modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
BigSalmon/Lincoln4
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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}
| 11
| 2022-12-20T12:21:25Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 619.50 +/- 144.11
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zbenmo -f logs/
python enjoy.py --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 zbenmo -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 zbenmo
```
## 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', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/MrLincoln
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 7
| null |
---
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: kzipa/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/MrLincoln11
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
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}
| 9
| null |
# -*- coding: utf-8 -*-
"""imageClassifier.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1S-yO7cqOfeKz8Iu1h-DwhTgkkGml0tKb
"""
pip install git+https://github.com/huggingface/transformers.git
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'https://www.livechennai.com/businesslistings/News_photo/dosa11218.jpg'
image = Image.open(requests.get(url, stream=True).raw)
display(image)
feature_extractor = ViTFeatureExtractor.from_pretrained("Amrrs/south-indian-foods")
model = ViTForImageClassification.from_pretrained("Amrrs/south-indian-foods")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
|
BigSalmon/MrLincoln12
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
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"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 9
| 2022-12-20T12:33:51Z
|
---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0,google/fleurs
metrics:
- wer
model-index:
- name: Whisper small Greek Farsipal and El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr
type: mozilla-foundation/common_voice_11_0,google/fleurs
config: el
split: None
metrics:
- name: Wer
type: wer
value: 16.493313521545318
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper small Greek Farsipal and El Greco
This model is a fine-tuned version of [emilios/whisper-sm-farsipal-e5](https://huggingface.co/emilios/whisper-sm-farsipal-e5) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5015
- Wer: 16.4933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0004 | 2.49 | 1000 | 0.4797 | 16.7348 |
| 0.0003 | 4.98 | 2000 | 0.4895 | 16.5397 |
| 0.0002 | 7.46 | 3000 | 0.4963 | 16.5119 |
| 0.0002 | 9.95 | 4000 | 0.5015 | 16.4933 |
| 0.0002 | 12.44 | 5000 | 0.5034 | 16.5676 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln13
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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}
| 9
| null |
---
language:
- zh
license: apache-2.0
tags:
- classification
inference: false
---
# Erlangshen- MacBERT-110M-BinaryClassification-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
1.1亿参数的MacBERT，在大规模二分类数据上预训练
The MacBERT with 110M parameters is pre-trained on large-scale binary classification data.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | MacBERT | 110M | Chinese |
## 模型信息 Model Information
为了提高模型在二分类任务上效果，我们收集了大量开源二分类数据并使用meta- learning方法对其增量预训练。
To improve the model performance on the binary classification task, we collected numerous binary classification datasets for incremental pre-training based on meta-learning methods.
### 下游效果 Performance
在EPRSTMT任务上的效果：
The results on EPRSTMT:
| Model | EPRSTMT |
| --------------------------------------------- | ------ |
| MacBERT | 74.96 |
| **Erlangshen- MacBERT-110M-BinaryClassification-Chinese** | 88.56 |
## 使用 Usage
```python3
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese")
model = AutoModelForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese")
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
BigSalmon/MrLincoln14
|
[] | null |
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}
| 0
| null |
---
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="spadaal/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"])
```
|
BigSalmon/MrLincoln6
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
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},
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},
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},
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}
| 9
| null |
---
language:
- fy
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Frisian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 fy-NL
type: mozilla-foundation/common_voice_11_0
config: fy-NL
split: test
args: fy-NL
metrics:
- name: Wer
type: wer
value: 21.03
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small fy-NL - RuudVelo
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the test set:
- Loss: 0.1443
- Wer: 21.03
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Step | Validation Loss | Wer |
|:-------------:|:-----:|:---------------:|:------:|
| 0.0053 | 1000 | 0.4201 | 21.64 |
| 0.0008 | 2000 | 0.4607 | 21.03 |
| 0.0004 | 3000 | 0.4853 | 21.11 |
| 0.0003 | 4000 | 0.5015 | 21.14 |
| 0.0002 | 5000 | 0.5084 | 21.20 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln8
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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},
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},
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}
| 12
| 2022-12-20T12:51:37Z
|
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### maherkou Dreambooth model trained by cobraxx 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)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
|
BigSalmon/MrLincolnBerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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},
"translation_en_to_de": {
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},
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},
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}
}
}
| 8
| null |
---
language:
- vi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: vi
split: test
args: vi
metrics:
- name: Wer
type: wer
value: 18.30445273086203
---
<!-- 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. -->
# openai/whisper-large-v2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5984
- Wer: 18.3045
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0002 | 24.01 | 1500 | 0.5984 | 18.3045 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BigSalmon/Neo
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 13
| null |
---
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="spadaal/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"])
```
|
BigSalmon/ParaphraseParentheses
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10
| null |
---
title: Whisper Demo
emoji: 🤫
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 3.9.1
app_file: app.py
pinned: false
tags:
- whisper-event
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
BigSalmon/ParaphraseParentheses2.0
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 13
| 2022-12-20T13:11:37Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/0xunihax0r-crypto_penn-cryptogodjohn/1671541946434/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1597407899810242571/JjFDzMSS_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1581791708802682882/YWC1ekmh_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1576478942545993731/GFOPRizx_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Johnny & Unihαx0r & Penndoh</div>
<div style="text-align: center; font-size: 14px;">@0xunihax0r-crypto_penn-cryptogodjohn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Johnny & Unihαx0r & Penndoh.
| Data | Johnny | Unihαx0r | Penndoh |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3238 | 3233 |
| Retweets | 19 | 428 | 494 |
| Short tweets | 550 | 704 | 260 |
| Tweets kept | 2681 | 2106 | 2479 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16qpjkr9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @0xunihax0r-crypto_penn-cryptogodjohn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/203wpi57) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/203wpi57/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/0xunihax0r-crypto_penn-cryptogodjohn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BigSalmon/PhraseBerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 10
| 2022-12-20T13:12:51Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 799.50 +/- 272.11
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga CreativeEvolution -f logs/
python enjoy.py --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 CreativeEvolution -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 CreativeEvolution
```
## 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', 4000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/Points
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 13
| 2022-12-20T13:15:14Z
|
---
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: 760.50 +/- 315.33
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dbaibak -f logs/
python enjoy.py --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 dbaibak -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 dbaibak
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.025),
('frame_stack', 3),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('normalize', False),
('optimize_memory_usage', None),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4)])
```
|
BigSalmon/Points2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12
| 2022-12-20T13:18:03Z
|
---
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="andge/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"])
```
|
BigSalmon/Robertsy
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: sdcid
---
### training params
```json
```
|
BigSalmon/Rowerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="andge/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"])
```
|
BigSalmon/T52
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
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}
| 8
| null |
---
library_name: stable-baselines3
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
metrics:
- type: mean_reward
value: 19.10 +/- 4.83
name: mean_reward
verified: false
---
# **PPO** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **BreakoutNoFrameskip-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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env BreakoutNoFrameskip-v4 -orga Arch4ngel -f logs/
python enjoy.py --algo ppo --env BreakoutNoFrameskip-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 ppo --env BreakoutNoFrameskip-v4 -orga Arch4ngel -f logs/
rl_zoo3 enjoy --algo ppo --env BreakoutNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ -orga Arch4ngel
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 1000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
BigSalmon/T5Salmon
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
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}
}
| 6
| null |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
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-256_new_data
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` 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: 2
- eval_batch_size: 2
- 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/Yagorka/ddpm-butterflies-256_new_data/tensorboard?#scalars)
|
BigSalmon/T5Salmon2
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 13
| null |
---
model-index:
- name: DavidErikMollberg/whisper-medium-is
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: is_is
split: test
metrics:
- type: wer
value: 16.17
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: language-and-voice-lab/samromur_asr
type: language-and-voice-lab/samromur_asr
config: samromur_asr
split: test
metrics:
- type: wer
value: 10.22
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: language-and-voice-lab/althingi_asr
type: language-and-voice-lab/althingi_asr
config: althingi_asr
split: test
metrics:
- type: wer
value: 9.67
name: WER
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination-optional)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation-optional)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
- **Resources for more information:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details>
|
BigTooth/DialoGPT-Megumin
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
}
| 16
| null |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of ccorgi dog in the Acropolis
---
# DreamBooth model for ccorgi trained by lewtun on the lewtun/corgi dataset.
This is a Stable Diffusion model fine-tuned the ccorgi concept taught to Stable Diffusion with DreamBooth.
