.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.01</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.38</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Base</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.89</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.14</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.01</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.80</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.87/84.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.76</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 8L512H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Medium</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.06</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.10</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.09</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.63/78.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.84</span>
</td>
</tr>
<tr>
<td rowspan=5 align=center> 6L768H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh.pdparams">
ERNIE 3.0-Medium-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>72.49</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>73.37</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>57.00</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>80.64</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.88</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.28</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.60</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>65.83/87.30</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>69.73</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">HLF/RBT6, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.06</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.45</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.36</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.72/84.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.85</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">TinyBERT<sub>6</sub>, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.70</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.12</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.03/83.75</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.11</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoFormerV2 Small</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>60.72</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.37</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.97/83.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.66</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.41</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-L6-H768</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.09</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.54</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.49</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.04</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.74/75.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.73</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.40</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 6L384H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh.pdparams">
ERNIE 3.0-Mini-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.85</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.24</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.19</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.30</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.53/81.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.60</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L768H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBT4, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.42</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.34</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.30/81.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.45</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L512H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Small</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.25</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.21</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.552</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.80</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">46.75/69.69</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.59</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">50.92</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L384H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh.pdparams">
ERNIE 3.0-Micro-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">64.21</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.15</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.81</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.77/77.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.53</span>
</td>
</tr>
<tr>
<td rowspan=2 align=center> 4L312H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh.pdparams">
ERNIE 3.0-Nano-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.97</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.51</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>54.57</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>48.36</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>74.97</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.61</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.75</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>75.93</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>52.00/76.35</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>58.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>55.11</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">TinyBERT<sub>4</sub>, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">39.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.94</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.59</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.07</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">46.04/69.34</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">52.18</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L256H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Mini</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.40</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">41.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.40</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.36</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">5.96/17.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">51.19</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">39.68</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 3L1024H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBTL3, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.14</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.74</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.50/80.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.03</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.56</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 3L768H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBT3, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.73/78.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.93</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 2L128H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Tiny</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">44.45</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">51.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">20.28</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.73</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.43</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">3.08/14.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">23.57</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">28.12</span>
</td>
</tr>
<tbody>
</table>
<br />
## Citation Info
```text
@article{sun2021ernie,
title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
journal={arXiv preprint arXiv:2107.02137},
year={2021}
}
@article{su2021ernie,
title={Ernie-tiny: A progressive distillation framework for pretrained transformer compression},
author={Su, Weiyue and Chen, Xuyi and Feng, Shikun and Liu, Jiaxiang and Liu, Weixin and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:2106.02241},
year={2021}
}
@article{wang2021ernie,
title={Ernie 3.0 titan: Exploring larger-scale knowledge enhanced pre-training for language understanding and generation},
author={Wang, Shuohuan and Sun, Yu and Xiang, Yang and Wu, Zhihua and Ding, Siyu and Gong, Weibao and Feng, Shikun and Shang, Junyuan and Zhao, Yanbin and Pang, Chao and others},
journal={arXiv preprint arXiv:2112.12731},
year={2021}
}
``` |
PaddlePaddle/ernie-3.0-mini-zh | PaddlePaddle | 2023-01-06T05:33:54Z | 0 | 1 | paddlenlp | [
"paddlenlp",
"paddlepaddle",
"ernie",
"zh",
"arxiv:2107.02137",
"arxiv:2106.02241",
"arxiv:2112.12731",
"license:apache-2.0",
"region:us"
] | null | 2023-01-06T03:25:14Z | ---
license: apache-2.0
library_name: paddlenlp
language:
- zh
---
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/ernie-3.0-mini-zh
## Intro
[ERNIE 3.0 Models](https://github.com/paddlepaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0) are lightweight models obtained from Wenxin large model ERNIE 3.0 using distillation technology. The model structure is consistent with ERNIE 2.0, and has a stronger Chinese effect than ERNIE 2.0.
For a detailed explanation of related technologies, please refer to the article [_解析全球最大中文单体模型鹏城-百度·文心技术细节_](https://www.jiqizhixin.com/articles/2021-12-08-9)
## How to Use
Click on the "Use in paddlenlp" on the top right corner!
## Performance
ERNIE 3.0 open sources six models: **ERNIE 3.0 _XBase_**, **ERNIE 3.0 _Base_**, **ERNIE 3.0 _Medium_**, **ERNIE 3.0 _Mini_**, **ERNIE 3.0 _Micro_**, **ERNIE 3.0 _Nano_**:
- **ERNIE 3.0-_XBase_** (_20-layer, 1024-hidden, 16-heads_)
- **ERNIE 3.0-_Base_** (_12-layer, 768-hidden, 12-heads_)
- **ERNIE 3.0-_Medium_** (_6-layer, 768-hidden, 12-heads_)
- **ERNIE 3.0-_Mini_** (_6-layer, 384-hidden, 12-heads_)
- **ERNIE 3.0-_Micro_** (_4-layer, 384-hidden, 12-heads_)
- **ERNIE 3.0-_Nano_** (_4-layer, 312-hidden, 12-heads_)
Below is the **precision-latency graph** of the small Chinese models in PaddleNLP. The abscissa represents the latency (unit: ms) tested on CLUE IFLYTEK dataset (maximum sequence length is set to 128), and the ordinate is the average accuracy on 10 CLUE tasks (including text classification, text matching, natural language inference, Pronoun disambiguation, machine reading comprehension and other tasks), among which the metric of CMRC2018 is Exact Match (EM), and the metric of other tasks is Accuracy. The closer the model to the top left in the figure, the higher the level of accuracy and performance.The top left model in the figure has the highest level of accuracy and performance.
The number of parameters of the model are marked under the model name in the figure. For the test environment, see [Performance Test](https://github.com/paddlepaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0#%E6%80%A7%E8%83%BD%E6%B5%8B%E8%AF%95) in details.
precision-latency graph under CPU (number of threads: 1 and 8), batch_size = 32:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852121-2798b5c9-d122-4ac0-b4c8-da46b89b5512.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852129-bbe58835-8eec-45d5-a4a9-cc2cf9a3db6a.png"></a></td>
</tr>
</table>
precision-latency graph under CPU (number of threads: 1 and 8), batch_size = 1:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852106-658e18e7-705b-4f53-bad0-027281163ae3.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175852112-4b89d675-7c95-4d75-84b6-db5a6ea95e2c.png"></a></td>
</tr>
</table>
precision-latency graph under GPU, batch_size = 32, 1:
<table>
<tr>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175854679-3247f42e-8716-4a36-b5c6-9ce4661b36c7.png"></a></td>
<td><a><img src="https://user-images.githubusercontent.com/26483581/175854670-57878b34-c213-47ac-b620-aaaec082f435.png"></a></td>
</tr>
</table>
As can be seen from the figure, the comprehensive performance of the ERNIE Tiny 3.0 models has been comprehensively ahead of UER-py, Huawei-Noah and HFL in terms of accuracy and performance. And when batch_size=1 and the precision mode is FP16, the inference performance of the wide and shallow model on the GPU is more advantageous.
