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
|
|---|---|---|---|---|---|---|
Davlan/bert-base-multilingual-cased-finetuned-igbo
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 15 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/clinic-kitchen_and_dining-roberta
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/clinic-kitchen_and_dining-roberta")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Davlan/bert-base-multilingual-cased-finetuned-luo
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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fill-mask
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| 11 | null |
---
license: creativeml-openrail-m
language:
- en
tags:
- anime
- art
- stable diffusion
pipeline_tag: text-to-image
library_name: diffusers
---
MeinaMix Objective is to be able to do good art with little prompting.
* For examples and prompts, please checkout: https://civitai.com/models/7240/meinamix
I have a discord server where you can post images that you generated, discuss prompt and/or ask for help.
* https://discord.gg/XC9nGZNDUd
If you like one of my models and want to support their updates
* I've made a ko-fi page; https://ko-fi.com/meina where you can pay me a coffee <3
* And a Patreon page; https://www.patreon.com/MeinaMix where you can support me and get acess to beta of my models!
* You may also try this model using Sinkin.ai: https://sinkin.ai/m/vln8Nwr
* MeinaMix and the other of Meinas will ALWAYS be FREE.
* Recommendations of use:
Enable Quantization in K samplers.
Hires.fix is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes!
Recommended parameters:
* Sampler: Euler a: 40 to 60 steps.
* Sampler: DPM++ SDE Karras: 30 to 60 steps.
* CFG Scale: 7.
* Resolutions: 512x768, 512x1024 for Portrait!
* Resolutions: 768x512, 1024x512, 1536x512 for Landscape!
* Hires.fix: R-ESRGAN 4x+Anime6b, with 10 steps at 0.1 up to 0.3 denoising.
* Clip Skip: 2.
* Negatives: ' (worst quality:2, low quality:2), (zombie, sketch, interlocked fingers, comic), '
|
Davlan/bert-base-multilingual-cased-finetuned-wolof
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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fill-mask
|
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| 4 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/shawarmersa/1675847296427/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/1620720441923878913/0Bn7lo4G_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">شاورمر | Shawarmer</div>
<div style="text-align: center; font-size: 14px;">@shawarmersa</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 شاورمر | Shawarmer.
| Data | شاورمر | Shawarmer |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 8 |
| Short tweets | 543 |
| Tweets kept | 2699 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1dz0zr8g/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 @shawarmersa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hjtpyyda) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hjtpyyda/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/shawarmersa')
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)
|
Davlan/bert-base-multilingual-cased-masakhaner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 88 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- Ailyth/autotrain-data-3lables
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 2.650072914067399
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3341092265
- CO2 Emissions (in grams): 2.6501
## Validation Metrics
- Loss: 0.133
- Accuracy: 0.950
- Macro F1: 0.951
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.951
- Micro Precision: 0.950
- Weighted Precision: 0.950
- Macro Recall: 0.951
- Micro Recall: 0.950
- Weighted Recall: 0.950
|
Davlan/xlm-roberta-base-finetuned-xhosa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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}
| 12 | null |
---
datasets:
- EvaKlimentova/knots_AF
---
# M1 - finetuned ProtBert-BFD
The model is trained on [knots_AF dataset](https://huggingface.co/datasets/EvaKlimentova/knots_AF)
The accuracy on the test set is ~ 0.9848
| M1 ProtBert BFD | Dataset size | Unknotted set size | Accuracy | TPR | TNR |
|:----------------------------:|:------------:|:------------------:|:--------:|:------:|:-------:|
| All | 39412 | 19718 | 0.9848 | 0.9871 | 0.9826 |
| SPOUT | 7371 | 550 | 0.9905 | 0.9963 | 0.9182 |
| TDD | 612 | 24 | 0.9918 | 0.9966 | 0.8750 |
| DUF | 736 | 429 | 0.97905 | 0.9826 | 0.9767 |
| AdoMet synthase | 1794 | 240 | 0.9939 | 0.9968 | 0.9750 |
| Carbonic anhydrase | 1531 | 539 | 0.9556 | 0.9718 | 0.9258 |
| UCH | 477 | 125 | 0.9099 | 0.9631 | 0.7600 |
| ATCase/OTCase | 3799 | 3352 | 0.9992 | 0.9955 | 0.9997 |
| ribosomal-mitochondrial | 147 | 41 | 0.8912 | 0.9906 | 0.63412 |
| membrane | 8309 | 1577 | 0.9791 | 0.9895 | 0.9347 |
| VIT | 14347 | 12639 | 0.9873 | 0.9415 | 0.9935 |
| biosynthesis of lantibiotics | 392 | 286 | 0.9719 | 0.9811 | 0.9685 |
| PGluconate dehydrogenase | 1 | 0 | 1.0 | 1.0 | |
|
Dawn576/Dawn
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_bert-base-uncased
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. -->
# finetuned_bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1947
- Accuracy: 0.6793
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2761 | 1.0 | 102 | 1.3225 | 0.3375 |
| 0.9847 | 2.0 | 204 | 1.0792 | 0.5509 |
| 0.6882 | 3.0 | 306 | 0.9260 | 0.6382 |
| 0.5099 | 4.0 | 408 | 0.9072 | 0.6634 |
| 0.4614 | 5.0 | 510 | 0.9115 | 0.6867 |
| 0.3406 | 6.0 | 612 | 1.0022 | 0.6751 |
| 0.189 | 7.0 | 714 | 1.0881 | 0.6751 |
| 0.2179 | 8.0 | 816 | 1.1520 | 0.6712 |
| 0.2085 | 9.0 | 918 | 1.2567 | 0.6896 |
| 0.1914 | 10.0 | 1020 | 1.2074 | 0.6828 |
| 0.1271 | 11.0 | 1122 | 1.3389 | 0.6887 |
| 0.1236 | 12.0 | 1224 | 1.3539 | 0.6790 |
| 0.0946 | 13.0 | 1326 | 1.4042 | 0.6838 |
| 0.0968 | 14.0 | 1428 | 1.4079 | 0.6877 |
| 0.1095 | 15.0 | 1530 | 1.4884 | 0.6799 |
| 0.1102 | 16.0 | 1632 | 1.5244 | 0.6790 |
| 0.1159 | 17.0 | 1734 | 1.5238 | 0.6799 |
| 0.1448 | 18.0 | 1836 | 1.5568 | 0.6780 |
| 0.1105 | 19.0 | 1938 | 1.5629 | 0.6780 |
| 0.092 | 20.0 | 2040 | 1.5588 | 0.6809 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Dayout/test
|
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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="shashankgarewal/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"])
```
|
Dazai/Ko
|
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| 0 | null |
---
language:
- hi