It can be used by modifying the `instance_prompt`: **a photo of ccorgi dog**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `dog` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('lewtun/ccorgi-dog')
image = pipeline().images[0]
image
```
|
BigTooth/DialoGPT-small-tohru
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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}
| 10
| 2022-12-20T14:02:23Z
|
---
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: 986.00 +/- 315.59
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Kastakin -f logs/
python enjoy.py --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 Kastakin -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 Kastakin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.25),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 3000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BillelBenoudjit/jplu-wikiann
|
[
"fr",
"dataset:wikiann",
"model-index"
] | null |
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}
| 0
| 2022-12-20T14:09:11Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.933
- name: F1
type: f1
value: 0.9334204673837257
---
<!-- 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 the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1850
- Accuracy: 0.933
- F1: 0.9334
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.652 | 1.0 | 500 | 0.2286 | 0.922 | 0.9221 |
| 0.1892 | 2.0 | 1000 | 0.1850 | 0.933 | 0.9334 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Bilz/DialoGPT-small-harrypotter
|
[] | null |
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}
| 0
| 2022-12-20T14:09:53Z
|
---
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="RayKau/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"])
```
|
Bimal/my_bot_model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
| 10
| null |
---
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 40 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": 40,
"warmup_steps": 4,
"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 -->
|
Biniam/en_ti_translate
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
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"architectures": [
"MarianMTModel"
],
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| 14
| 2022-12-20T14:12:32Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.78
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="RayKau/taxi", 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"])
```
|
BinksSachary/DialoGPT-small-shaxx
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
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"model_type": "gpt2",
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}
| 12
| 2022-12-20T14:13:10Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 690.00 +/- 137.15
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Cesar514 -f logs/
python enjoy.py --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 Cesar514 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 Cesar514
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 120000),
('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', 0.00015),
('learning_starts', 100000),
('n_timesteps', 3000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BinksSachary/ShaxxBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 9
| null |
---
language:
- ha
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-V2 Hausa - Drishti Sharma
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ha
split: test
args: ha
metrics:
- name: Wer
type: wer
value: 36.568901303538176
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large-V2 Nepali - Drishti Sharma
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5364
- Wer: 36.5689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.04 | 3.21 | 1000 | 0.5364 | 36.5689 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BinksSachary/ShaxxBot2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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}
| 12
| 2022-12-20T14:18:57Z
|
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
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 `huggan/smithsonian_butterflies_subset` 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/Annuta/ddpm-butterflies-128/tensorboard?#scalars)
|
BitanBiswas/mbert-bengali-ner-finetuned-ner
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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}
| 4
| 2022-12-20T14:19:45Z
|
### Classifier of COVID-19 variant (omicron, delta)
This model takes complete neucleotide sequence as input and return its COVID variant (omicron and delta only)
## Model training
The model was pretrained with RoBERTa config, using all complete necleotide genome of COVID-19 on NCBI. The finetuning step uses ~10000 labeled training data for a binary classification task.\
For more details, please go to this [Powerpoint slides]( https://d24h-my.sharepoint.com/:p:/g/personal/isaacwu_d24h_hk/EYcJNznxuKdEmpfMP_R48yAB0zJa96L0W6RWbS-K6RSmVQ).
## Label representation
- LABEL_0 is omicron
- LABEL_1 is delta
## Example input
Click the links to get a sequence neucleotide, then copy the ATCGs and paste it on the input field
- Delta sequence\
\>LC633760.1 Severe acute respiratory syndrome coronavirus 2 TKYTK1734_2021 genomic RNA, complete genome
[Click here to download](https://d24h-my.sharepoint.com/:t:/g/personal/isaacwu_d24h_hk/EU7_KEKf4VlBg0yf_EmoHY4BLLYM8cS3MkC-4FrNqiKp5w?e=Jmr8CS)
- Omicron sequence\
\>OM210115.1 Severe acute respiratory syndrome coronavirus 2 isolate SARS-CoV-2/human/USA/CO-CDPHE-2102509336/2021, complete genome
[Click here to download](https://d24h-my.sharepoint.com/:t:/g/personal/isaacwu_d24h_hk/ETWJgxp0nJJOlYOiWaiY5yQB2mz_o7nHg0-Vom0ens9P2Q)
|
Blabla/Pipipopo
|
[] | null |
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}
| 0
| 2022-12-20T14:20:11Z
|
---
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: arrandi/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Blackmist786/DialoGPt-small-transformers4
|
[
"pytorch"
] | null |
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| 4
| 2022-12-20T14:21:32Z
|
---
language:
- sk
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Slovak - Robust
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 sk
type: mozilla-foundation/common_voice_11_0
config: sk
split: test
args: sk
metrics:
- type: wer
value: 43.62208472156116
name: Wer
- type: wer
value: 36.11
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Slovak - Robust
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7397
- Wer: 43.6221
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0232 | 14.29 | 1000 | 0.7425 | 51.8801 |
| 0.0083 | 28.57 | 2000 | 0.7698 | 48.4888 |
| 0.0006 | 42.86 | 3000 | 0.7640 | 47.5964 |
| 0.0005 | 57.14 | 4000 | 0.7649 | 44.8953 |
| 0.0002 | 71.43 | 5000 | 0.7440 | 44.3598 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Blazeolmo/Scrabunzi
|
[] | null |
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| 0
| null |
---
language: en
license: cc-by-nc-sa-4.0
pipeline_tag: document-question-answering
tags:
- layoutlm
- document-question-answering
- pdf
- invoices
widget:
- text: "What is the invoice number?"
src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
- text: "What is the purchase amount?"
src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
---
# LayoutLM for Invoices
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of
invoices as well as both [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) for general comprehension.
## Non-consecutive tokens
Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional
classifier head. For example, QA models often encounter this failure mode:
### Before
### After
However this model is able to predict non-consecutive tokens and therefore the address correctly:
## Getting started with the model
The best way to use this model is via [DocQuery](https://github.com/impira/docquery).
## About us
This model was created by the team at [Impira](https://www.impira.com/).
|
Blerrrry/Kkk
|
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}
| 0
| null |
---
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="kzipa/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"])
```
|
BlightZz/MakiseKurisu
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
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"model_type": "gpt2",
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| 14
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
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="kzipa/taxi", 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"])
```
|
Bloodwarrior/Chikfalay
|
[] | null |
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}
}
| 0
| null |
---
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="daripaez/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"])
```
|
BlueGamerBeast/DialoGPT-small-Morgana
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
| 12
| null |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Aman6917/autotrain-data-tscholak_finetune_2
co2_eq_emissions:
emissions: 17.282664720294886
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2548477985
- CO2 Emissions (in grams): 17.2827
## Validation Metrics
- Loss: 0.090
- Rouge1: 96.687
- Rouge2: 93.600
- RougeL: 96.016
- RougeLsum: 96.170
- Gen Len: 32.733
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Aman6917/autotrain-tscholak_finetune_2-2548477985
```
|
BlueGamerBeast/DialoGPT-small-joshua
|
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}
| 0
| null |
---
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: wooihen/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bman/DialoGPT-medium-harrypotter
|
[] | null |
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}
| 0
| null |
---
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: 290.30 +/- 19.57
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
...
```
|
BobBraico/bert-finetuned-ner
|
[] | null |
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}
| 0
| null |
---
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: 287.82 +/- 9.65
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
...