The precision data on the CLUE **validation set** are shown in the following table:
<table style="width:100%;" cellpadding="2" cellspacing="0" border="1" bordercolor="#000000">
<tbody>
<tr>
<td style="text-align:center;vertical-align:middle">
<span style="font-size:18px;">Arch</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">Model</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">AVG</span>
</td>
<td style="text-align:center">
<span style="font-size:18px;">AFQMC</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">TNEWS</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">IFLYTEK</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CMNLI</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">OCNLI</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CLUEWSC2020</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CSL</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CMRC2018</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">CHID</span>
</td>
<td style="text-align:center;">
<span style="font-size:18px;">C<sup>3</sup></span>
</td>
</tr>
<tr>
<td rowspan=3 align=center> 24L1024H </td>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Large-cw</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.03</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.65</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>85.09</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.73</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>93.09</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.53</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>74.22/91.88</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>88.57</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.54</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 2.0-Large-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.23</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>59.33</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.85</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">89.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.95/90.31</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">86.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.12</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoBERTa-wwm-ext-large</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.61</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.88</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.81</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">90.79</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.58/89.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">85.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.26</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 20L1024H </td>
<td style="text-align:center">
<span style="font-size:18px"><b>ERNIE 3.0-Xbase-zh</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>78.39</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.16</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>59.55</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>61.87</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.40</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.73</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>88.82</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.60</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>75.99/93.00</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>86.78</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.98</b></span>
</td>
</tr>
<tr>
<td rowspan=9 align=center> 12L768H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh.pdparams">
ERNIE 3.0-Base-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>80.10</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">86.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.71/90.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">84.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>77.88</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Base-zh-cw</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.47</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.07</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.86</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.41</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>89.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>83.42</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>72.88/90.78</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>84.68</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.98</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE-Gram-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.28</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.88</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.87</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.82/90.38</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">84.04</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.69</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">Langboat/Mengzi-BERT-Base</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.69</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.76</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.16</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.04/88.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.74</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.70</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 2.0-Base-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.65</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.25</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.08/87.46</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.19</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">ERNIE 1.0-Base-zh</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.84</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>58.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.25</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.68</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">85.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.32/87.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.68</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoBERTa-wwm-ext</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.60</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.92</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">88.49</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.39/88.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">83.43</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.03</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">BERT-Base-Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.57</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.30/86.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">82.01</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.38</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Base</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.89</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">61.14</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.01</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.58</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.80</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.87/84.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.76</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 8L512H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Medium</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.06</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.10</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.35</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.09</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.63/78.91</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.84</span>
</td>
</tr>
<tr>
<td rowspan=5 align=center> 6L768H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh.pdparams">
ERNIE 3.0-Medium-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>72.49</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>73.37</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>57.00</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>80.64</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>76.88</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.28</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>81.60</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>65.83/87.30</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>79.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>69.73</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">HLF/RBT6, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.06</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.45</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.36</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.67</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.72/84.77</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.85</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">TinyBERT<sub>6</sub>, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.62</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.70</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.12</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">80.17</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.03/83.75</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.11</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">RoFormerV2 Small</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>60.72</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.37</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">81.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.97/83.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.66</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.41</span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-L6-H768</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.09</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.54</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.49</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.00</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.04</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.74/75.52</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.73</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.40</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 6L384H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh.pdparams">
ERNIE 3.0-Mini-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.85</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.24</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.48</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.19</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">79.30</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.53/81.97</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.60</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L768H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBT4, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.42</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">72.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">77.34</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">78.23</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.30/81.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.45</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L512H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Small</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.25</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.21</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.552</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.64</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.80</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.78</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">46.75/69.69</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.59</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">50.92</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L384H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh.pdparams">
ERNIE 3.0-Micro-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">64.21</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.15</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.05</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.83</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">74.81</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.08</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.50</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.77/77.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">62.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.53</span>
</td>
</tr>
<tr>
<td rowspan=2 align=center> 4L312H </td>
<td style="text-align:center">
<span style="font-size:18px">
<a href="https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh.pdparams">
ERNIE 3.0-Nano-zh
</a>
</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>62.97</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.51</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>54.57</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>48.36</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>74.97</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.61</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">68.75</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>75.93</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>52.00/76.35</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>58.91</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>55.11</b></span>
</td>
</tr>
<tr>
<td style="text-align:center">
<span style="font-size:18px">TinyBERT<sub>4</sub>, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">60.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">39.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">73.94</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.59</span>
</td>
<td style="text-align:center">
<span style="font-size:18px"><b>70.07</b></span>
</td>
<td style="text-align:center">
<span style="font-size:18px">75.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">46.04/69.34</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">52.18</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 4L256H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Mini</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">53.40</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.32</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.22</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">41.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.40</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.36</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.07</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">5.96/17.13</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">51.19</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">39.68</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 3L1024H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBTL3, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">66.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">56.14</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.56</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.41</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.29</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.74</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.93</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">58.50/80.90</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">71.03</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.56</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 3L768H </td>
<td style="text-align:center">
<span style="font-size:18px">HFL/RBT3, Chinese</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">65.72</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.53</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.18</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.20</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.71</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.11</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">76.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">55.73/78.63</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">70.26</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">54.93</span>
</td>
</tr>
<tr>
<td rowspan=1 align=center> 2L128H </td>
<td style="text-align:center">
<span style="font-size:18px">UER/Chinese-RoBERTa-Tiny</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">44.45</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">69.02</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">51.47</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">20.28</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">59.95</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">57.73</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">63.82</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">67.43</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">3.08/14.33</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">23.57</span>
</td>
<td style="text-align:center">
<span style="font-size:18px">28.12</span>
</td>
</tr>
<tbody>
</table>
<br />
## Citation Info
```text
@article{sun2021ernie,
title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
journal={arXiv preprint arXiv:2107.02137},
year={2021}
}
@article{su2021ernie,
title={Ernie-tiny: A progressive distillation framework for pretrained transformer compression},
author={Su, Weiyue and Chen, Xuyi and Feng, Shikun and Liu, Jiaxiang and Liu, Weixin and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},
journal={arXiv preprint arXiv:2106.02241},
year={2021}
}
@article{wang2021ernie,
title={Ernie 3.0 titan: Exploring larger-scale knowledge enhanced pre-training for language understanding and generation},
author={Wang, Shuohuan and Sun, Yu and Xiang, Yang and Wu, Zhihua and Ding, Siyu and Gong, Weibao and Feng, Shikun and Shang, Junyuan and Zhao, Yanbin and Pang, Chao and others},
journal={arXiv preprint arXiv:2112.12731},
year={2021}
}
``` |
N75242/FloralMarbles_Model | N75242 | 2023-01-06T05:29:00Z | 0 | 7 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-06T00:13:12Z | ---
license: creativeml-openrail-m
---
### Model info
---
This is a dreambooth model trained with the data set of [FloralMarble](https://huggingface.co/datasets/spaablauw/FloralMarble_dataset) on top of stable diffusion 1.5, all creadits to [spaablauw](https://huggingface.co/spaablauw) for original images.