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- logistics
model-index:
- name: Whisper small 2 - BeaW
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. -->
# Whisper small 2 - BeaW
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chat analysis dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0
|
Dbluciferm3737/Idk
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: vvn0/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeadBeast/emoBERTTamil
|
[
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:tamilmixsentiment",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
| 35 | 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: 466.50 +/- 191.94
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pittawat -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pittawat -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pittawat
```
## 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)])
```
|
Declan/Breitbart_model_v2
|
[
"pytorch",
"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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| 7 | null |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned
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. -->
# videomae-base-finetuned
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5590
- Accuracy: 0.8641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 135
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 0.21 | 28 | 0.6078 | 0.8098 |
| 0.7383 | 1.21 | 56 | 0.6975 | 0.4728 |
| 0.6853 | 2.21 | 84 | 0.6637 | 0.6957 |
| 0.7065 | 3.21 | 112 | 0.5590 | 0.8641 |
| 0.6673 | 4.17 | 135 | 0.5766 | 0.8587 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/Breitbart_model_v7
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 5 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1656
- F1: 0.8589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 |
| 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 |
| 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/CNN_model_v1
|
[
"pytorch",
"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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}
| 7 | 2023-02-08T11:21:17Z |
---
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: kongacute/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/FoxNews_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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": {
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}
| 3 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1135.60 +/- 218.06
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Declan/HuffPost_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
"translation_en_to_fr": {
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},
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}
| 3 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: multiqa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multiqa_model
This model is a fine-tuned version of [nc33/multiqa_model](https://huggingface.co/nc33/multiqa_model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1150
- Precision: 0.0855
- Recall: 0.0485
- F1: 0.0619
- Accuracy: 0.9626
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 327 | 0.1121 | 0.0708 | 0.0280 | 0.0402 | 0.9631 |
| 0.0786 | 2.0 | 654 | 0.1098 | 0.0531 | 0.0254 | 0.0343 | 0.9599 |
| 0.0786 | 3.0 | 981 | 0.1085 | 0.0657 | 0.0243 | 0.0354 | 0.9634 |
| 0.0681 | 4.0 | 1308 | 0.1133 | 0.0765 | 0.0453 | 0.0569 | 0.9618 |
| 0.0641 | 5.0 | 1635 | 0.1150 | 0.0855 | 0.0485 | 0.0619 | 0.9626 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/Reuters_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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,
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},
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan_t5_summarization
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. -->
# flan_t5_summarization
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6162
- Rouge1: 15.9418
- Rouge2: 7.4447
- Rougel: 15.5655
- Rougelsum: 15.5835
- Gen Len: 18.7313
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 272 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7405 | 2.0 | 544 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7405 | 3.0 | 816 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7453 | 4.0 | 1088 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7453 | 5.0 | 1360 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7372 | 6.0 | 1632 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7372 | 7.0 | 1904 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7436 | 8.0 | 2176 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7436 | 9.0 | 2448 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
| 0.7425 | 10.0 | 2720 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/Reuters_model_v8
|
[
"pytorch",
"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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"length_penalty": null,
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}
| 3 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Fine tune the
### concept name: photoreal-v2.5-custom
### Training steps: 1500
### Text encoder steps: 350% of Training steps
Sample pictures of this concept:
|
Declan/WallStreetJournal_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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}
| 3 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: mtlulka/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/WallStreetJournal_model_v6
|
[] | null |
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| 0 | null |
---
language:
- ja
library_name: transformers
tags:
- conversational
- ja
- japanese
- gpt2
- text-generation
- lm
- nlp
---
# Japanese DialoGPT trained with Aozora
**(ja) 青空文庫のセリフで学習した日本語のDialoGPT Smallです**
**(en) Japanese DialoGPT Small trained on Aozora Bunko.**
## [Demo](https://huggingface.co/spaces/akiFQC/Japanese_DialoGPT_small_Aozora)
Demo in this page is not working so well. I recommend you to try it on [Hugging Face Spaces Version](https://huggingface.co/spaces/akiFQC/Japanese_DialoGPT_small_Aozora).
## Reference
- [Aozora-bunko](https://www.aozora.gr.jp/)
- Japanese public domain books.
- I extracted the dialogue part from the books and used it as the training data.
- [japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small)
- Novel Japanese GPT2. I used a small model because of the limitation of GPU memory of my desktop PC(with RTX3060x1) 😢.
- I used this model as a pre-trained model.