```
|
BogdanKuloren/continual-learning-paper-embeddings-model
|
[
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"MPNetModel"
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}
| 11
| null |
---
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: fuatenginoruc/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BonjinKim/dst_kor_bert
|
[
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
] | null |
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}
| 5
| null |
---
language:
- ar
- multilingual
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- whisper-event
- generated_from_trainer
- Arabic
- multilingual
- STT
datasets:
- mozilla-foundation/common_voice_12_0
metrics:
- wer
model-index:
- name: Kalemat-Tech Arabic Speech Recognition Model (STT)
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_12_0
name: mozilla-foundation/common_voice_12_0
config: ar
split: test
args: ar
metrics:
- type: wer
value: 58.5848
name: wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Kalemat-Tech Arabic Speech Recognition Model (STT) - Mohamed Salama
# نموذج كلماتك للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
# KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on Common_Voice_Arabic_12.0_Augmented.
It achieves the following results on the evaluation set:
- Loss: 0.5362
- Wer: 58.5848
## Example of usage:
```
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small")
```
## Intended uses & limitations
Automatic Speech Recognition
## Training and evaluation data
```
Common_Voice_Arabic_12.0 and I made some augmentations to it as follows:
- 25% of the data TimeMasking
- 25% of the data SpecAugmentation
- 25% of the data WavAugmentation (AddGaussianNoise)
- The final dataset is the original common voice plus the augmented files
```
## Training procedure
### Training hyperparameters
```
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
- mixed_precision_training: Native AMP
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2728 | 1.01 | 1000 | 0.3063 | 60.4733 |
| 0.1442 | 2.01 | 2000 | 0.2878 | 55.6935 |
| 0.0648 | 3.02 | 3000 | 0.3009 | 59.2568 |
| 0.0318 | 4.03 | 4000 | 0.3278 | 59.2993 |
| 0.0148 | 5.04 | 5000 | 0.3539 | 61.0364 |
| 0.0088 | 6.04 | 6000 | 0.3714 | 56.9154 |
| 0.0061 | 7.05 | 7000 | 0.3920 | 57.5515 |
| 0.0041 | 8.06 | 8000 | 0.4149 | 61.6328 |
| 0.0033 | 9.06 | 9000 | 0.4217 | 58.0310 |
| 0.0033 | 10.07 | 10000 | 0.4376 | 59.9594 |
| 0.0021 | 11.08 | 11000 | 0.4485 | 56.7812 |
| 0.0015 | 12.08 | 12000 | 0.4577 | 57.6936 |
| 0.0013 | 13.09 | 13000 | 0.4671 | 60.6606 |
| 0.0011 | 14.1 | 14000 | 0.4686 | 59.8159 |
| 0.0008 | 15.11 | 15000 | 0.4856 | 60.7111 |
| 0.0011 | 16.11 | 16000 | 0.4851 | 59.5198 |
| 0.0005 | 17.12 | 17000 | 0.4936 | 59.2608 |
| 0.0004 | 18.13 | 18000 | 0.4995 | 57.9619 |
| 0.0003 | 19.13 | 19000 | 0.5085 | 58.3630 |
| 0.0002 | 20.14 | 20000 | 0.5155 | 58.0987 |
| 0.0001 | 21.15 | 21000 | 0.5251 | 58.8504 |
| 0.0001 | 22.16 | 22000 | 0.5268 | 58.4228 |
| 0.0001 | 23.16 | 23000 | 0.5317 | 59.0881 |
| 0.0001 | 24.17 | 24000 | 0.5362 | 58.5848 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Boondong/Wandee
|
[] | null |
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| 0
| null |
---
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: nirajandhakal/Huggy-the-dog
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2
|
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: roberta-base-finetuned-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. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5732
- Matthews Correlation: 0.6495
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5211 | 1.0 | 534 | 0.4031 | 0.5599 |
| 0.3739 | 2.0 | 1068 | 0.4688 | 0.5713 |
| 0.0697 | 3.0 | 1602 | 0.4988 | 0.6070 |
| 0.0712 | 4.0 | 2136 | 0.5596 | 0.6221 |
| 0.0955 | 5.0 | 2670 | 0.5732 | 0.6495 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Botjallu/DialoGPT-small-harrypotter
|
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| 0
| null |
---
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: 676.00 +/- 226.40
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lnros -f logs/
python enjoy.py --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 lnros -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 lnros
```
## 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)])
```
|
Botslity/Bot
|
[] | null |
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| 0
| null |
---
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="daripaez/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"])
```
|
BotterHax/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
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"model_type": "gpt2",
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}
| 8
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1016
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Brendan/cse244b-hw2-roberta
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
],
"model_type": "roberta",
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| 28
| null |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of Pseudagrilus beetle underwater
---
# DreamBooth model for Pseudagrilus trained by johnowhitaker on the johnowhitaker/Pseudagrilus dataset.
This is a Stable Diffusion model fine-tuned the Pseudagrilus concept taught to Stable Diffusion with DreamBooth.
It can be used by modifying the `instance_prompt`: **a photo of Pseudagrilus beetle**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `beetle` images for the animal theme. (test run)
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('johnowhitaker/Pseudagrilus-beetle')
image = pipeline().images[0]
image
```
|
BrianTin/MTBERT
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
| 11
| null |
---
language:
- sr
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-V2 Serbian - Drishti Sharma
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 10.764872521246458
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large-V2 Serbian - Drishti Sharma
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2974
- Wer: 10.7649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0235 | 4.78 | 1000 | 0.2974 | 10.7649 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Brinah/1
|
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| 0
| null |
---
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="ggiux/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"])
```
|
BritishLibraryLabs/bl-books-genre
|
[
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
] |
text-classification
|
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"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
| 76
| null |
---
language:
- vi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper large v2 vi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: vi
split: test
args: vi
metrics:
- name: Wer
type: wer
value: 17.076113182715506
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper large v2 vi
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5530
- Wer: 17.0761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 50
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0012 | 21.01 | 150 | 0.5211 | 17.2845 |
| 0.0006 | 42.02 | 300 | 0.5530 | 17.0761 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Broadus20/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 9
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2-test-mlm
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-cased-wikitext2-test-mlm
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8438
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Broadus20/DialoGPT-small-joshua
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
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}
| 12
| null |
---
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: 7.34 +/- 2.90
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="ggiux/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"])
```
|
Brona/model1
|
[] | null |
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}
}
| 0
| null |
---
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 playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kurohige/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Brona/poc_de
|
[] | null |
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}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- wer
model-index:
- name: model_broadclass_onSet0
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_broadclass_onSet0
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9207
- 0 Precision: 1.0
- 0 Recall: 1.0
- 0 F1-score: 1.0
- 0 Support: 31
- 1 Precision: 0.9615
- 1 Recall: 1.0
- 1 F1-score: 0.9804
- 1 Support: 25
- 2 Precision: 1.0
- 2 Recall: 0.9630
- 2 F1-score: 0.9811
- 2 Support: 27
- 3 Precision: 1.0
- 3 Recall: 1.0
- 3 F1-score: 1.0
- 3 Support: 15
- Accuracy: 0.9898
- Macro avg Precision: 0.9904
- Macro avg Recall: 0.9907
- Macro avg F1-score: 0.9904
- Macro avg Support: 98
- Weighted avg Precision: 0.9902
- Weighted avg Recall: 0.9898
- Weighted avg F1-score: 0.9898
- Weighted avg Support: 98
- Wer: 0.9344
- Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:|
| 2.3791 | 4.16 | 100 | 2.2297 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 2.276 | 8.33 | 200 | 2.1645 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.9646 | 12.49 | 300 | 1.9022 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.7089 | 16.65 | 400 | 1.6727 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5546 | 20.82 | 500 | 1.5776 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5671 | 24.98 | 600 | 1.5759 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5548 | 29.16 | 700 | 1.5419 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5148 | 33.33 | 800 | 1.4847 | 0.3263 | 1.0 | 0.4921 | 31 | 0.0 | 0.0 | 0.0 | 25 | 1.0 | 0.1111 | 0.2000 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3469 | 0.3316 | 0.2778 | 0.1730 | 98 | 0.3787 | 0.3469 | 0.2108 | 98 | 0.9837 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 24, 0, 3, 0], [3, 15, 0, 0, 0]] |
| 1.4234 | 37.49 | 900 | 1.4497 | 0.4429 | 1.0 | 0.6139 | 31 | 1.0 | 0.28 | 0.4375 | 25 | 1.0 | 0.5556 | 0.7143 | 27 | 1.0 | 0.4 | 0.5714 | 15 | 0.6020 | 0.8607 | 0.5589 | 0.5843 | 98 | 0.8238 | 0.6020 | 0.5900 | 98 | 0.9975 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 18, 7, 0, 0], [2, 12, 0, 15, 0], [3, 9, 0, 0, 6]] |
| 1.3619 | 41.65 | 1000 | 1.3438 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9796 | 0.9815 | 0.9827 | 0.9816 | 98 | 0.9811 | 0.9796 | 0.9798 | 98 | 0.9832 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] |
| 0.9703 | 45.82 | 1100 | 0.9444 | 1.0 | 1.0 | 1.0 | 31 | 0.9615 | 1.0 | 0.9804 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9898 | 0.9904 | 0.9907 | 0.9904 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9289 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] |