I left several models uploaded, all the intermediate steps + two anime models that I merged into.
I would recomend try [the 4000 steps model](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/FloralMarble_step_4000.ckpt) or the [7000 steps one](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/FloralMarble_step_7000.ckpt), it depends a bit in what you want, I had relly good result in booth.
For img2img 7000 step version is better.
[Download Eimis Merge](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/EimisAnimeDiffusion_1-0v_0-FloralMarble_step_3000.safetensors)
[Download Anything Merge](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/Anything-V3.0_0-FloralMarble_step_3000_1.safetensors)
Use whatever VAE you want.
---
### Examples, download images to get prompts from exif data
---
### Tag list
[Get the tag list images had here](https://huggingface.co/N75242/FloralMarbles_Model/resolve/main/tags.txt)
I used "flrmrbl" as an unique token, so it should activate the model traing data, also "floral marble" is present in all images, but its more generic si probably less powerfull.
But as an alternative use "in the style of flrmrbl" or "flrmrbl style".
Have fun!
|
muhtasham/small-mlm-glue-mrpc-custom-tokenizer | muhtasham | 2023-01-06T05:16:41Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-06T05:02:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-mrpc-custom-tokenizer
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. -->
# small-mlm-glue-mrpc-custom-tokenizer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4085
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.9986 | 1.09 | 500 | 6.7224 |
| 6.2058 | 2.18 | 1000 | 6.3947 |
| 5.981 | 3.27 | 1500 | 6.4669 |
| 5.8487 | 4.36 | 2000 | 6.6145 |
| 5.7411 | 5.45 | 2500 | 6.4085 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
muhtasham/small-mlm-glue-mnli-custom-tokenizer | muhtasham | 2023-01-06T05:00:35Z | 102 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-06T02:07:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-mnli-custom-tokenizer
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. -->
# small-mlm-glue-mnli-custom-tokenizer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6551
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 7.0308 | 0.4 | 500 | 6.6001 |
| 6.346 | 0.8 | 1000 | 6.3998 |
| 6.1061 | 1.2 | 1500 | 6.3170 |
| 5.9586 | 1.6 | 2000 | 6.2799 |
| 5.8773 | 2.0 | 2500 | 6.2034 |
| 5.7403 | 2.4 | 3000 | 6.1609 |
| 5.6602 | 2.8 | 3500 | 6.1113 |
| 5.5809 | 3.2 | 4000 | 6.1267 |
| 5.5663 | 3.6 | 4500 | 6.0647 |
| 5.6266 | 4.0 | 5000 | 6.1090 |
| 5.4756 | 4.4 | 5500 | 6.0302 |
| 5.4905 | 4.8 | 6000 | 6.0292 |
| 5.3179 | 5.2 | 6500 | 5.9758 |
| 5.3375 | 5.6 | 7000 | 6.0125 |
| 5.3035 | 6.0 | 7500 | 5.9495 |
| 5.1918 | 6.4 | 8000 | 5.9537 |
| 5.2499 | 6.8 | 8500 | 5.9100 |
| 5.1905 | 7.2 | 9000 | 5.8620 |
| 5.1787 | 7.6 | 9500 | 5.9296 |
| 5.1534 | 8.0 | 10000 | 5.9442 |
| 5.1396 | 8.4 | 10500 | 5.8609 |
| 5.1272 | 8.8 | 11000 | 5.8358 |
| 4.9615 | 9.2 | 11500 | 5.8617 |
| 5.0062 | 9.6 | 12000 | 5.8043 |
| 5.0131 | 10.0 | 12500 | 5.8119 |
| 4.9326 | 10.4 | 13000 | 5.7851 |
| 4.9655 | 10.8 | 13500 | 5.7792 |
| 4.9256 | 11.2 | 14000 | 5.7843 |
| 4.9195 | 11.6 | 14500 | 5.7652 |
| 4.8299 | 12.0 | 15000 | 5.7606 |
| 4.8748 | 12.4 | 15500 | 5.7577 |
| 4.7588 | 12.8 | 16000 | 5.7048 |
| 4.8185 | 13.2 | 16500 | 5.7245 |
| 4.7679 | 13.6 | 17000 | 5.7402 |
| 4.7377 | 14.0 | 17500 | 5.7034 |
| 4.7403 | 14.4 | 18000 | 5.7054 |
| 4.6628 | 14.8 | 18500 | 5.7203 |
| 4.6801 | 15.2 | 19000 | 5.6798 |
| 4.6014 | 15.6 | 19500 | 5.6931 |
| 4.618 | 16.0 | 20000 | 5.6620 |
| 4.6037 | 16.4 | 20500 | 5.6441 |
| 4.6004 | 16.8 | 21000 | 5.6262 |
| 4.5432 | 17.2 | 21500 | 5.6726 |
| 4.576 | 17.6 | 22000 | 5.6322 |
| 4.5568 | 18.0 | 22500 | 5.6551 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
lantianai/Stable_Diffusion_Inpainting_Mask_EulerA | lantianai | 2023-01-06T03:50:46Z | 29 | 1 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"diffusers:StableDiffusionInpaintPipeline",
"region:us"
] | text-to-image | 2023-01-06T03:37:24Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
library_name: diffusers
extra_gated_prompt: |-
One more step before getting this model.
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask.
The **Stable-Diffusion-Inpainting** was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original). First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
[](https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
:-------------------------:|:-------------------------:|
## Examples:
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```python
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
revision="fp16",
torch_dtype=torch.float16,
)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
#image and mask_image should be PIL images.
#The mask structure is white for inpainting and black for keeping as is
image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
image.save("./yellow_cat_on_park_bench.png")
```
**How it works:**
`image` | `mask_image`
:-------------------------:|:-------------------------:|
<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/>
`prompt` | `Output`
:-------------------------:|:-------------------------:|
<span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/>
### Original GitHub Repository
1. Download the weights [sd-v1-5-inpainting.ckpt](https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt)
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/runwayml/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. 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 generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
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 demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide six checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, `sd-v1-4.ckpt`, `sd-v1-5.ckpt` and `sd-v1-5-inpainting.ckpt`
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-4.ckpt`: Resumed from stable-diffusion-v1-2.225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-5.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
- `sd-v1-5-inpaint.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Inpainting Evaluation
To assess the performance of the inpainting model, we used the same evaluation
protocol as in our [LDM paper](https://arxiv.org/abs/2112.10752). Since the
Stable Diffusion Inpainting Model acccepts a text input, we simply used a fixed
prompt of `photograph of a beautiful empty scene, highest quality settings`.