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536)
|
Declan/test_model
|
[] | null |
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| 0 | null |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 1.0
- name: NER Recall
type: recall
value: 1.0
- name: NER F Score
type: f_score
value: 1.0
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (1 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `TEMTEM` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 100.00 |
| `ENTS_P` | 100.00 |
| `ENTS_R` | 100.00 |
| `TOK2VEC_LOSS` | 0.00 |
| `NER_LOSS` | 0.00 |
|
Declan/test_push
|
[] | 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="azaazato/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"])
```
|
DeepChem/ChemBERTa-10M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
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"model_type": "roberta",
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| 90 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
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="azaazato/q-Taxi-v3-v1", 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"])
```
|
DeepChem/ChemBERTa-10M-MTR
|
[
"pytorch",
"roberta",
"arxiv:1910.09700",
"transformers"
] | null |
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"RobertaForRegression"
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}
| 708 | 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="azaazato/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"])
```
|
DeepChem/ChemBERTa-5M-MTR
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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"RobertaForRegression"
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| 13 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive_social-roberta-large-v1-2
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive_social-roberta-large-v1-2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
DeepESP/gpt2-spanish
|
[
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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}
| 1,463 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-sigir-support-no-label-20-sigir-tune2nd-LR10-labelled-30
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-large-cased-sigir-support-no-label-20-sigir-tune2nd-LR10-labelled-30
This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-no-label-20](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-20) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3995
## 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-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1303 | 1.0 | 1 | 3.2415 |
| 2.3107 | 2.0 | 2 | 2.1225 |
| 1.2824 | 3.0 | 3 | 2.2623 |
| 1.0548 | 4.0 | 4 | 0.5449 |
| 1.1366 | 5.0 | 5 | 1.1446 |
| 0.5947 | 6.0 | 6 | 0.3811 |
| 0.4889 | 7.0 | 7 | 1.6445 |
| 1.2689 | 8.0 | 8 | 1.7214 |
| 0.8074 | 9.0 | 9 | 2.3152 |
| 0.7084 | 10.0 | 10 | 0.9325 |
| 1.0307 | 11.0 | 11 | 2.4217 |
| 0.7119 | 12.0 | 12 | 2.6455 |
| 1.0052 | 13.0 | 13 | 1.1594 |
| 0.7125 | 14.0 | 14 | 1.2795 |
| 0.4732 | 15.0 | 15 | 0.1245 |
| 0.8829 | 16.0 | 16 | 1.8585 |
| 0.7079 | 17.0 | 17 | 1.6644 |
| 0.6243 | 18.0 | 18 | 1.6117 |
| 1.2438 | 19.0 | 19 | 2.3044 |
| 1.0812 | 20.0 | 20 | 4.5037 |
| 0.7003 | 21.0 | 21 | 1.5862 |
| 0.867 | 22.0 | 22 | 2.1851 |
| 0.9098 | 23.0 | 23 | 1.6055 |
| 0.6214 | 24.0 | 24 | 2.6699 |
| 0.282 | 25.0 | 25 | 1.3515 |
| 0.1888 | 26.0 | 26 | 2.3864 |
| 0.6863 | 27.0 | 27 | 1.2444 |
| 0.8527 | 28.0 | 28 | 1.9603 |
| 0.9416 | 29.0 | 29 | 3.7045 |
| 0.8302 | 30.0 | 30 | 0.9336 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DeepPavlov/marianmt-tatoeba-enru
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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],
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}
| 1 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 29.00 +/- 17.79
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DeepPavlov/rubert-base-cased
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 148,127 | null |
---
license: mit
datasets:
- trivia_qa
language:
- en
pipeline_tag: question-answering
---
This model was finetuned with the base model microsoft/xtremedistil-l6-h256-uncased on the TriviQA dataset for the course NLP4Web at TU Darmstadt 2023.
|
DeepPavlov/xlm-roberta-large-en-ru-mnli
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 227 | null |
---
datasets:
- beans
---
# Custom neural network
This is a custom neural network that will be trained on the [Beans dataset](https://huggingface.co/datasets/beans).
|
DeividasM/wav2vec2-large-xlsr-53-lithuanian
|
[
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
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| 7 | null |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: videomae-base-finetuned
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. -->
# videomae-base-finetuned
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8842
- F1: 0.7147
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 197750
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.0
- Tokenizers 0.13.2
|
DeltaHub/adapter_t5-3b_qnli
|
[
"pytorch",
"transformers"
] | null |
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| 3 | null |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 4.50 +/- 3.93
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p100_pt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p100_pt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p100_pt0.1 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p100_pt0.1 --start-policy-f 100000 --end-policy-f 100000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 100000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p100_pt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 0.1,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 100000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
DemangeJeremy/4-sentiments-with-flaubert
|
[
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
] |
text-classification
|
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| 226 | 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: 885.00 +/- 294.17
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mili7522 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mili7522 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mili7522
```
## 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)])
```
|
Denilson/gbert-base-germaner
|
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| 0 | null |
---
license: openrail
tags:
- generated_from_trainer
- code
- codegen
- assembly
model-index:
- name: santacoder-finetuned-the-stack-cobol
results: []
datasets:
- bigcode/the-stack-dedup
language:
- code
pipeline_tag: text-generation
library_name: transformers
---
<!-- 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. -->
# santacoder-finetuned-the-stack-cobol
This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an The Stack [cobol](https://huggingface.co/datasets/bigcode/the-stack-dedup) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7161
## Model description
The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests).
The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255).
In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations.
## Intended uses & limitations
The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits.
## Training and evaluation data
The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.**
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3911 | 0.1 | 100 | 1.1141 |
| 0.9478 | 0.2 | 200 | 0.9735 |
| 0.784 | 0.3 | 300 | 0.8497 |
| 0.4702 | 0.4 | 400 | 0.7686 |
| 0.6133 | 0.5 | 500 | 0.7375 |
| 0.5396 | 0.6 | 600 | 0.7265 |
| 0.3937 | 0.7 | 700 | 0.6952 |
| 0.5691 | 0.8 | 800 | 0.7059 |
| 0.6366 | 0.9 | 900 | 0.7069 |
| 0.3661 | 1.0 | 1000 | 0.7161 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Denny29/DialoGPT-medium-asunayuuki