| 0.9299 | 49.98 | 1200 | 0.9207 | 1.0 | 1.0 | 1.0 | 31 | 0.9615 | 1.0 | 0.9804 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9898 | 0.9904 | 0.9907 | 0.9904 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9344 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BrunoNogueira/DialoGPT-kungfupanda
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
}
}
| 10
| null |
---
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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kurohige/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Brykee/BrykeeBot
|
[] | null |
{
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},
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}
| 0
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO with a simple MLP policy from Stable-Baselines3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 278.90 +/- 14.70
name: mean_reward
verified: false
---
# **PPO with a simple MLP policy from Stable-Baselines3** Agent playing **LunarLander-v2**
This is a trained model of a **PPO with a simple MLP policy from Stable-Baselines3** 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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
| 32
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.74
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="Bluelemon883/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"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
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"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
}
| 1,862
| null |
---
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: paicup09/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
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}
| 21
| null |
---
license: apache-2.0
tags:
---
# DQN
DQN model applied to the this discrete environments CartPole-v1
## Model Description
The model was trained from the CleanRl library using the DQN algorithm on CartPole-v1
## Intended Use & Limitation
The model is intended to be used for the following environments CartPole-v1
and understand the implication of Quantization on this type of model from a pretrained state
## Training Procdure
### Training Hyperparameters
The folloing hyperparameters were used during training:
- exp_name: functional_dqn
- seed: 0
- torch_deterministic: True
- cuda: False
- track: True
- wandb_project_name: cleanRL
- wandb_entity: compress_rl
- capture_video: False
- env_id: CartPole-v1
- total_timesteps: 500000
- learning_rate: 0.00025
- buffer_size: 10000
- gamma: 0.99
- target_network_frequency: 500
- batch_size: 128
- start_e: 1
- end_e: 0.05
- exploration_fraction: 0.5
- learning_starts: 10000
- train_frequency: 10
- optimizer: Adan
- max_grad_norm: 0.0
- weight_decay: 0.02
- opt_eps: None
- opt_betas: None
- no_prox: False
- wandb_project: cleanrl
### Framework and version
Pytorch 1.12.1+cu102
gym 0.23.1
Weights and Biases 0.13.3
Hugging Face Hub 0.11.1
Python Version 3.8.16 (default, Dec 7 2022, 01:12:13)
[GCC 7.5.0]
## Citation
```bibtex
```
|
CLTL/icf-levels-fac
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
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}
| 32
| null |
---
language:
- hu
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-V2 Hungarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 hu
type: mozilla-foundation/common_voice_11_0
config: hu
split: test
args: hu
metrics:
- name: Wer
type: wer
value: 17.45330566261488
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large-V2 Hungarian
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2075
- Wer: 17.4533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1751 | 0.67 | 1000 | 0.2075 | 17.4533 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Cameron/BERT-SBIC-offensive
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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}
| 31
| null |
---
license: creativeml-openrail-m
language:
- en
thumbnail: "https://huggingface.co/Norod78/sd15-muppet-blip/resolve/main/sample_images/Collage_HD%202022-12-20%2020_51_11.jpg"
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-diffusers
datasets:
- Norod78/muppet-blip-captions
inference: true
widget:
- text: A painting of Cthulhu as kermit the frog, very detailed, clean, high quality, sharp image, based on H.P Lovecraft stories
- text: An oil painting of kermit the frog muppet in flemish baroque style, very detailed, clean, high quality, sharp image, John Philip
- text: A painting of the cookie monster as a demonic hungry beast, very detailed, clean, high quality, sharp image, based on H.P Lovecraft stories
- text: A digital painting of a drummer muppet, very detailed, clean, high quality, sharp image
---
### SDv1.5 sd15-muppet-blip model trained by Norod78 with [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py)
## For better results, use an explicit name of a muppet such as "Kermit, Cookie monster, etc" or simply use "muppet"
## A few sample pictures generated with this mode (more available [here](https://huggingface.co/Norod78/sd15-muppet-blip/tree/main/sample_images)):
A painting of the cookie monster, very detailed, clean, high quality, sharp image, based on H.P Lovecraft stories
Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged
Steps: 32, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3320437546, Size: 768x640, Model hash: 9b5251e8, Model: sd15-muppet-blip, Batch size: 4, Batch pos: 2
An oil painting of kermit the frog muppet in flemish baroque style, very detailed, clean, high quality, sharp image, John Philip Falter, Very detailed painting
Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged
Steps: 20, Sampler: DPM++ 2S a Karras, CFG scale: 7, Seed: 3762044222, Size: 512x512, Model hash: 9b5251e8, Model: sd15-muppet-blip, Batch size: 4, Batch pos: 3
|
Cameron/BERT-SBIC-targetcategory
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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}
| 30
| null |
---
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: GauthamB/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Cameron/BERT-eec-emotion
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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}
| 36
| null |
# Fine-tuned LongT5 for Conversational QA (ONNX Format)
This model is an ONNX export of [tryolabs/long-t5-tglobal-base-blogpost-cqa](https://huggingface.co/tryolabs/long-t5-tglobal-base-blogpost-cqa), a fine-tuned version of [long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) for the task of Conversational QA. The model was fine-tuned on the [SQuADv2](https://huggingface.co/datasets/squad_v2) and [CoQA](https://huggingface.co/datasets/coqa) datasets and on Tryolabs' own custom dataset, [TryoCoQA](https://github.com/tryolabs/TryoCoQA).
The model was exported using 🤗 Optimum's `exporters` feature, which separates the original model into three componentes: the encoder, the decoder with the Language Modeling head, and the decoder with hidden states as additional inputs. Using 🤗 Optimum and ONNX Runtime, you can combine these components for faster inference.
You can find the details on how we fine-tuned the model and built TryoCoQA on our blog post!
You can also play with the model on the following [space](https://huggingface.co/spaces/tryolabs/blogpost-cqa).
## Results
* Fine-tuning for 3 epochs on SQuADv2 and CoQA combined achieved a 74.29 F1 score on the test set.
* Fine-tuning for 166 epochs on TryoCoQA achieved a 54.77 F1 score on the test set.
|
Cameron/BERT-rtgender-opgender-annotations
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 33
| null |
---
language:
- te
license: apache-2.0
tags:
- whisper-event
metrics:
- wer
model-index:
- name: Whisper Telugu Small - Vasista Sai Lodagala
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: te_in
split: test
metrics:
- type: wer
value: 11.59
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Telugu Small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Telugu data available from multiple publicly available ASR corpuses.
It has been fine-tuned as a part of the Whisper fine-tuning sprint.
**NOTE:** The code used to train this model is available for re-use in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository.
## Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
In order to infer a single audio file using this model, the following code snippet can be used:
```python
>>> import torch
>>> from transformers import pipeline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-telugu-small", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="te", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
For faster inference of whisper models, the [whisper-jax](https://github.com/sanchit-gandhi/whisper-jax) library can be used. Please follow the necessary installation steps as mentioned [here](https://github.com/vasistalodagala/whisper-finetune#faster-evaluation-with-whisper-jax), before using the following code snippet:
```python
>>> import jax.numpy as jnp
>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> transcribe = FlaxWhisperPipline("vasista22/whisper-telugu-small", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="te", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
## Training and evaluation data
Training Data:
- [CSTD IIIT-H ASR Corpus](https://asr.iiit.ac.in/cstd_ulca/)
- [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#telugu-labelled-total-duration-is-102593-hours)
- [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
- [Microsoft Speech Corpus (Indian Languages)](https://msropendata.com/datasets/7230b4b1-912d-400e-be58-f84e0512985e)
- [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)
- Babel ASR Corpus
Evaluation Data:
- [Microsoft Speech Corpus (Indian Languages) Test Set](https://msropendata.com/datasets/7230b4b1-912d-400e-be58-f84e0512985e)
- [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)
- [OpenSLR](https://www.openslr.org/66/)
- Babel Test Set
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.7e-05
- train_batch_size: 48
- eval_batch_size: 32
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15000
- training_steps: 26856 (terminated upon convergence. Initially set to 89520 steps)
- mixed_precision_training: True
## Acknowledgement
This work was done at [Speech Lab, IIT Madras](https://asr.iitm.ac.in/).
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.
|
Camzure/MaamiBot-test
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 9
| null |
---
language:
- te
license: apache-2.0
tags:
- whisper-event
metrics:
- wer
model-index:
- name: Whisper Telugu Medium - Vasista Sai Lodagala
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: te_in
split: test
metrics:
- type: wer
value: 9.47
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Telugu Medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Telugu data available from multiple publicly available ASR corpuses.
It has been fine-tuned as a part of the Whisper fine-tuning sprint.
**NOTE:** The code used to train this model is available for re-use in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository.
## Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
In order to infer a single audio file using this model, the following code snippet can be used:
```python
>>> import torch
>>> from transformers import pipeline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-telugu-medium", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="te", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
For faster inference of whisper models, the [whisper-jax](https://github.com/sanchit-gandhi/whisper-jax) library can be used. Please follow the necessary installation steps as mentioned [here](https://github.com/vasistalodagala/whisper-finetune#faster-evaluation-with-whisper-jax), before using the following code snippet:
```python
>>> import jax.numpy as jnp
>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> transcribe = FlaxWhisperPipline("vasista22/whisper-telugu-medium", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="te", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
## Training and evaluation data
Training Data:
- [CSTD IIIT-H ASR Corpus](https://asr.iiit.ac.in/cstd_ulca/)
- [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#telugu-labelled-total-duration-is-102593-hours)
- [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
- [Microsoft Speech Corpus (Indian Languages)](https://msropendata.com/datasets/7230b4b1-912d-400e-be58-f84e0512985e)
- [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)
- Babel ASR Corpus
Evaluation Data:
- [Microsoft Speech Corpus (Indian Languages) Test Set](https://msropendata.com/datasets/7230b4b1-912d-400e-be58-f84e0512985e)
- [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)
- [OpenSLR](https://www.openslr.org/66/)
- Babel Test Set
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15000
- training_steps: 35808 (terminated upon convergence. Initially set to 89520 steps)
- mixed_precision_training: True
## Acknowledgement
This work was done at [Speech Lab, IIT Madras](https://asr.iitm.ac.in/).
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.
|
Camzure/MaamiBot
|
[] | null |
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| 0
| null |
---
language:
- te
license: apache-2.0
tags:
- whisper-event
metrics:
- wer
model-index:
- name: Whisper Telugu Large-v2 - Vasista Sai Lodagala
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: te_in
split: test
metrics:
- type: wer
value: 9.65
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Telugu Large-v2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Telugu data available from multiple publicly available ASR corpuses.
It has been fine-tuned as a part of the Whisper fine-tuning sprint.
**NOTE:** The code used to train this model is available for re-use in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository.
## Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
In order to infer a single audio file using this model, the following code snippet can be used:
```python
>>> import torch
>>> from transformers import pipeline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-telugu-large-v2", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="te", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
For faster inference of whisper models, the [whisper-jax](https://github.com/sanchit-gandhi/whisper-jax) library can be used. Please follow the necessary installation steps as mentioned [here](https://github.com/vasistalodagala/whisper-finetune#faster-evaluation-with-whisper-jax), before using the following code snippet:
```python
>>> import jax.numpy as jnp
>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> transcribe = FlaxWhisperPipline("vasista22/whisper-telugu-large-v2", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="te", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
## Training and evaluation data
Training Data:
- [CSTD IIIT-H ASR Corpus](https://asr.iiit.ac.in/cstd_ulca/)
- [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#telugu-labelled-total-duration-is-102593-hours)
- [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
- [Microsoft Speech Corpus (Indian Languages)](https://msropendata.com/datasets/7230b4b1-912d-400e-be58-f84e0512985e)
- [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)
- Babel ASR Corpus
Evaluation Data:
- [Microsoft Speech Corpus (Indian Languages) Test Set](https://msropendata.com/datasets/7230b4b1-912d-400e-be58-f84e0512985e)
- [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)
- [OpenSLR](https://www.openslr.org/66/)
- Babel Test Set
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.75e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 22000
- training_steps: 75000
- mixed_precision_training: True
## Acknowledgement
This work was done at [Speech Lab, IIT Madras](https://asr.iitm.ac.in/).
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.
|
Canadiancaleb/DialoGPT-small-walter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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},
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}
| 13
| null |
---
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: 547.50 +/- 197.25
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dzegan -f logs/
python enjoy.py --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 dzegan -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 dzegan
```
## 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)])
```
|
Canadiancaleb/jessebot
|
[] | null |
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}
| 0
| null |
---
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: 792.50 +/- 265.08
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga IngoTB303 -f logs/
python enjoy.py --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 IngoTB303 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 IngoTB303
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 80000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.05),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 9.5e-05),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab
|
[] | null |
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}
}
}
| 0
| null |
---
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: 261.28 +/- 14.14
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
...
```
|
Capreolus/birch-bert-large-car_mb
|
[
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null |
{
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
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},
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},
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}
}
}
| 4
| null |
---
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: 215.54 +/- 33.76
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
...
```
|
Capreolus/birch-bert-large-msmarco_mb
|
[
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null |
{
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
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},
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}
}
}
| 1
| null |
---
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: 524.00 +/- 140.57
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga msgerasyov -f logs/
python enjoy.py --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 msgerasyov -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 msgerasyov
```
## 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', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Captain-1337/CrudeBERT
|
[
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
| 28
| null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **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: kstiers/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CarlosPR/mt5-spanish-memmories-analysis
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 7
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
---
# Unstable Diffusion PhotorealV0.5 converted to Diffusers Format!
You will most likely need to disable the safety checker.
|
Carolhuehuehuehue/Sla
|
[] | null |
{
"architectures": null,
"model_type": null,
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}
| 0
| null |
---
library_name: pytorch
language: en
tags:
- image-to-image
---
# ROME: Realistic one-shot mesh-based head avatars
[Paper](https://arxiv.org/abs/2206.08343) | [Project Page](https://samsunglabs.github.io/rome) | [Github](https://github.com/SamsungLabs/rome)
## Model Description
The ROME models can be used to create a personal avatar from a single image. The resulted meshes can be animated and rendered with photorealistic quality.
To render a ROME avatar with pretrained weights the FLAME Model and DECA weights are required.
FLAME Project: https://flame.is.tue.mpg.de/modellicense.html
## Citations
```
@inproceedings{Khakhulin2022ROME,
author = {Khakhulin, Taras and Sklyarova, Vanessa and Lempitsky, Victor and Zakharov, Egor},
title = {Realistic One-shot Mesh-based Head Avatars},
booktitle = {European Conference of Computer vision (ECCV)},
year = {2022}
}
```
|
dccuchile/albert-base-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 14
| null |
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln93Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln93Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
```
```
ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences
related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions
***
ideas: i know this one guy who retired so young, attesting to how careful they were with money.
related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion
```
```
less specific: actors and musicians should ( support democracy ).
clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ).
***
less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ).
clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ).
```
|
dccuchile/albert-base-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 25
| null |
# 基于trocr(beit+roberta)实现对中文场景文字识别
trocr原地址(https://github.com/microsoft/unilm/tree/master/trocr)
## 实现功能
- [x] 单行/多行文字/横竖排文字识别
- [x] 不规则文字（印章，公式等）
- [ ] 表格识别
- [ ] 模型蒸馏/DML(协作学习)
- [ ] Prompt Learning
## 环境编译
```
docker build --network=host -t trocr-chinese:latest .
docker run --gpus all -it -v /tmp/trocr-chinese:/trocr-chinese trocr-chinese:latest bash
```
1. Set up Python for macOS, see https://developer.apple.com/metal/tensorflow-plugin/
2. Install requirements `python -m pip install -r requirements.txt`
3. Install Pillow `python -m pip install pillow`
4. Upgrade Numpy `python -m pip install numpy --upgrade`
5. Install PyTorch `conda install pytorch torchvision torchaudio -c pytorch`
6. Set envioronmental variable so PyTorch can fallback to CPU: `conda env config vars set PYTORCH_ENABLE_MPS_FALLBACK=1`
7. Reactivate environment: `source ~/miniconda/bin/activate`
8. Generate custom vocab:
```
python gen_vocab.py \
--dataset_path "dataset/*/*.txt" \
--cust_vocab ./cust-data/vocab.txt
```
9. Download pretrained weights from https://pan.baidu.com/s/1rARdfadQlQGKGHa3de82BA, password: 0o65
10. Initialize weights for fine-tuning
Make sure you are using 4.15.0 version of transformers by running `pip install transformers==4.15.0`.
```
python init_custdata_model.py --cust_vocab ./cust-data/vocab.txt --pretrain_model ./weights --cust_data_init_weights_path ./cust-data/weights
```
11. Train
To enable M1 GPU support, install the dev version of transformers by running `pip install git+https://github.com/huggingface/transformers`.
```
pip install git+https://github.com/huggingface/transformers@3be028bc9d4b2cce9539b940f17052f333284684
```
In Dec 21, 2022, the dev version that's working for me is `transformers-4.26.0.dev0`. Later stable releases may have M1 GPU support built-in so you don't need to install the dev version.