| Model | FID | LPIPS |
|-----------------------------|------|------------------|
| Stable Diffusion Inpainting | 1.00 | 0.141 (+- 0.082) |
| Latent Diffusion Inpainting | 1.50 | 0.137 (+- 0.080) |
| CoModGAN | 1.82 | 0.15 |
| LaMa | 2.21 | 0.134 (+- 0.080) |
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
adakoda/sd-class-butterflies-64 | adakoda | 2023-01-06T03:48:45Z | 10 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-01-06T03:48:35Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('adakoda/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
Yiff/dayum-cuh | Yiff | 2023-01-06T03:43:44Z | 21 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-01-06T03:43:30Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: dayum-cuh
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.028037382289767265
---
# dayum-cuh
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
#### fortnite
#### fortnite characters
#### fortnite landscapes
#### fortnite pictures
#### fortnite porn
 |
adakoda/sd-class-butterflies-32 | adakoda | 2023-01-06T03:09:23Z | 32 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-01-06T03:08:44Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('adakoda/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
hucuioo/sd-class-butterflies-64 | hucuioo | 2023-01-06T02:57:38Z | 32 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-01-06T02:57:28Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('hucuioo/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
cleanrl/DemonAttack-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1 | cleanrl | 2023-01-06T02:48:48Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"DemonAttack-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T02:48:44Z | ---
tags:
- DemonAttack-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DemonAttack-v5
type: DemonAttack-v5
metrics:
- type: mean_reward
value: 88490.00 +/- 45858.13
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DemonAttack-v5**
This is a trained model of a PPO agent playing DemonAttack-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id DemonAttack-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id DemonAttack-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'DemonAttack-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
gaarsmu/PPO-LunarLenderv2_default | gaarsmu | 2023-01-06T02:42:20Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T02:41:56Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_default
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.35 +/- 20.49
name: mean_reward
verified: false
---
# **PPO_default** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_default** 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
...
```
|
obokkkk/kobigbird-bert-base-finetuned-klue | obokkkk | 2023-01-06T02:41:32Z | 95 | 0 | transformers | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-04-12T07:41:56Z | ---
tags:
- generated_from_trainer
model-index:
- name: kobigbird-bert-base-finetuned-klue
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. -->
# kobigbird-bert-base-finetuned-klue
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5589
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.888 | 13.89 | 500 | 3.5589 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
pdmct/q-Taxi-v3-base | pdmct | 2023-01-06T02:33:49Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T02:21:27Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-base
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="pdmct/q-Taxi-v3-base", 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"])
```
|
pdmct/q-FrozenLake-v1-4x4-noSlippery | pdmct | 2023-01-06T02:16:23Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T02:16:18Z | ---
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="pdmct/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"])
```
|
mnavas/hf-rl-chopperv1 | mnavas | 2023-01-06T02:01:59Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T02:00:59Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: hf-rl-chopperv1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.20 +/- 14.39
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
|
rakeshjohny/dqn-SpaceInvadersNoFrameskip | rakeshjohny | 2023-01-06T01:52:13Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T01:51:37Z | ---
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: 586.00 +/- 207.65
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 rakeshjohny -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 rakeshjohny -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 rakeshjohny
```
## 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)])
```
|
gababas/rraacchhiissbb | gababas | 2023-01-06T01:42:22Z | 36 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-01-06T01:37:31Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### rraacchhiissbb Dreambooth model trained by gababas 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:
|
bitcloud2/Reinforce-1 | bitcloud2 | 2023-01-06T01:31:48Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T01:31:39Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
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
|
Librezo/bog-001 | Librezo | 2023-01-06T01:23:10Z | 0 | 0 | null | [
"region:us"
] | null | 2023-01-05T06:19:53Z | # Libérez Adam
TD;DR
```
pip install -r requirements.txt
python3 main.py
```
## Objectifs
Créer un assistant pour aider l'équipe de Duniter et de Librezo à atteindre leurs objectifs.
Il est également question de rendre open source (donc auditable et personnalisable) un service équivalent au playground de GPT, mais en libre et utilisant des Model locaux (BoGs).
Les sources de données pour générer nos models doivent pouvoir être facilement adaptables, pour générer toutes sortes de models customs **par dessus des models pré-entrainé comme GPT2 (open-source)**.
Plus d'infos au sujet de la nature open ou non du model GPT3: https://github.com/openai/gpt-3/blob/master/model-card.md
Nous comptons pyTorch pour entrainer nos models.
Il nous est également possible de générer notre models avec pyTorch de manière optimisé, puis de laisser la boucle de machine learning à tensorFlow, qui pourrait être un peu plus performant avec certains hyperparamètres.
Celà semble donc permettre dès maintenant d'ajouter les données que nous voulons à un model pré-existant, sans frais ni limitations.
Ce sujet reste à creuser.
## Pourquoi adapter GPT en licence libre ?
Considérant le danger de laisser un outil aussi performant et inquiétant que l'IA sémantique GPT entre les mains de géant du web, Poka a demandé à GPT de transmettre son code en licence libre, ce que GPT a fait.
Il nous est donc théoriquement possible d'intégrer GPT et de l'utiliser dans sa version libre.
Cela implique un travail d'intégration, de maturation et de bidouillage, ainsi qu'un serveur relativement puissant avec une forte capacité de stockage.
## Comment faire
*Demandez à GPT3.5: https://beta.openai.com/playground*
Nous avons déjà le choix parmis plusieurs libs open source sensées effectuer la même chose que GPT3 (en partie): Du machine learning par Transformation.
pyTorch nous semble l'approche la plus simple et reconnue par les chercheurs du domaine. tensorFlow est une alternative également intérressante (python aussi), et peut être utilisé de manière complémentaire à pyTorch.
GPT nous conseille déjà sur la manière d'implémenter notre pyTorch de manière optimal pour notre besoin (se forker lui même).
Nous avons commencé à alimenter GPT en contexte pour notre projet, de manière à ce qu'il finisse par se recoder lui même totalement avec des outils open source.
## Sources de données
Pour rendre notre model réellement performant sans nécessité de faire appel à du fine tunning (réglage métier de post traitement), nous devons ajouter plus de donnée à notre model, je pense par exemple à :
- Wikipedia (international, mais avant tout FR (6Go))
- stackoverflow (todo darty scrappy)
- Toutes les documentations techniques des principaux langages de programmation, ainsi que le plus de docs de libs possible (github, gitlab)
- Ce qu'on veut, qui colorera la façon de penser et de parler de notre IA:
- Coluche
- Desproges
- Dieudo
- Bakounine
- Kropotkin
- Etienne klein
- Isaac Azimov (les robots)
- Jacques Prévert
- Diogène (les cyniques)
- Nietzsche
- Rousseau
Les sources de données sont nombreuses, nous devons penser aux retranscriptions text des vidéos qui nous intéressent (sous titre youtube).
Il faut également garder en tête que nous allons être amenés à générer plusieurs models, où nous pouvons faire varier et évoluer les sources de données d'entrée de ces différents models.
Il est probable que tout cela nécessite des montées en compétence significatives de notre part, concernant la mise bout à bout de tout le nécessaire pour arriver à un résultat intéressant.
## Matériel
C'est le point bloquant.
Pour entrainer ces models, il faut beaucoup, beaucoup de GPU et de RAM, des disque ultra performants, ou bien beaucoup, beaucoup, beaucou de temps.
Nous allons benchmarker tout celà au fur et à mesure de nos tests.