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 9 | null |
---
license: cc-by-2.0
---
+ Amemori_Sayo:
+ Amemori_Sayo_NF:
+ additional prompts you may need to get better results:
`black hair`, `sailor dress`, `double braids`, `straight on`
|
Denver/distilbert-base-uncased-finetuned-squad
|
[] | 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="pyflynn/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"])
```
|
DeskDown/MarianMixFT_en-fil
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
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}
| 3 | null |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: -16.40 +/- 1.85
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p500_pt0.1_tt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p500_pt0.1_tt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p500_pt0.1_tt0.1 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p500_pt0.1_tt0.1 --start-policy-f 500000 --end-policy-f 500000 --evaluation-fraction 1.00 --target-tau 0.1 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 500000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p500_pt0.1_tt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 0.1,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 500000,
'target_network_frequency': 1000,
'target_tau': 0.1,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
DeskDown/MarianMixFT_en-hi
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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},
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}
| 3 | null |
---
license: cc-by-nc-4.0
---
MBW for xmdp.WD
xilmo - 1, 0.5, 0.5, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1
dpep - 0, 0.5, 0.5, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0
|
DeskDown/MarianMixFT_en-ms
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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}
| 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: DaniilSirota/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeskDown/MarianMixFT_en-my
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
model-index:
- name: my_awesome_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DeskDown/MarianMixFT_en-th
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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}
| 3 | 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="mshibatatt/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"])
```
|
DeskDown/MarianMix_en-zh-10
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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| 3 | 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="mshibatatt/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"])
```
|
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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"max_length": null
},
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}
}
| 5 | 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="irenekar/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"])
```
|
Devrim/prism-default
|
[
"license:mit"
] | null |
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}
| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxiv3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.40 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="irenekar/taxiv3", 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"])
```
|
DevsIA/Devs_IA
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 78 | 0.7144 |
| No log | 2.0 | 156 | 0.3996 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.9.0
- Datasets 2.5.1
- Tokenizers 0.13.2
|
Dhritam/Zova-bot
|
[] | 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: 679.50 +/- 183.98
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mwissing -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mwissing -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mwissing
```
## 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)])
```
|
DicoTiar/wisdomfiy
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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}
| 3 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
"model_type": "marian",
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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: 422.50 +/- 299.79
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga frangiral
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('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)])
```
|
Digakive/Hsgshs
|
[] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-model-v0
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="pyflynn/taxi-v3-model-v0", 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"])
```
|
Dilmk2/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
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| 13 | null |
---
language:
- zh
pipeline_tag: text2text-generation
tags:
- t5
---
```python
from transformers import T5ForConditionalGeneration
from transformers import T5TokenizerFast as T5Tokenizer
model = "svjack/dialogue-summary-fill-characters"
device = "cpu"
tokenizer = T5Tokenizer.from_pretrained(model)
model = T5ForConditionalGeneration.from_pretrained(model).to(device).eval()
x = '''
根据上下文填补人物映射：
上下文：汤姆：能告诉我如何去机场吗？
杰克：我认为你最好座机场大巴。
汤姆：可是我有些晕车。
杰克：如果你觉得颠簸的话，也可以坐地铁去。
汤姆：地铁需要多长时间？
杰克：1小时左右。
汤姆：周围有什么好的风景吗？
杰克：路过龙井山。
汤姆：那里有哪些特产？
杰克：山中出产龙井茶。
摘要：[对话者0]问去机场的交通工具，[对话者1]推荐地铁，路过龙井山
候选集： 杰克 汤姆
答案：
'''
encode = tokenizer(x, return_tensors='pt').to(device)
answer = model.generate(encode.input_ids,
max_length = 128,
num_beams=2,
top_p = 0.95,
top_k = 50,
repetition_penalty = 2.5,
length_penalty=1.0,
early_stopping=True,
)[0]
decoded = tokenizer.decode(answer, skip_special_tokens=True)
decoded
```
</br>
```json
'[对话者0]<->汤姆 [对话者1]<->杰克'
```
|
DimaOrekhov/transformer-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"EncoderDecoderModel"
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| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-tiny
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. -->
# openai/whisper-tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2034
- Wer: 5.3823
## 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: 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0814 | 10.0 | 500 | 0.1915 | 6.6701 |
| 0.0045 | 20.0 | 1000 | 0.1816 | 5.5088 |
| 0.0016 | 30.01 | 1500 | 0.1924 | 5.5014 |
| 0.0009 | 40.01 | 2000 | 0.1959 | 5.5609 |
| 0.0006 | 51.0 | 2500 | 0.1989 | 5.4195 |
| 0.0005 | 61.0 | 3000 | 0.2014 | 5.4418 |
| 0.0004 | 71.01 | 3500 | 0.2030 | 5.3674 |
| 0.0004 | 81.01 | 4000 | 0.2034 | 5.3823 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Dimedrolza/DialoGPT-small-cyberpunk
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-tiny.en
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. -->
# openai/whisper-tiny.en
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2166
- Wer: 6.5585
## 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: 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1174 | 10.0 | 500 | 0.1975 | 6.4170 |
| 0.0034 | 20.0 | 1000 | 0.1896 | 5.2259 |
| 0.0012 | 30.01 | 1500 | 0.2040 | 6.6478 |
| 0.0007 | 40.01 | 2000 | 0.2080 | 6.6404 |
| 0.0005 | 51.0 | 2500 | 0.2117 | 6.5957 |
| 0.0004 | 61.0 | 3000 | 0.2139 | 6.5510 |
| 0.0003 | 71.01 | 3500 | 0.2162 | 6.5883 |
| 0.0003 | 81.01 | 4000 | 0.2166 | 6.5585 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
DingleyMaillotUrgell/homer-bot
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 12 | 2023-02-08T15:16:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# openai/whisper-base
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1929
- Wer: 4.3549
## 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: 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0326 | 10.0 | 500 | 0.1670 | 5.0398 |
| 0.0019 | 20.0 | 1000 | 0.1728 | 4.5113 |