If you are running the whole procedure again, remember to reinstall the older transformers version as instructed in step 10. Otherwise, the weights initialized will not be in the correct format and you will see miserable accuracy rate, likely due to breaking changes involving how tokenization is done.
```
python train.py --cust_data_init_weights_path ./cust-data/weights --checkpoint_path ./checkpoint/trocr-custdata --dataset_path "./dataset/*/*.jpg" --per_device_train_batch_size 8
```
## Optimize inference
Install dependencies
```
python -m pip install optimum
conda install onnxruntime -c conda-forge
```
Convert to ONNX
```
python -m transformers.onnx --model=checkpoint/trocr-custdata-8000/last --feature=vision2seq-lm onnx/ --atol 1e-3
```
## 训练
### 初始化模型到自定义训练数据集
#### 字符集准备参考cust-data/vocab.txt
```
vocab.txt
1
2
...
a
b
c
```
```[python]
python gen_vocab.py \
--dataset_path "dataset/cust-data/0/*.txt" \
--cust_vocab ./cust-data/vocab.txt
```
### 初始化自定义数据集模型
#### 下载预训练模型trocr模型权重
链接: https://pan.baidu.com/s/1rARdfadQlQGKGHa3de82BA 密码: 0o65
```
python init_custdata_model.py \
--cust_vocab ./cust-data/vocab.txt \
--pretrain_model ./weights \
--cust_data_init_weights_path ./cust-data/weights
## cust_vocab 词库文件
## pretrain_model 预训练模型权重
## cust_data_init_weights_path 自定义模型初始化模型权重保存位置
```
### 训练模型
#### 数据准备,数据结构如下图所示
```
dataset/cust-data/0/0.jpg
dataset/cust-data/0/0.txt
...
dataset/cust-data/100/10000.jpg
dataset/cust-data/100/10000.txt
```
#### 训练模型
```
python train.py \
--cut_data_init_weights_path ./cust-data/weights \
--checkpoint_path ./checkpoint/trocr-custdata \
--dataset_path "./dataset/cust-data/*/*.jpg" \
--per_device_train_batch_size 8 \
--CUDA_VISIBLE_DEVICES 1
```
#### 评估模型
##### 拷贝checkpoint/trocr-custdata训练完成的pytorch_model.bin 到 ./cust-data/weights 目录下
```[python]
python eval.py \
--dataset_path "./data/cust-data/test/*/*.jpg" \
--cust_data_init_weights_path ./cust-data/weights
```
## 测试模型
```
## 拷贝训练完成的pytorch_model.bin 到 ./cust-data/weights 目录下
index = 2300 ##选择最好的或者最后一个step模型
cp ./checkpoint/trocr-custdata/checkpoint-$index/pytorch_model.bin ./cust-data/weights
python app.py --cust_data_init_weights_path ./cust-data/weights --test_img test/test.jpg
```
## 预训练模型
| 模型 | cer(字符错误率) | acc(文本行) | 下载地址 |训练数据来源 |训练耗时(GPU:3090) |
| ------------- |:-------------:| -----:|-----:|-----:|-----:|
| hand-write(中文手写) |0.011 | 0.940 |[hand-write](https://pan.baidu.com/s/19f7iu9tLHkcT_zpi3UfqLQ) 密码: punl |[数据集地址](https://aistudio.baidu.com/aistudio/datasetdetail/102884/0) |8.5h(10epoch)|
| seal-ocr(印章识别) |0.006 | 0.956 | 整理后开放下载|- |
| im2latex(数学公式识别) |- | - |- |[im2latex](https://zenodo.org/record/56198#.YkniL25Bx_S) ||
| TAL_OCR_TABLE(表格识别) |- | - |- |[TAL_OCR_TABLE](https://ai.100tal.com/dataset) |
| TAL_OCR_MATH(小学低年级算式数据集)|- | - |- | [TAL_OCR_MATH](https://ai.100tal.com/dataset) |
| TAL_OCR_CHN(手写中文数据集)|0.0455 | 0.674（标注质量不太高,例如：test_64/552.jpg 标注值:蝶恋花, 实际值:欧阳修 ） |[TAL_OCR_CHN](https://pan.baidu.com/s/1GbyVpbiKyVN1nJipvH2fpA) 密码: 9kd8 | [TAL_OCR_CHN](https://ai.100tal.com/dataset) |0.6h(20epoch)|
| HME100K(手写公式)|- | - |- | [HME100K](https://ai.100tal.com/dataset) |
备注:后续所有模型会开源在这个目录下链接,可以自由下载. https://pan.baidu.com/s/1uSdWQhJPEy2CYoEULoOhRA 密码: vwi2
### 模型调用
#### 手写识别
```
unzip hand-write.zip
python app.py --cust_data_init_weights_path hand-write --test_img test/hand.png
## output: '醒我的昏迷,偿还我的天真。'
```
## 训练技巧
###### 数据集较少时，可以采用数据增强的方法构造更多的数据，理论上几十万的数据（可不做数据增强，模型预训练已经见到过足够多的数据(票据类、证件类，打印、手写、拍照等场景)），可以收敛到90%以上的准确率（CER<0.05）
###### 训练样本不要自己resize到384x384（后续会优化这个结构，目前预训练是384x384），保留原图即可，模型前处理processor会自动处理
###### 如果要训练识别多行文字，文字行之间可以加一个特殊字符标记，例如："1234\n4567\n89990"
###### fine-tune中英文以外的语言效果可能不太好（足够多的数据及足够steps也能收敛），因为没有在其他语言上预训练
###### 遇到问题先分析一下自己的数据，然后增加一些训练的技巧去优化，不要指望模型解决100%的问题
###### 本项目采用的encoder-decoder结构, 模型还是比较大，如果上生产对硬件开销大，也可以优化encoder（比如cnn结构的mobilenet，resnet）或者decoder（roberta-tiny），然后对其进行蒸馏
###### 如果此项目不能解决您的问题，请选择其他项目，不要因为此项目影响自己的心情！！！
|
dccuchile/albert-base-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"AlbertForTokenClassification"
],
"model_type": "albert",
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},
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},
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},
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}
}
}
| 5
| null |
---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper small Sindhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: sd_in
split: test
metrics:
- name: Wer
type: wer
value: 39.360351975632454
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper small Sindhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs sd_in dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8761
- Wer: 39.3604
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0125 | 30.74 | 400 | 0.7639 | 43.5485 |
| 0.0007 | 61.52 | 800 | 0.8301 | 39.4873 |
| 0.0003 | 92.3 | 1200 | 0.8761 | 39.3604 |
| 0.0002 | 123.07 | 1600 | 0.8949 | 39.3604 |
| 0.0002 | 153.81 | 2000 | 0.9013 | 39.4196 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dccuchile/albert-large-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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}
}
}
| 27
| null |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-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="Musha-the-Yusha/q-FrozenLake-v1-8x8-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"])
```
|
dccuchile/albert-large-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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"max_length": null
},
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},
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},
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}
}
}
| 3
| null |
---
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: 705.50 +/- 211.97
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Rowehn -f logs/
python enjoy.py --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 Rowehn -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 Rowehn
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 80000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.05),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 9e-05),
('learning_starts', 30000),
('n_timesteps', 500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
dccuchile/albert-large-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 29
| null |
---
license: cc-by-nc-4.0
---
This model is a fine-tuned version of RoBERTa-large [1]. It was trained on 1,400 employee reviews to measure corporate culture. More specifically, it predicts which of the four culture dimensions of the Competing Values Framework [2,3] best fits the text at hands, i.e., what is the **dominant culture**.
The model assigns one of four possible labels:
0 (**clan**): Text is best described by a **clan (collaborating) culture**. <br />
1 (**adhocracy**): Text is best described by an **adhocracy (creating) culture**.<br />
2 (**market**): Text is best described by a **market (competing) culture**.<br />
3 (**hierarchy**): Text is best described by a **hierarchy (controlling) culture**.<br />
For details on the model and its performance, see Koch and Pasch (2022). Please cite this article when using the model: <br />
Koch, Sebastian; Pasch, Stefan (2022): CultureBERT: Fine-Tuning Transformer Based Language Models for Corporate Culture. Available online at http://arxiv.org/abs/2212.00509.