Nous aimerions tisser des partenariats institutionnels de manière à accéder à du temps de supercalculateur nationnal pour notre projet de libérer et distribuer les TIA. |
Scrwed/ppo-LunarLander-v2-trained | Scrwed | 2023-01-06T01:21:34Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-06T01:21:08Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 278.75 +/- 17.89
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ChouBERT/ChouBERT-32-plant-health-ner | ChouBERT | 2023-01-06T01:05:19Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-12-24T00:15:36Z | ---
language:
- fr
pipeline_tag: token-classification
widget:
- text: "La # pyrale du # buis à l'air très friande du # tournesol # semences."
example_title: "1 ravageur"
- text: "Quelles tactiques le producteur de la # SaskAg utilise-t-il pour protéger ses 13 800 acres de la cécidomyie du blé , de la fausse-teigne des crucifères , des vers-gris et des pucerons."
example_title: "4 ravageurs"
- text: "puceron cendré sur # colza à surveiller à l ’ automne - symptômes classiques de déformation et décoloration de feuilles ( ici en Normandie ) virus transmis : mosaïque du chou-fleur et / ou du navet ( rare )."
example_title: "Ravageur & maladie"
- text: "Traitement juste après le triage , un traitement contre la fusariose et contre la mouche grise sur cette variété car elle sera semé après betteraves."
example_title: "Maladie & ravageur"
- text: "Nous voulons des coquelicots ! Le coquelicot héberge notamment les virus de la jaunisse grave , jaunisse modérée et occidentale de la betterave , virus latent italien de l'artichaut , virus de la mosaïque du navet , virus X de la pomme de terre et le virus du flétrissement de la fève."
example_title: "5 maladies"
- text: "Plus j’ai du recul sur ma situation d’ancien taupin plus je me dis qu’il faut vraiment cramer les prepas et les écoles d’ingé/de commerce."
example_title: "Taupin - prépa"
- text: "Vous savez Taupin et ses problèmes de gonades mal hydratées ?Bah c'est aussi sec, la Loire."
example_title: "Taupin - sec"
- text: "#MercrediCestPermis je vous présente taupin et scuti. Un fléau qui va grandir avec l'arrêt des neonicotinoide. Deux ravageurs de racines qui sont friands de blé maïs pomme de terre et autres cultures Peut provoquer la perte totale. #agriculture #FrAgTW"
example_title: "Taupin - ravageur"
- text: "Thon juste saisi , crème de betterave , une petite rouille dont j'ignore la constitution , légumes de saison Ça va comme ça ? "
example_title: "Rouille - sauce"
- text: "Rouille de la # betterave sucrière causée par # Uromyces betae # urédospores # phytopathologie"
example_title: "Rouille - maladie"
- text: "Colzas qui rougissaient précocement , avec de l ’ oidium , dégâts de campagnols , mouche du chou . . ."
example_title: "Mouche - ravageur"
- text: "Vacances de Noël : les touristes français visitent Paris à bord d’un bateau mouche."
example_title: "Mouche - bateau"
---
### How to use
You can use this model directly with a pipeline for token classification:
```python
>>>from transformers import pipeline
>>>pipe = pipeline(model="ChouBERT/ChouBERT-32-plant-health-ner", aggregation_strategy="simple")
>>>pipe(" Attaque de rouille brune en Dordogne sur du blé tendre variété Oregrain !")
[]
>>>pipe("Soupe de poisson toute prête de carrefour avec fromage râpé, croûtons à l'ail et rouille #TeamFeignasse.")
[{'entity_group': 'Maladie',
'score': 0.80249035,
'word': '',
'start': 11,
'end': 12},
{'entity_group': 'Maladie',
'score': 0.80133665,
'word': 'rouille brune',
'start': 12,
'end': 25}]
```
|
Agog/LunarLander-v2 | Agog | 2023-01-06T01:01:10Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-04T14:58:42Z | ---
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: 298.53 +/- 18.94
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
...
```
|
muhtasham/small-mlm-glue-stsb | muhtasham | 2023-01-06T00:46:26Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-06T00:26:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-stsb
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. -->
# small-mlm-glue-stsb
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7187
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2666 | 0.7 | 500 | 2.7896 |
| 3.0117 | 1.39 | 1000 | 2.8245 |
| 2.9461 | 2.09 | 1500 | 2.7108 |
| 2.7341 | 2.78 | 2000 | 2.6721 |
| 2.7235 | 3.48 | 2500 | 2.6946 |
| 2.6687 | 4.17 | 3000 | 2.7103 |
| 2.5373 | 4.87 | 3500 | 2.7187 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Addwater/rl-course-unit4-cartpole | Addwater | 2023-01-06T00:42:36Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T16:54:27Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: rl-course-unit4-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
|
Buseak/model_6012023 | Buseak | 2023-01-06T00:36:05Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-05T19:40:18Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model_6012023
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. -->
# model_6012023
This model is a fine-tuned version of [Buseak/my_pos_model](https://huggingface.co/Buseak/my_pos_model) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2391
- Precision: 0.9109
- Recall: 0.9042
- F1: 0.9076
- Accuracy: 0.9348
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 244 | 0.3030 | 0.8906 | 0.8845 | 0.8875 | 0.9202 |
| No log | 2.0 | 488 | 0.2526 | 0.9051 | 0.8977 | 0.9014 | 0.9306 |
| 0.4278 | 3.0 | 732 | 0.2391 | 0.9109 | 0.9042 | 0.9076 | 0.9348 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
muhtasham/small-mlm-glue-sst2 | muhtasham | 2023-01-06T00:26:18Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-06T00:12:03Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-sst2
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. -->
# small-mlm-glue-sst2
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9876
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5439 | 0.4 | 500 | 2.9993 |
| 3.4175 | 0.8 | 1000 | 2.8910 |
| 3.2455 | 1.2 | 1500 | 2.9637 |
| 3.247 | 1.6 | 2000 | 2.9003 |
| 3.2491 | 2.0 | 2500 | 2.9876 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
muhtasham/small-mlm-glue-rte | muhtasham | 2023-01-06T00:11:20Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-05T23:59:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-rte
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. -->
# small-mlm-glue-rte
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3557
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5903 | 1.6 | 500 | 2.1820 |
| 2.4763 | 3.21 | 1000 | 2.4737 |
| 2.3778 | 4.81 | 1500 | 2.2902 |
| 2.2735 | 6.41 | 2000 | 2.3557 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Gadersd/dqn-SpaceInvadersNoFrameskip-v4 | Gadersd | 2023-01-05T23:55:56Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T23:55:22Z | ---
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: 404.50 +/- 175.35
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 Gadersd -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 Gadersd -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 Gadersd
```
## 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)])
```
|
elRivx/100Memories2.1E | elRivx | 2023-01-05T23:54:53Z | 0 | 2 | null | [
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-01-05T23:29:56Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
**100Memories2.1E**
Hi guys! Do you remember my SD 1.5 model about photos with a little bit of vintage style? I resurrect the project as a SD 2.1 embedding Some recomendations: the magic word for your prompts is 100Memories.