| 0.0008 | 30.01 | 1500 | 0.1820 | 4.4071 |
| 0.0005 | 40.01 | 2000 | 0.1847 | 4.3773 |
| 0.0004 | 51.0 | 2500 | 0.1886 | 4.3549 |
| 0.0003 | 61.0 | 3000 | 0.1910 | 4.3475 |
| 0.0003 | 71.01 | 3500 | 0.1925 | 4.3549 |
| 0.0002 | 81.01 | 4000 | 0.1929 | 4.3549 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
DivyanshuSheth/T5-Seq2Seq-Final
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-base.en
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. -->
# openai/whisper-base.en
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1913
- Wer: 3.9530
## 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: 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0489 | 10.0 | 500 | 0.1624 | 8.5536 |
| 0.0019 | 20.0 | 1000 | 0.1682 | 4.0051 |
| 0.0007 | 30.01 | 1500 | 0.1782 | 4.1167 |
| 0.0004 | 40.01 | 2000 | 0.1823 | 4.0497 |
| 0.0003 | 51.0 | 2500 | 0.1861 | 3.9827 |
| 0.0002 | 61.0 | 3000 | 0.1888 | 3.9753 |
| 0.0002 | 71.01 | 3500 | 0.1907 | 3.9678 |
| 0.0002 | 81.01 | 4000 | 0.1913 | 3.9530 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Dizoid/Lll
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-small.en
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. -->
# openai/whisper-small.en
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1764
- Wer: 2.9777
## 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: 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0179 | 10.0 | 500 | 0.1422 | 3.4691 |
| 0.0006 | 20.0 | 1000 | 0.1530 | 3.0001 |
| 0.0004 | 30.01 | 1500 | 0.1631 | 3.0150 |
| 0.0002 | 40.01 | 2000 | 0.1672 | 2.9777 |
| 0.0001 | 51.0 | 2500 | 0.1717 | 2.9703 |
| 0.0001 | 61.0 | 3000 | 0.1742 | 2.9926 |
| 0.0001 | 71.01 | 3500 | 0.1759 | 2.9852 |
| 0.0001 | 81.01 | 4000 | 0.1764 | 2.9777 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Dkwkk/Da
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-small
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. -->
# openai/whisper-small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1815
- Wer: 206.4766
## 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: 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0065 | 10.0 | 500 | 0.1476 | 109.2459 |
| 0.0006 | 20.0 | 1000 | 0.1683 | 144.5619 |
| 0.0012 | 30.01 | 1500 | 0.1623 | 205.1738 |
| 0.0002 | 40.01 | 2000 | 0.1710 | 152.7209 |
| 0.0001 | 51.0 | 2500 | 0.1760 | 171.9869 |
| 0.0001 | 61.0 | 3000 | 0.1789 | 193.3447 |
| 0.0001 | 71.01 | 3500 | 0.1808 | 201.9206 |
| 0.0001 | 81.01 | 4000 | 0.1815 | 206.4766 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Dkwkk/W
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_cmu_9h
type: rishabhjain16/infer_cmu_9h
config: en
split: test
metrics:
- type: wer
value: 16.57
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pfs
type: rishabhjain16/infer_pfs
config: en
split: test
metrics:
- type: wer
value: 3.15
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_myst
type: rishabhjain16/infer_myst
config: en
split: test
metrics:
- type: wer
value: 16.18
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/libritts_dev_clean
type: rishabhjain16/libritts_dev_clean
config: en
split: test
metrics:
- type: wer
value: 5.33
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. -->
# openai/whisper-medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1594
- Wer: 21.8343
## 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: 32
- 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0269 | 5.0 | 500 | 0.1069 | 118.0302 |
| 0.0049 | 10.01 | 1000 | 0.1263 | 135.2788 |
| 0.0009 | 15.01 | 1500 | 0.1355 | 94.5731 |
| 0.0001 | 20.01 | 2000 | 0.1413 | 7.5188 |
| 0.0001 | 25.01 | 2500 | 0.1515 | 7.2508 |
| 0.0001 | 30.02 | 3000 | 0.1568 | 24.8493 |
| 0.0 | 35.02 | 3500 | 0.1588 | 22.1470 |
| 0.0 | 40.02 | 4000 | 0.1594 | 21.8343 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Dmitriiserg/Pxd
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-medium.en
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_cmu_9h
type: rishabhjain16/infer_cmu_9h
config: en
split: test
metrics:
- type: wer
value: 15.53
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pfs
type: rishabhjain16/infer_pfs
config: en
split: test
metrics:
- type: wer
value: 3.14
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_myst
type: rishabhjain16/infer_myst
config: en
split: test
metrics:
- type: wer
value: 15.84
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/libritts_dev_clean
type: rishabhjain16/libritts_dev_clean
config: en
split: test
metrics:
- type: wer
value: 5.28
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. -->
# openai/whisper-medium.en
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1748
- Wer: 2.7097
## 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: 32
- 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0329 | 5.0 | 500 | 0.1343 | 4.0125 |
| 0.0013 | 10.01 | 1000 | 0.1531 | 2.8810 |
| 0.0002 | 15.01 | 1500 | 0.1609 | 2.7321 |
| 0.0002 | 20.01 | 2000 | 0.1608 | 2.7544 |
| 0.0001 | 25.01 | 2500 | 0.1688 | 2.7321 |
| 0.0002 | 30.02 | 3000 | 0.1722 | 2.7172 |
| 0.0001 | 35.02 | 3500 | 0.1742 | 2.7172 |
| 0.0001 | 40.02 | 4000 | 0.1748 | 2.7097 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Dmitry12/sber
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-large
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. -->
# openai/whisper-large
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1412
- Wer: 6.7893
## 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: 16
- 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0475 | 2.03 | 500 | 0.1095 | 62.6591 |
| 0.0201 | 5.01 | 1000 | 0.1225 | 16.9285 |
| 0.0044 | 7.03 | 1500 | 0.1312 | 3.6701 |
| 0.0026 | 10.01 | 2000 | 0.1278 | 7.9506 |
| 0.0001 | 12.04 | 2500 | 0.1323 | 17.9186 |
| 0.0001 | 15.02 | 3000 | 0.1386 | 16.3031 |
| 0.0001 | 17.05 | 3500 | 0.1403 | 6.7074 |
| 0.0 | 20.02 | 4000 | 0.1412 | 6.7893 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Doiman/DialoGPT-medium-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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| 13 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_cmu_9h
type: rishabhjain16/infer_cmu_9h
config: en
split: test
metrics:
- type: wer
value: 15.22
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pfs
type: rishabhjain16/infer_pfs
config: en
split: test
metrics:
- type: wer
value: 2.88
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_myst
type: rishabhjain16/infer_myst
config: en
split: test
metrics:
- type: wer
value: 15.79
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/libritts_dev_clean
type: rishabhjain16/libritts_dev_clean
config: en
split: test
metrics:
- type: wer
value: 5.1
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. -->
# 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1534
- Wer: 145.6786
## 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: 16
- 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0799 | 2.03 | 500 | 0.1010 | 28.1322 |
| 0.0239 | 5.01 | 1000 | 0.1388 | 161.0139 |
| 0.0066 | 7.03 | 1500 | 0.1221 | 99.3747 |
| 0.0007 | 10.01 | 2000 | 0.1295 | 250.8822 |
| 0.0007 | 12.04 | 2500 | 0.1423 | 77.2203 |
| 0.0003 | 15.02 | 3000 | 0.1480 | 149.4380 |
| 0.0001 | 17.05 | 3500 | 0.1518 | 141.5842 |
| 0.0001 | 20.02 | 4000 | 0.1534 | 145.6786 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
DongHai/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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| 9 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: NLP4Web_Home_Exercise6_Group13
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. -->
# NLP4Web_Home_Exercise6_Group13
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification
|
[] | 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: 273.60 +/- 12.28
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
...