Please see the following **tutorial** on how to apply CultureBERT to measure corporate culture in your own text documents: https://github.com/Stefan-Pasch/CultureBERT
Other References:
[1] Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
[2] Cameron, Kim S.; Quinn, Robert E. (2011): Diagnosing and Changing Organizational Culture. Based on the Competing Values Framework. 3rd ed. San Francisco (CA): Jossey-Bass.
[3] Quinn, Robert E.; Rohrbaugh, John (1983): A Spatial Model of Effectiveness Criteria: Towards a Competing Values Approach to Organizational Analysis. In Management Science 29 (3), pp. 363–377. DOI: 10.1287/mnsc.29.3.363.
|
dccuchile/albert-xlarge-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
}
| 91
| null |
---
language: en
thumbnail: http://www.huggingtweets.com/messiiionei/1671569960697/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1414174228069470209/ZPxTsYAJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">#LionelMessi10🇦🇷</div>
<div style="text-align: center; font-size: 14px;">@messiiionei</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from #LionelMessi10🇦🇷.
| Data | #LionelMessi10🇦🇷 |
| --- | --- |
| Tweets downloaded | 373 |
| Retweets | 24 |
| Short tweets | 224 |
| Tweets kept | 125 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/i0091oli/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @messiiionei's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/swu7qn0q) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/swu7qn0q/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/messiiionei')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-1
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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}
| 7
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="rpant/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"])
```
|
Chun/w-zh2en-hsk
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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}
| 3
| null |
---
license: mit
widget:
- text: 오늘 아침 정부는 발표를 통해
- text: |
아 배고프다
datasets:
- heegyu/korean-petitions
- heegyu/namuwiki-extracted
- heegyu/kowikitext
language:
- ko
pipeline_tag: text-generation
---
## 모델 구성
- GPT-J(Flax, Pytorch)
- 24 Layers, 768 hidden dim, 3072 intermediate, 12 heads, 51200 vocab size
- 1024 max_seq_len
- 파라미터 수: 237M
### 성능 벤치마크
<img src="https://github.com/HeegyuKim/language-model/blob/63d8bd7cd39f25e87e0e376cdd18df3f8b460dee/image/benchmark0304.png?raw=true" />
## 학습 환경 및 하이퍼파라미터
- TPU V2-8
- Learning Rate: 3e-4, Batch Size: 512(=64 accum x 8 devices), Scheduler: Linear, WarmUp: 1000 step
- adam_beta1=0.9 adam_beta2=0.98, weight_decay=0.01
- Training Steps: 43247 (3 epoch)
- 학습 토큰 수: 21.11B (43247 * 512 * 1024seq / 1024^3)
- 학습 기간: 2023/1/25 ~ 2023/1/29
## 학습에 사용한 데이터
- AIHub SNS 대화(730MB)
- AIHub 구어체(422MB)
- AIHub 도서(1.6MB)
- AIHub 대규모 웹데이터 기반 한국어 말뭉치(12GB)
- 한국어 위키(867MB)
- 나무위키(6.4GB)
- 국립국어원 메신저 대화(21MB)
- 국립국어원 일상대화 말뭉치(23MB)
- 국립국어원 문어 말뭉치(3.2GB)
- 국립국어원 구어 말뭉치(1.1GB)
- 국립국어원 신문 말뭉치(~2022, 17GB)
- 청와대 국민청원(525MB)
데이터셋 크기는 전처리한 jsonl파일을 기준으로 함.
총 토큰 수는 약 7B임
## 사용 예시
```python
from transformers import pipeline
model_name = "heegyu/kogpt-j-base-24L"
pipe = pipeline('text-generation', model=model_name)
print(pipe("안녕하세요", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128))
print(pipe("오늘 정부 발표에 따르면, ", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128))
print(pipe("싸늘하다. 가슴에 비수가 날아와 꽂힌다. ", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128, min_length=64))
```
결과
```bash
[{'generated_text': '안녕하세요?\n우선은\n네.\n뭐~ 이런 부분들이\n네.\n좀 어~ 영향을 많이 받는 거 같다는 생각이 좀 듭니다.\n네.\n네.\n어~ 우리 윤종천 교수님께서 먼저 정리를 해주셨잖아요.\n네.\n어~ 사실은 인제 아까 우 교수님 말씀하신 것처럼 좀 이런 게 그~\n사실은\n네.\n그~ 한 가지 문제가 더 있는데 그~ 어~ 인제\n음\n이제 아까 이혜정 선생님이 말씀하신 것처럼 이제 어떤 부분에 대한 어~ 그런 저~ 그런 어~ 인제 그~ 이런 부분들이 있는 것 같구요.\n네.\n그래서 어떤 부분에 이제'}]
[{'generated_text': '오늘 정부 발표에 따르면, ㎡당 평균 분양가는 1천215만4천원으로, 1천209만8천원에 해당하는 서울시 강동구 둔촌주공을 재건축하는 둔촌주공 아파트가 재건축 후 최고가를 기록했다.\n동부준공 임대의 경우 분양가가 1천262만5천원이며, 2천만~3천600만원에 달하는 강남구 개포동 개포 주공아파트(옛 개포 주공2단지)의 경우는 1천208만6천원으로 1천208만7천원인 대치동 은마아파트(개포시영아파트)의 절반에도 미치지 못했다.\n반면 개포동 개'}]
[{'generated_text': '싸늘하다. 가슴에 비수가 날아와 꽂힌다. 詩人은 시커멓고 불길하다.\n詩는 꽃 중의 꽃\n詩人은 꽃은 다 시커멓고 불길하다.\n詩人은 시커멀고 불길하다.\n詩無詩無詩無詩無\n詩無詩無詩無詩無\n(고은, 「서정에서 시로」)\n서정에 대한 시비, 시에 대한 시비, 시에 대한 시비가 있는데, 이 두 시비가 시인이 시인지 아닌지 시비인지 아닌지 판가름하는 잣대를 지녔다.\n나는 시인의 삶 속에서 이 시인들의 삶과 시를 바라보며 시인이 무엇을 추구'}]
```
## 주의사항
이 모델의 학습 데이터는 각종 차별/혐오 데이터가 포함됐을 수 있으며, 별도의 제거작업을 진행하지 않았습니다. 따라서 모델이 생성하는 문장에 특정 인물이나 인종, 성별, 장애에 따른 차별/혐오발언을 생성할 수 있습니다.
|
Chungu424/qazwsx
|
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}
| 0
| null |
---
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: 249.16 +/- 27.89
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
...
```
|
Ciruzzo/DialoGPT-medium-harrypotter
|
[] | null |
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}
| 0
| 2022-12-21T01:50:59Z
|
---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper_small_Occitan
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: oc_fr
split: test
metrics:
- name: Wer
type: wer
value: 39.84848484848485
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper_small_Occitan
This model is a fine-tuned version of [bofenghuang/whisper-small-cv11-french](https://huggingface.co/bofenghuang/whisper-small-cv11-french) on the google/fleurs oc_fr dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1744
- Wer: 39.8485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0087 | 30.77 | 400 | 1.1744 | 39.8485 |
| 0.001 | 61.54 | 800 | 1.2807 | 39.8939 |
| 0.0005 | 92.31 | 1200 | 1.3227 | 40.3447 |
| 0.0004 | 123.08 | 1600 | 1.3445 | 40.1098 |
| 0.0003 | 153.85 | 2000 | 1.3524 | 40.0492 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Corvus/DialoGPT-medium-CaptainPrice
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 7
| null |
# Task-aware Retrieval with Instructions
Official repository: [github.com/facebookresearch/tart](https://github.com/facebookresearch/tart)
### Model descriptions
`facebook/tart-full-flan-t5-xl` is a multi-task cross-encoder model trained via instruction-tuning on approximately 40 retrieval tasks, which is initialized with [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl).
TART-full is a 1.5 billion cross-necoder and it can rerank top documents given a query and natural language instruction (e.g., *find a Wikipedia paragraph that answers this question.*).
Experimental results on widely-used [BEIR](https://github.com/beir-cellar/beir), [LOTTE](https://huggingface.co/datasets/colbertv2/lotte), and our new evaluation, [X^2-Retrieval](https://github.com/facebookresearch/tart/cross_task_cross_eval) show that TART-full outperforms previous state-of-the-art methods by levaraging natural language instructions.
More details about modeling and training are in our paper: [Task-aware Retrieval with Instructions](https://arxiv.org/abs/2211.09260).
### Installation
```sh
git clone https://github.com/facebookresearch/tart
pip install -r requirements.txt
cd tart/TART
```
### How to use?
TART-full can be loaded through our customized EncT5 model.