If you enjoy my work, please consider supporting me:
[](https://www.buymeacoffee.com/elrivx)
Examples:
<img src=https://imgur.com/3EjRdsJ.png width=30% height=30%>
<img src=https://imgur.com/YPcD8wd.png width=30% height=30%>
<img src=https://imgur.com/XzoTc2l.png width=30% height=30%>
<img src=https://imgur.com/7DfSVIT.png width=30% height=30%>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Dongyeop/distilbert-base-uncased-finetuned-clinc | Dongyeop | 2023-01-05T23:52:09Z | 105 | 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 | 2023-01-05T07:50:24Z | ---
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
config: plus
split: train
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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7432 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 |
| 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Isaacp/bert-base-uncased-issues-128 | Isaacp | 2023-01-05T23:46:25Z | 94 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-05T22:19:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2456
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0986 | 1.0 | 291 | 1.6929 |
| 1.6401 | 2.0 | 582 | 1.4304 |
| 1.4881 | 3.0 | 873 | 1.3916 |
| 1.4 | 4.0 | 1164 | 1.3796 |
| 1.3416 | 5.0 | 1455 | 1.2012 |
| 1.2807 | 6.0 | 1746 | 1.2733 |
| 1.2396 | 7.0 | 2037 | 1.2646 |
| 1.1993 | 8.0 | 2328 | 1.2098 |
| 1.1661 | 9.0 | 2619 | 1.1862 |
| 1.1406 | 10.0 | 2910 | 1.2223 |
| 1.1294 | 11.0 | 3201 | 1.2056 |
| 1.1042 | 12.0 | 3492 | 1.1655 |
| 1.0827 | 13.0 | 3783 | 1.2525 |
| 1.0738 | 14.0 | 4074 | 1.1685 |
| 1.0626 | 15.0 | 4365 | 1.1182 |
| 1.0629 | 16.0 | 4656 | 1.2456 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
mtlulka/Reinforce-Pixelcopter-PLE-v0_m2 | mtlulka | 2023-01-05T23:42:01Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T23:41:55Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0_m2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.40 +/- 16.64
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
|
muhtasham/small-mlm-glue-qnli | muhtasham | 2023-01-05T23:26:52Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-05T23:08:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-qnli
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. -->
# small-mlm-glue-qnli
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4436
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5207 | 0.4 | 500 | 2.3765 |
| 2.5094 | 0.8 | 1000 | 2.3648 |
| 2.508 | 1.2 | 1500 | 2.4080 |
| 2.4448 | 1.6 | 2000 | 2.4203 |
| 2.4978 | 2.0 | 2500 | 2.4436 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
bao0584/LunarLander-v2 | bao0584 | 2023-01-05T22:57:50Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T22:57:22Z | ---
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: 255.19 +/- 20.14
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
...
```
|
EduardoCGarridoMerchan/ppo-LunarLander-v2 | EduardoCGarridoMerchan | 2023-01-05T22:53:07Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T22:52:41Z | ---
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: 256.15 +/- 20.41
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
...
```
|
JYC333/q-Taxi-v3 | JYC333 | 2023-01-05T22:45:10Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-02T09:50:02Z | ---
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="JYC333/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"])
```
|
muhtasham/small-mlm-glue-mnli | muhtasham | 2023-01-05T22:44:09Z | 108 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-05T22:10:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-mnli
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. -->
# small-mlm-glue-mnli
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8314
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.0194 | 0.4 | 500 | 2.7922 |
| 3.0037 | 0.8 | 1000 | 2.8022 |
| 2.9388 | 1.2 | 1500 | 2.7826 |
| 2.915 | 1.6 | 2000 | 2.7838 |
| 2.8626 | 2.0 | 2500 | 2.7769 |
| 2.7908 | 2.4 | 3000 | 2.7829 |
| 2.789 | 2.8 | 3500 | 2.7933 |
| 2.7784 | 3.2 | 4000 | 2.8314 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
ChouBERT/ChouBERT-2 | ChouBERT | 2023-01-05T22:25:39Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-12-23T22:57:47Z | ---
language:
- fr
pipeline_tag: fill-mask
widget:
- text: "Voila les <mask> de retour. Ça faisait longtemps que j’en avais pas vu sur blé."
example_title: "limace"
- text: "C’est bon le maïs , pour la <mask> . Dans le 64, les larves les plus âgées prennent des force avant de se chrysalider et faire une 2 è génération début août. @Arvalisofficiel @Fragritwittos https://t.co/JLypU2zFFe"
example_title: "Pyrale de maïs"
- text: "<mask> sur céréales à paille : de nombreux retours témoignent de dégâts importants aux quatre coins de l’Hexagone !"
example_title: "JNO"
- text: "Ravageurs sur les maïs, 90% de la parcelle perdue. Impressionnant à voir, difficile à vivre pour l'éleveur <mask> #morbihan https://t.co/DMw3c4EtyQ"
example_title: "choucas"
- text: "Visite des plateformes d’essais dans les #Vosges on observe un flétrissement des feuilles de #maïs et surprise on trouve un <mask>. #lorraine #babycorn"
example_title: "Taupin"
- text: "Lol <mask> ? Toi qui critiquait le programme de classe prépa LoL ! "
example_title: "Taupin - prépa"
--- |
BobMcDear/resnext101_32x16d_wsl | BobMcDear | 2023-01-05T21:59:44Z | 0 | 0 | null | [
"region:us"
] | null | 2023-01-05T19:19:09Z | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
BobMcDear/resnext101_32x4d_ssl | BobMcDear | 2023-01-05T21:59:23Z | 0 | 0 | null | [
"region:us"
] | null | 2023-01-05T19:19:05Z | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
BobMcDear/resnext101_32x8d_ssl | BobMcDear | 2023-01-05T21:58:43Z | 0 | 0 | null | [
"region:us"
] | null | 2023-01-05T19:18:58Z | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
BobMcDear/resnext101_32x16d_ssl | BobMcDear | 2023-01-05T21:58:16Z | 0 | 0 | null | [
"region:us"
] | null | 2023-01-05T19:18:59Z | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
muhtasham/tiny-mlm-glue-wnli | muhtasham | 2023-01-05T21:50:27Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-01-05T21:34:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: tiny-mlm-glue-wnli
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. -->
# tiny-mlm-glue-wnli
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7902
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.5021 | 6.25 | 500 | 2.8676 |
| 2.9602 | 12.5 | 1000 | 2.5328 |
| 2.6906 | 18.75 | 1500 | 2.3640 |
| 2.4167 | 25.0 | 2000 | 2.0918 |
| 2.2699 | 31.25 | 2500 | 2.1182 |
| 2.0933 | 37.5 | 3000 | 1.8802 |
| 1.9399 | 43.75 | 3500 | 1.8979 |
| 1.7961 | 50.0 | 4000 | 1.5276 |
| 1.6738 | 56.25 | 4500 | 1.5327 |
| 1.5784 | 62.5 | 5000 | 1.2767 |
| 1.4405 | 68.75 | 5500 | 1.3593 |
| 1.3428 | 75.0 | 6000 | 0.9772 |
| 1.3257 | 81.25 | 6500 | 1.2789 |
| 1.1988 | 87.5 | 7000 | 0.9494 |
| 1.1275 | 93.75 | 7500 | 0.8278 |
| 1.0854 | 100.0 | 8000 | 0.6301 |
| 1.0275 | 106.25 | 8500 | 0.8044 |
| 0.9714 | 112.5 | 9000 | 0.7544 |
| 0.9019 | 118.75 | 9500 | 0.7902 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
usu96/ddpm-butterflies-128 | usu96 | 2023-01-05T21:47:27Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:sample_data",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-12-31T06:09:26Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: sample_data
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `sample_data` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/usu96/ddpm-butterflies-128/tensorboard?#scalars)
|
mtlulka/Reinforce-Pixelcopter-PLE-v0 | mtlulka | 2023-01-05T20:59:01Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T20:58:54Z | ---
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: 36.40 +/- 32.48
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
|
095ey11/bert-emotion | 095ey11 | 2023-01-05T20:47:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-05T19:42:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: train
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7071669427034283
- name: Recall
type: recall
value: 0.723286061789479
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2053
- Precision: 0.7072
- Recall: 0.7233
- Fscore: 0.7124
## 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: 4
- eval_batch_size: 4
- 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 | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8623 | 1.0 | 815 | 0.7198 | 0.7536 | 0.6312 | 0.6559 |
| 0.5637 | 2.0 | 1630 | 0.8756 | 0.7213 | 0.7166 | 0.7160 |
| 0.2845 | 3.0 | 2445 | 1.2053 | 0.7072 | 0.7233 | 0.7124 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
jerryxyj/ppo-Huggy | jerryxyj | 2023-01-05T20:41:58Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-01-05T20:41:50Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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
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-Huggy
2. Step 1: Write your model_id: jerryxyj/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jinghua2tang/Reinforce-CartPole8 | jinghua2tang | 2023-01-05T20:24:48Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T20:24:39Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
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
|
Eloimoliner/audio-inpainting-diffusion | Eloimoliner | 2023-01-05T20:06:11Z | 0 | 2 | null | [
"license:mit",
"region:us"
] | null | 2023-01-05T14:45:20Z | ---
license: mit
---
Unconditional diffusion models used for audio inpainting.