```
|
Donghyun/L2_BERT
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: speller-t5-909_both_
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. -->
# speller-t5-909_both_
This model is a fine-tuned version of [sberbank-ai/ruT5-large](https://huggingface.co/sberbank-ai/ruT5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0771
- Rouge1: 20.0565
- Rouge2: 7.9096
- Rougel: 20.1271
- Rougelsum: 20.1977
- Gen Len: 41.2712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.1653 | 0.1 | 1500 | 0.1176 | 19.8446 | 7.4011 | 19.8446 | 19.9153 | 41.2712 |
| 0.2083 | 0.2 | 3000 | 0.1023 | 19.7034 | 8.7571 | 19.7034 | 19.774 | 41.1186 |
| 0.1617 | 0.31 | 4500 | 0.0975 | 19.2797 | 7.9096 | 19.2797 | 19.209 | 41.2797 |
| 0.17 | 0.41 | 6000 | 0.0949 | 20.5508 | 8.7571 | 20.5862 | 20.6215 | 41.2712 |
| 0.1416 | 0.51 | 7500 | 0.0871 | 20.0565 | 7.9096 | 20.1271 | 20.1977 | 41.1017 |
| 0.1409 | 0.61 | 9000 | 0.0807 | 20.0565 | 7.9096 | 20.1271 | 20.1977 | 41.1695 |
| 0.1094 | 0.72 | 10500 | 0.0746 | 19.9859 | 7.6271 | 19.9506 | 19.9859 | 41.2627 |
| 0.1256 | 0.82 | 12000 | 0.0754 | 19.9859 | 7.6271 | 19.9506 | 19.9859 | 41.2119 |
| 0.1206 | 0.92 | 13500 | 0.0771 | 20.0565 | 7.9096 | 20.1271 | 20.1977 | 41.2712 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Dongjae/mrc2reader
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"XLMRobertaForQuestionAnswering"
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| 3 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive_social-roberta-large-v1-2-0.13
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive_social-roberta-large-v1-2-0.13")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Waynehillsdev/Wayne_NLP_mT5
|
[
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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},
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},
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}
}
}
| 11 | null |
---
pipeline_tag: zero-shot-classification
license: apache-2.0
language:
- en
tags:
- zero-shot
- text-classification
- science
- mag
widget:
- text: Leo Messi is the best player ever
candidate_labels: politics, science, sports, environment
multi_class: true
---
# SCIroShot
## Overview
<details>
<summary>Click to expand</summary>
- **Model type:** Language Model
- **Architecture:** RoBERTa-large
- **Language:** English
- **License:** Apache 2.0
- **Task:** Zero-Shot Text Classification
- **Data:** Microsoft Academic Graph
- **Additional Resources:**
- [Paper]() <-- WiP (soon to be published in EACL 2023)
- [GitHub](https://github.com/TeMU-BSC/sciroshot)
</details>
## Model description
SCIroShot is an entailment-based Zero-Shot Text Classification model that
has been fine-tuned using a self-made dataset composed of scientific articles
from [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/)
(MAG). The resulting model achieves SOTA
performance in the scientific domain and very competitive results in other areas.
## Intended Usage
This model is intended to be used for zero-shot text classification in English.
## How to use
```python
from transformers import pipeline
zstc = pipeline("zero-shot-classification", model="BSC-LT/sciroshot")
sentence = "Leo Messi is the best player ever."
candidate_labels = ["politics", "science", "sports", "environment"]
template = "This example is {}"
output = zstc(sentence, candidate_labels, hypothesis_template=template, multi_label=False)
print(output)
print(f'Predicted class: {output["labels"][0]}')
```
## Limitations and bias
No measures have been taken to estimate the bias and toxicity embedded in the model.
Even though the fine-tuning data (which is of a scientific nature) may seem harmless, it is important to note that the corpus used to pre-train the vanilla model is very likely to contain a lot of unfiltered content from the internet, as stated in the [RoBERTa-large model card](https://huggingface.co/roberta-large#limitations-and-bias).
## Training
### Training data
Our data builds on top of scientific-domain
annotated data from Microsoft Academic Graph (MAG).
This database consists of a heterogeneous
graph with billions of records from both scientific
publications and patents, in addition to metadata
information such as the authors, institutions, journals,
conferences and their citation relationships.
The documents are organized in a six-level hierarchical
structure of scientific concepts, where the two
top-most levels are manually curated in order to
guarantee a high level of accuracy.
To create the training corpus, a random sample of
scientific articles with a publication year between
2000 and 2021 were retrieved from MAG with their respective
titles and abstracts in English. This results in over 2M documents
with their corresponding Field Of Study, which was obtained from
the 1-level MAG taxonomy (292 possible classes, such as "Computational biology"
or "Transport Engineering").
The fine-tuning dataset was constructed in a weakly supervised
manner by converting text classification data to the entailment format.
Using the relationship between scientific texts
and their matching concepts in the 1-level MAG
taxonomy we are able to generate the premise-
hypothesis pairs corresponding to the entailment
label. Conversely, we generate the pairs for the
neutral label by removing the actual relationship
between the texts and their scientific concepts and
creating a virtual relationship with those to which
they are not matched.
### Training procedure
The newly-created scientific dataset described in the previous section
was used to fine-tune a 355M parameters RoBERTa model on the entailment task.
To do so, the model has to compute the entailment score between every text that
is fed to it and all candidate labels. The final prediction would be the
highest-scoring class in a single-label classification setup, or the N classes
above a certain threshold in a multi-label scenario.
A subset of 52 labels from the training data were kept apart so that they
could be used as a development set of fully-unseen classes.
As a novelty, the validation was not performed on the entailment task (which is used a proxy)
but directly on the target text classification task. This allows us to stop training at the right
time via early stopping, which prevents the model from "overfitting" to the training task. This method
was our way to counteract an effect that was empirically discovered during the experimentation period, where it was observed
that after a certain point the model can start to worsen in the target task (ZSTC) despite still continuing to
improve in the training task (RTE). The simple act of shortening the training time led to a boost in performance.
Read the paper for more details on the methodology and the analysis of RTE/ZSTC correlation.
## Evaluation
### Evaluation data
The model's performance was evaluated on a collection of disciplinary-labeled textual datasets, both from the scientific domain (closer to training data) and the general domain (to assess generalizability).
The following table provides an overview of the number of examples and labels for each dataset:
| Dataset | Labels | Size |
|------------------|--------|--------|
| arXiv | 11 | 3,838 |
| SciDocs-MeSH | 11 | 16,433 |
| SciDocs-MAG | 19 | 17,501 |
| Konstanz | 24 | 10,000 |
| Elsevier | 26 | 14,738 |
| PubMed | 109 | 5,000 |
| Topic Categorization (Yahoo! Answers) | 10 | 60,000 |
| Emotion Detection (UnifyEmotion) | 10 | 15,689 |
| Situation Frame Detection (Situation Typing) | 12 | 3,311 |
Please refer to the paper for further details on each particular dataset.