```python
from src.modeling_enc_t5 import EncT5ForSequenceClassification
from src.tokenization_enc_t5 import EncT5Tokenizer
import torch
import torch.nn.functional as F
import numpy as np
# load TART full and tokenizer
model = EncT5ForSequenceClassification.from_pretrained("facebook/tart-full-flan-t5-xl")
tokenizer = EncT5Tokenizer.from_pretrained("facebook/tart-full-flan-t5-xl")
model.eval()
q = "What is the population of Tokyo?"
in_answer = "retrieve a passage that answers this question from Wikipedia"
p_1 = "The population of Japan's capital, Tokyo, dropped by about 48,600 people to just under 14 million at the start of 2022, the first decline since 1996, the metropolitan government reported Monday."
p_2 = "Tokyo, officially the Tokyo Metropolis (東京都, Tōkyō-to), is the capital and largest city of Japan."
# 1. TART-full can identify more relevant paragraph.
features = tokenizer(['{0} [SEP] {1}'.format(in_answer, q), '{0} [SEP] {1}'.format(in_answer, q)], [p_1, p_2], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
scores = model(**features).logits
normalized_scores = [float(score[1]) for score in F.softmax(scores, dim=1)]
print([p_1, p_2][np.argmax(normalized_scores)]) # "The population of Japan's capital, Tokyo, dropped by about 48,600 people to just under 14 million ... "
# 2. TART-full can identify the document that is more relevant AND follows instructions.
in_sim = "You need to find duplicated questions in Wiki forum. Could you find a question that is similar to this question"
q_1 = "How many people live in Tokyo?"
features = tokenizer(['{0} [SEP] {1}'.format(in_sim, q), '{0} [SEP] {1}'.format(in_sim, q)], [p_1, q_1], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
scores = model(**features).logits
normalized_scores = [float(score[1]) for score in F.softmax(scores, dim=1)]
print([p_1, q_1][np.argmax(normalized_scores)]) # "How many people live in Tokyo?"
```
|
D3xter1922/electra-base-discriminator-finetuned-mnli
|
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| 0
| 2022-12-21T08:10:02Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/heyonuoha/1671610411593/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1598231642417537024/hfNTQwNg_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">⚡Favor⚡</div>
<div style="text-align: center; font-size: 14px;">@heyonuoha</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ⚡Favor⚡.
| Data | ⚡Favor⚡ |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 128 |
| Short tweets | 1538 |
| Tweets kept | 1584 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vd8tittu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @heyonuoha's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/383vdqmb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/383vdqmb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/heyonuoha')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DavidSpaceG/MSGIFSR
|
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}
| 0
| null |
---
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: 613.50 +/- 107.96
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Scrwed -f logs/
python enjoy.py --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 Scrwed -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 Scrwed
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.005),
('exploration_fraction', 0.025),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 1e-05),
('learning_starts', 500),
('n_timesteps', 150000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Davlan/bert-base-multilingual-cased-finetuned-amharic
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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}
| 109
| null |
---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper medium Greek El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 el
type: mozilla-foundation/common_voice_11_0
config: el
split: test
args: el
metrics:
- name: Wer
type: wer
value: 10.642644873699851
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper medium Greek El Greco
This model is a fine-tuned version of [emilios/whisper-medium-el](https://huggingface.co/emilios/whisper-medium-el) on the mozilla-foundation/common_voice_11_0 el dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4656
- Wer: 10.6426
## 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: 4e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0066 | 111.11 | 1000 | 0.2357 | 23.0441 |
| 0.0024 | 222.22 | 2000 | 0.2607 | 19.6657 |
| 0.0011 | 333.33 | 3000 | 0.2771 | 20.8748 |
| 0.0043 | 234.82 | 4000 | 0.4536 | 10.7077 |
| 0.0024 | 293.65 | 5000 | 0.4656 | 10.6426 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
| 27
| null |
---
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: 889.50 +/- 425.06
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ziemke -f logs/
python enjoy.py --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 ziemke -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 ziemke
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Davlan/bert-base-multilingual-cased-finetuned-luo
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
| 11
| null |
---
license: apache-2.0
tags:
- vision
- depth-estimation
- generated_from_trainer
model-index:
- name: glpn-nyu-finetuned-diode-221221-110911
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. -->
# glpn-nyu-finetuned-diode-221221-110911
This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4188
- Mae: 0.4087
- Rmse: 0.6260
- Abs Rel: 0.3672
- Log Mae: 0.1626
- Log Rmse: 0.2222
- Delta1: 0.4391
- Delta2: 0.6801
- Delta3: 0.8037
## 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: 24
- eval_batch_size: 48
- seed: 2022
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
| 0.5004 | 1.0 | 72 | 0.4287 | 0.4162 | 0.6314 | 0.3823 | 0.1665 | 0.2260 | 0.4303 | 0.6746 | 0.7946 |
| 0.3996 | 2.0 | 144 | 0.4796 | 0.4471 | 0.6517 | 0.4339 | 0.1868 | 0.2450 | 0.3620 | 0.5981 | 0.7637 |
| 0.4145 | 3.0 | 216 | 0.4401 | 0.4195 | 0.6352 | 0.4073 | 0.1682 | 0.2302 | 0.4202 | 0.6745 | 0.8016 |
| 0.4064 | 4.0 | 288 | 0.4573 | 0.4302 | 0.6366 | 0.4508 | 0.1742 | 0.2363 | 0.3917 | 0.6483 | 0.7958 |
| 0.4247 | 5.0 | 360 | 0.4441 | 0.4204 | 0.6227 | 0.4272 | 0.1693 | 0.2276 | 0.3986 | 0.6512 | 0.8034 |
| 0.4084 | 6.0 | 432 | 0.4326 | 0.4155 | 0.6287 | 0.3913 | 0.1667 | 0.2266 | 0.4183 | 0.6714 | 0.8022 |
| 0.3569 | 7.0 | 504 | 0.4334 | 0.4189 | 0.6355 | 0.3775 | 0.1697 | 0.2285 | 0.4125 | 0.6622 | 0.7967 |
| 0.3239 | 8.0 | 576 | 0.4246 | 0.4116 | 0.6259 | 0.3822 | 0.1640 | 0.2237 | 0.4319 | 0.6736 | 0.8043 |
| 0.32 | 9.0 | 648 | 0.4226 | 0.4171 | 0.6359 | 0.3648 | 0.1670 | 0.2256 | 0.4241 | 0.6677 | 0.7977 |
| 0.3087 | 10.0 | 720 | 0.4188 | 0.4087 | 0.6260 | 0.3672 | 0.1626 | 0.2222 | 0.4391 | 0.6801 | 0.8037 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-swahili
|
[
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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}
| 67
| null |
---
license: openrail
tags:
- image-classification
datasets:
- TUFTS face dataset
# Environmental Impact
- CO2 Emissions (in grams): 1.2933
## Validation Metrics
- Loss: 0.359
- Accuracy: 0.871
- Precision: 0.909
- Recall: 0.909
- AUC: 0.880
- F1: 0.909
## Training Details :
- Pre-trained on [Tuft's Face Dataset](https://github.com/kpvisionlab/Tufts-Face-Database) 10,000 images for 350 epochs using a single NVIDIA A100 GPU
|
Davlan/bert-base-multilingual-cased-finetuned-wolof
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
| 4
| null |
Access to model ThomasGerald/mbart-multi-question-generation is restricted and you are not in the authorized list. Visit https://huggingface.co/ThomasGerald/mbart-multi-question-generation to ask for access.
|
Davlan/bert-base-multilingual-cased-finetuned-yoruba
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
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}
}
| 21
| null |
---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- text-to-image
---
gonn need retrain
|
Davlan/bert-base-multilingual-cased-masakhaner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 88
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1-Basic
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 82.20 +/- 21.97
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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Davlan/bert-base-multilingual-cased-ner-hrl
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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},
"translation_en_to_fr": {
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},
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}
| 269,898
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-squad
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. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Davlan/byt5-base-eng-yor-mt
|
[
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
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"max_length": null,
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"prefix": null
}
}
}
| 11
| null |
---
license: gpl
---
This model is a merged one,which has great improved the ability of futanari photos generation.
It's also has great result generating almost everything.
|
Davlan/m2m100_418M-eng-yor-mt
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9
| null |
---
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: 261.49 +/- 32.45
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
...
```
|
Davlan/m2m100_418M-yor-eng-mt
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 6
| null |
---
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="emmashe15/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"])
```
|
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