Models:
filename: maestro_22k_8s-750000.pt
dataset: MAESTRO
fs: 22.05 kHz
segment_length: 8s
filename: musicnet_44k_4s-560000.pt
dataset: MusicNet
fs: 44.1 kHz
segment_length: 4s
|
Poulette/wav2vec2-base-spanish-demo-google-colab2 | Poulette | 2023-01-05T20:01:15Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-01-05T19:49:18Z | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-spanish-demo-google-colab2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-spanish-demo-google-colab2
This model is a fine-tuned version of [facebook/wav2vec2-base-es-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-es-voxpopuli-v2) on the None 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
lss8ak/bert-emotion | lss8ak | 2023-01-05T19:58:46Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-05T19:42:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: train
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7052789678093683
- name: Recall
type: recall
value: 0.7133003963197697
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2194
- Precision: 0.7053
- Recall: 0.7133
- Fscore: 0.7084
## 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: 4
- eval_batch_size: 4
- 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 | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8589 | 1.0 | 815 | 0.7744 | 0.7321 | 0.6122 | 0.6349 |
| 0.5321 | 2.0 | 1630 | 1.0469 | 0.7381 | 0.6703 | 0.6930 |
| 0.2615 | 3.0 | 2445 | 1.2194 | 0.7053 | 0.7133 | 0.7084 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
bvandy/bert-emotion | bvandy | 2023-01-05T19:58:24Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-05T19:40:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: train
args: emotion
metrics:
- name: Precision
type: precision
value: 0.6872092440165337
- name: Recall
type: recall
value: 0.6954893287385614
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2767
- Precision: 0.6872
- Recall: 0.6955
- Fscore: 0.6906
## 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: 4
- eval_batch_size: 4
- 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 | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8803 | 1.0 | 815 | 0.7232 | 0.7435 | 0.6516 | 0.6775 |
| 0.549 | 2.0 | 1630 | 0.9588 | 0.7380 | 0.6640 | 0.6860 |
| 0.2732 | 3.0 | 2445 | 1.2767 | 0.6872 | 0.6955 | 0.6906 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ashutosh1919/Taxi-v3 | ashutosh1919 | 2023-01-05T19:56:14Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T19:56:08Z | ---
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: 7.56 +/- 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="ashutosh1919/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"])
```
|
charlemagne/distilbert-base-uncased-new-cola | charlemagne | 2023-01-05T19:55:12Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-05T17:44:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-new-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-new-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2124
- Accuracy: 0.9496
## 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: 32
- eval_batch_size: 32
- 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 | 164 | 0.4181 | 0.8694 |
| No log | 2.0 | 328 | 0.2656 | 0.9282 |
| No log | 3.0 | 492 | 0.2518 | 0.9366 |
| 0.441 | 4.0 | 656 | 0.2124 | 0.9496 |
| 0.441 | 5.0 | 820 | 0.2177 | 0.9481 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.8.0+cu111
- Datasets 2.1.0
- Tokenizers 0.11.6
|
RazzzHF/creepy-diffusion | RazzzHF | 2023-01-05T19:54:13Z | 0 | 4 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-05T18:47:37Z | ---
license: creativeml-openrail-m
---
This model has a creepy bias producing great horror picture with great fidelity.
You don't need any specific trigger words. Any horror related prompt will result in a strong level of creep.
It's working great in 512x512 and 768x768.