### Evaluation results
These are the official results reported in the paper:
#### Scientific domain benchmark
| Model | arXiv | SciDocs-MesH | SciDocs-MAG | Konstanz | Elsevier | PubMed |
|-------|-------|--------------|-------------|----------|----------|--------|
| [fb/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) | 33.28 | **66.18**🔥 | 51.77 | 54.62 | 28.41 | **31.59**🔥 |
| SCIroShot | **42.22**🔥 | 59.34 | **69.86**🔥 | **66.07**🔥 | **54.42**🔥 | 27.93 |
#### General domain benchmark
| Model | Topic | Emotion | Situation |
|-------|-------|---------|-----------|
| RTE [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 43.8 | 12.6 | **37.2**🔥 |
| FEVER [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 40.1 | 24.7 | 21.0 |
| MNLI [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 37.9 | 22.3 | 15.4 |
| NSP [(Ma et al., 2021)](https://aclanthology.org/2021.acl-short.99.pdf) | 50.6 | 16.5 | 25.8 |
| NSP-Reverse [(Ma et al., 2021)](https://aclanthology.org/2021.acl-short.99.pdf) | 53.1 | 16.1 | 19.9 |
| SCIroShot | **59.08**🔥 | **24.94**🔥 | 27.42
All the numbers reported above represent **label-wise weighted F1** except for the Topic classification dataset, which is evaluated in terms of **accuracy** following the notation from [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf).
## Additional information
### Authors
- SIRIS Lab, Research Division of SIRIS Academic.
- Language Technologies Unit, Barcelona Supercomputing Center.
### Contact
For further information, send an email to either <[email protected]> or <[email protected]>.
### License
This work is distributed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Funding
This work was partially funded by 2 projects under EU’s H2020 Research and Innovation Programme:
- INODE (grant agreement No 863410).
- IntelComp (grant agreement No 101004870).
### Citation
```bibtex
@inproceedings{pamies2023weakly,
title={A weakly supervised textual entailment approach to zero-shot text classification},
author={P{\`a}mies, Marc and Llop, Joan and Multari, Francesco and Duran-Silva, Nicolau and Parra-Rojas, C{\'e}sar and Gonz{\'a}lez-Agirre, Aitor and Massucci, Francesco Alessandro and Villegas, Marta},
booktitle={Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
pages={286--296},
year={2023}
}
```
### Disclaimer
<details>
<summary>Click to expand</summary>
The model published in this repository is intended for a generalist purpose
and is made available to third parties under a Apache v2.0 License.
Please keep in mind that the model may have bias and/or any other undesirable distortions.
When third parties deploy or provide systems and/or services to other parties using this model
(or a system based on it) or become users of the model itself, they should note that it is under
their responsibility to mitigate the risks arising from its use and, in any event, to comply with
applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owners and creators of the model be liable for any results arising from the use made by third parties.
</details>
|
Doohae/q_encoder
|
[
"pytorch"
] | null |
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| 3 | null |
---
license: mit
datasets:
- gwkim22/spectro_caption_dataset
- Chr0my/Epidemic_music
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- diffusion
- riffusion
- text-to-audio
---
### Introduce
Riffusion with LoRA, fine-tuned with <code>Chr0my/Epidemic_music</code> <br/>
This model was used during Naver Connect BoostCamp AI tech 4th, NLP Track
### Citation
~~~
@article{Forsgren_Martiros_2022,
author = {Forsgren, Seth* and Martiros, Hayk*},
title = {{Riffusion - Stable diffusion for real-time music generation}},
url = {https://riffusion.com/about},
year = {2022}
}
~~~
|
Doquey/DialoGPT-small-Michaelbot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 10 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: sksdog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Openjourney LoRA - by [PromptHero](https://prompthero.com/?utm_source=huggingface&utm_medium=referral)
These are LoRA adaption weights for [Openjourney](https://huggingface.co/prompthero/openjourney) trained by [@JHawkk](https://prompthero.com/JHawkk)
# Openjourney Links
- [Openjourney Dreambooth](https://huggingface.co/prompthero/openjourney)
- [Openjourney Fine tuned model](https://huggingface.co/prompthero/openjourney-v2)
# Want to learn AI art generation?:
- [Crash course in AI art generation](https://prompthero.com/academy/prompt-engineering-course?utm_source=huggingface&utm_medium=referral)
- [Learn to fine-tune Stable Diffusion for photorealism](https://prompthero.com/academy/dreambooth-stable-diffusion-train-fine-tune-course?utm_source=huggingface&utm_medium=referral)
# How to use LoRA's in auto1111:
- Update webui (use git pull like here or redownload it)
- Copy the file to stable-diffusion-webui/models/lora
- Select your LoRA like in this video
- Make sure to change the weight (by default it's :1 which is usually too high)
# Examples:
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 29 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# davanstrien/dataset_mentions
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("davanstrien/dataset_mentions")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
"text-generation": {
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},
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},
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},
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}
}
}
| 29 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: ahmad1289/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-4
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
| 44 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive_transport-roberta-large-v1-2-0.15
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive_transport-roberta-large-v1-2-0.15")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 29 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: conversation-summ
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 51.7796
---
<!-- 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. -->
# conversation-summ
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4048
- Rouge1: 51.7796
- Rouge2: 26.1341
- Rougel: 41.4013
- Rougelsum: 41.4563
- Gen Len: 29.656
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.5781 | 1.0 | 500 | 0.3637 | 50.8871 | 26.6178 | 41.8757 | 41.9291 | 25.16 |
| 0.2183 | 2.0 | 1000 | 0.3586 | 50.7919 | 25.4277 | 40.8428 | 40.8421 | 27.712 |
| 0.1354 | 3.0 | 1500 | 0.4048 | 51.7796 | 26.1341 | 41.4013 | 41.4563 | 29.656 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
| 28 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/lucataco/startuplogos-lora-supersmall
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lucataco/startuplogo-captions dataset. You can find some example images in the following.