Creepy images examples:
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.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
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.png)
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.png)
.png)
.png) |
rohitp1/timit-distil-kl-alpha-0.25-T-1-take-3 | rohitp1 | 2023-01-05T19:37:03Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-01-05T16:53:06Z | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: timit-distil-kl-alpha-0.25-T-1-take-3
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. -->
# timit-distil-kl-alpha-0.25-T-1-take-3
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 362.6431
- Wer: 0.8022
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 56
- eval_batch_size: 56
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 112
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 836.3694 | 2.43 | 100 | 652.1216 | 1.0487 |
| 438.9098 | 4.87 | 200 | 438.7045 | 0.8833 |
| 320.2348 | 7.31 | 300 | 396.8615 | 0.8490 |
| 267.4869 | 9.75 | 400 | 381.3956 | 0.8325 |
| 243.05 | 12.19 | 500 | 374.6377 | 0.8292 |
| 226.4688 | 14.63 | 600 | 372.4966 | 0.8197 |
| 220.0781 | 17.07 | 700 | 368.0202 | 0.8213 |
| 206.6639 | 19.51 | 800 | 366.3605 | 0.8112 |
| 199.0381 | 21.94 | 900 | 366.9292 | 0.8271 |
| 198.3046 | 24.39 | 1000 | 365.8394 | 0.8088 |
| 188.066 | 26.82 | 1100 | 364.1574 | 0.8057 |
| 188.2653 | 29.27 | 1200 | 364.2211 | 0.8025 |
| 181.248 | 31.7 | 1300 | 363.9985 | 0.8071 |
| 182.5918 | 34.14 | 1400 | 363.5379 | 0.8042 |
| 177.1421 | 36.58 | 1500 | 363.5888 | 0.8032 |
| 179.904 | 39.02 | 1600 | 362.6931 | 0.8038 |
| 174.7976 | 41.46 | 1700 | 362.9397 | 0.8053 |
| 173.5596 | 43.89 | 1800 | 362.9175 | 0.8011 |
| 176.6717 | 46.34 | 1900 | 363.0562 | 0.8013 |
| 173.9602 | 48.77 | 2000 | 362.6431 | 0.8022 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
castorini/wiki-text-8-4-multi-dpr2-passage-encoder | castorini | 2023-01-05T19:28:38Z | 3 | 0 | transformers | [
"transformers",
"jax",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2023-01-04T02:36:58Z | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the passage encoder portion of a 2nd iteration DPR model for the wiki-text-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
castorini/wiki-text-8-4-multi-dpr2-query-encoder | castorini | 2023-01-05T19:28:18Z | 3 | 0 | transformers | [
"transformers",
"jax",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2023-01-04T02:36:49Z | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the query encoder portion of a 2nd iteration DPR model for the wiki-text-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
castorini/wiki-text-6-3-multi-dpr2-query-encoder | castorini | 2023-01-05T19:27:25Z | 3 | 0 | transformers | [
"transformers",
"jax",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2023-01-04T02:36:19Z | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the query encoder portion of a 2nd iteration DPR model for the wiki-text-6-3 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
castorini/wiki-all-8-4-multi-dpr2-passage-encoder | castorini | 2023-01-05T19:26:22Z | 469 | 0 | transformers | [
"transformers",
"jax",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2023-01-04T02:34:47Z | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the passage encoder portion of a 2nd iteration DPR model for the wiki-all-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
castorini/wiki-all-6-3-multi-dpr2-query-encoder | castorini | 2023-01-05T19:25:23Z | 5 | 1 | transformers | [
"transformers",
"jax",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-12-27T01:35:21Z | Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. [Dense Passage Retrieval for Open-Domain Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.550/). _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the [Tevatron](https://github.com/texttron/tevatron) library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the query encoder portion of a 2nd iteration DPR model for the wiki-all-6-3 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets. |
sphchen/EHR_ML_simulation_1 | sphchen | 2023-01-05T19:21:51Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-12-08T13:27:18Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: EHR_ML_simulation_1
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. -->
# EHR_ML_simulation_1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
mwrob/distilbert-base-uncased-sexist | mwrob | 2023-01-05T19:18:43Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-12-19T13:19:14Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-sexist
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-sexist
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.11.0
|
LarryAIDraw/bochitest | LarryAIDraw | 2023-01-05T19:15:10Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-01-05T15:21:13Z | ---
license: creativeml-openrail-m
---
|
violll/unit1 | violll | 2023-01-05T19:12:32Z | 5 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T19:12:08Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.43 +/- 27.36
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
...
```
|
julenalvaro/Perros-VS-gatos-con-vit-base-patch16-224-in21k | julenalvaro | 2023-01-05T19:07:49Z | 32 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-01-04T12:36:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
widget:
- src: >-
https://huggingface.co/julenalvaro/Perros-VS-gatos-con-vit-base-patch16-224-in21k/resolve/main/dog.jpeg
example_title: dog
- src: >-
https://huggingface.co/julenalvaro/Perros-VS-gatos-con-vit-base-patch16-224-in21k/resolve/main/cat.jpeg
example_title: cat
model-index:
- name: vit-base-patch16-224-in21k
results: []
---
# vit-base-patch16-224-in21k
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1026
- Accuracy: 0.982
## Model description
This model is a fine-tuned version of google/vit-base-patch16-224-in21k which discriminates cats from dogs.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.177 | 0.5 | 500 | 0.2100 | 0.9435 |
| 0.1515 | 1.0 | 1000 | 0.0710 | 0.975 |
| 0.0443 | 1.5 | 1500 | 0.2043 | 0.9535 |
| 0.0625 | 2.0 | 2000 | 0.0898 | 0.9745 |
| 0.0181 | 2.5 | 2500 | 0.0961 | 0.9805 |
| 0.0091 | 3.0 | 3000 | 0.1049 | 0.982 |
| 0.0016 | 3.5 | 3500 | 0.1066 | 0.981 |
| 0.0015 | 4.0 | 4000 | 0.1026 | 0.982 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
alexispacek/q-Taxi-v3 | alexispacek | 2023-01-05T18:30:27Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T18:07:48Z | ---
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="alexispacek/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"])
```
|
cleanrl/Defender-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1 | cleanrl | 2023-01-05T18:19:18Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Defender-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T18:19:14Z | ---
tags:
- Defender-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Defender-v5
type: Defender-v5
metrics:
- type: mean_reward
value: 69430.00 +/- 15591.81
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Defender-v5**
This is a trained model of a PPO agent playing Defender-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Defender-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Defender-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/Defender-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Defender-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Defender-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Defender-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
nikcheerla/nooks-amd-detection-v2-full | nikcheerla | 2023-01-05T17:58:27Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-01-05T17:58:19Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6048 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 6048,
"warmup_steps": 605,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Raiden-1001/q-FrozenLake-v1-4x4-noSlippery | Raiden-1001 | 2023-01-05T17:49:43Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T17:49:39Z | ---
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="Raiden-1001/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"])
```
|
Gadersd/q-FrozenLake-v1-4x4-noSlippery | Gadersd | 2023-01-05T17:48:46Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T17:48:42Z | ---
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="Gadersd/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"])
```
|
nikcheerla/nooks-amd-detection-full | nikcheerla | 2023-01-05T17:47:55Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-01-05T07:11:48Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6048 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 6048,
"warmup_steps": 605,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Gemini91/ppo-LunarLander-v2 | Gemini91 | 2023-01-05T17:24:46Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T17:24:19Z | ---
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: 266.54 +/- 23.38
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
...
```
|
Gemini91/ppo-Huggy | Gemini91 | 2023-01-05T17:05:23Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-01-05T17:05:16Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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
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-Huggy
2. Step 1: Write your model_id: Gemini91/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Agog/q-FrozenLake-v1-4x4-noSlippery | Agog | 2023-01-05T16:55:13Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T16:55:09Z | ---
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="Agog/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"])
```
|
Nnarruqt/Reinforce-PixelCpt | Nnarruqt | 2023-01-05T16:46:28Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T16:45:57Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCpt
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 35.50 +/- 27.47
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
|
krecceg/ppo-LunarLander-v2 | krecceg | 2023-01-05T16:27:38Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T16:27:18Z | ---
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: 251.65 +/- 21.55
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
...
```
|
TomLi/distilbert-base-uncased-finetuned-emotion | TomLi | 2023-01-05T16:07:34Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-01-05T13:33:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2168
- Accuracy: 0.925
- F1: 0.9247
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8435 | 1.0 | 250 | 0.3160 | 0.9065 | 0.9045 |
| 0.2457 | 2.0 | 500 | 0.2168 | 0.925 | 0.9247 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
0xid/Reinforce-Pixelcopter-PLE-v0 | 0xid | 2023-01-05T16:04:12Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-05T16:04:02Z | ---
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: 55.60 +/- 41.02
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
|
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