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
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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
}
}
}
| 37 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive_calendar-roberta-large-v1-2-0.89
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive_calendar-roberta-large-v1-2-0.89")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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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},
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"early_stopping": null,
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}
}
}
| 27 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive_play-roberta-large-v1-2-0.64
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive_play-roberta-large-v1-2-0.64")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
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}
| 38,156 | 2023-02-08T16:36:19Z |
---
license: apache-2.0
datasets:
- andrea-t94/TwitterSentiment140
language:
- en
metrics:
- perplexity
library_name: transformers
tags:
- distillroberta-base
- twitter
pipeline_tag: fill-mask
---
## Twitter-roBERTa-base fine-tuned using masked language modelling
This is a RoBERTa-base model finetuned (domain adaptation) on ~2M tweets from Jin 2009 (sentiment140).
This is the first step of a two steps approach to finetune for sentiment analysis (ULMFit)
This model is suitable for English.
Main charachetistics:
- pretrained model and tokenizer: distillroberta-base
- no cleaning/processing applied to the data
Reference Paper: [ULMFit](https://arxiv.org/abs/1801.06146).
Reference dataset: [Sentiment140](https://www.kaggle.com/datasets/kazanova/sentiment140?resource=download)
Git Repo: TBD
Labels: 0 -> Negative; 1 -> Positive
|
albert-base-v2
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"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,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 4,785,283 | 2023-02-08T16:39:29Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: ahmad1289/pyramids-RND-1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-large-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 687 | 2023-02-08T16:39:32Z |
---
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="victorivus/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"])
```
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 26,792 | 2023-02-08T16:39:36Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: pabloac31/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"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
}
}
}
| 341 | 2023-02-08T16:42:47Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Learner-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.72
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="victorivus/Q-Learner-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"])
```
|
albert-xlarge-v2
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"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
}
}
}
| 2,973 | 2023-02-08T16:46:52Z |
---
tags:
- spacy
- token-classification
- text-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9970902037
- name: NER Recall
type: recall
value: 0.9970902037
- name: NER F Score
type: f_score
value: 0.9970902037
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9993694107
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9993694107
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9993120844
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9979572295
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9890201085
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 1.0
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat` |
| **Vectors** | 684830 keys, 684830 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (197 labels for 5 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
| **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `POS=ADP`, `Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Quot`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `POS=ADV`, `POS=VERB\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PART`, `POS=PUNCT\|PunctType=Comm`, `POS=PRON`, `POS=SCONJ`, `POS=VERB\|VerbForm=Inf`, `POS=PUNCT\|PunctType=Peri`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `ConjType=Cmp\|POS=CCONJ`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SPACE`, `Definite=Ind\|POS=DET\|PronType=Art`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Degree=Sup\|POS=ADJ`, `POS=VERB\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `NumType=Card\|POS=NUM`, `Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Sup\|POS=ADV`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Quot`, `POS=DET`, `Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PUNCT\|PunctType=Dash`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=AUX\|VerbForm=Ger`, `POS=AUX`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=X`, `POS=ADV\|PronType=Dem`, `POS=PART\|Polarity=Neg`, `Number=Plur\|POS=PRON\|PronType=Dem`, `POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADV`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PUNCT`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbType=Mod`, `POS=DET\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `NumType=Mult\|POS=ADV`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=AUX`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|POS=PRON\|PronType=Art`, `Aspect=Prog\|POS=AUX\|Tense=Pres\|VerbForm=Part`, `POS=X`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `dep`, `det`, `dobj`, `expl`, `mark`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `pcomp`, `pobj`, `poss`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `CARDINAL`, `DATE`, `FAC`, `GPE`, `LANGUAGE`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
| **`textcat`** | `Blaming_Geopolitics`, `Blaming_Government`, `Blaming_Migrants`, `No_Frustration`, `Uses_Infrastructure` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 99.94 |
| `POS_ACC` | 99.94 |
| `MORPH_ACC` | 99.93 |
| `DEP_UAS` | 99.80 |
| `DEP_LAS` | 98.90 |
| `SENTS_P` | 100.00 |
| `SENTS_R` | 100.00 |
| `SENTS_F` | 100.00 |
| `ENTS_F` | 99.71 |
| `ENTS_P` | 99.71 |
| `ENTS_R` | 99.71 |
| `CATS_SCORE` | 99.43 |
| `CATS_MICRO_P` | 99.43 |
| `CATS_MICRO_R` | 99.43 |
| `CATS_MICRO_F` | 99.43 |
| `CATS_MACRO_P` | 99.43 |
| `CATS_MACRO_R` | 99.43 |
| `CATS_MACRO_F` | 99.43 |
| `CATS_MACRO_AUC` | 100.00 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TOK2VEC_LOSS` | 441813.62 |
| `TAGGER_LOSS` | 3246.21 |
| `MORPHOLOGIZER_LOSS` | 3554.80 |
| `PARSER_LOSS` | 333496.66 |
| `NER_LOSS` | 6933.11 |
| `TEXTCAT_LOSS` | 1.50 |
|
albert-xxlarge-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
}
}
| 42,640 | 2023-02-08T16:49:17Z |
---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-sentimientos-pln-uned
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. -->
# clasificador-sentimientos-pln-uned
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3848
- Accuracy: 0.4297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3848 | 0.3806 |
| 1.4224 | 2.0 | 776 | 1.2911 | 0.4090 |
| 1.0722 | 3.0 | 1164 | 1.3848 | 0.4297 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
bert-base-german-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] |
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_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 175,983 | 2023-02-08T16:56:58Z |
---
title: Which Gender
emoji: 🦀
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 3.17.0
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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}
}
| 4,749,504 | 2023-02-08T17:07:03Z |
---
language:
- rw
license: cc-by-4.0
library_name: nemo
datasets:
- mozilla-foundation/common_voice_11_0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- ASR
- Kinyarwanda
- Swahili
- Luganda
- Multilingual
- audio
- CTC
- Conformer
- Transformer
- NeMo
- pytorch
model-index:
- name: stt_rw_sw_lg_conformer_ctc_large
results: []
---
## Model Overview
<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
## NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
```
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("yonas/stt_rw_sw_lg_conformer_ctc_large")
```
### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="yonas/stt_rw_sw_lg_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>
## Training
<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
### Datasets
<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
## Performance
<LIST THE SCORES OF THE MODEL -
OR
USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
## Limitations
<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
Eg:
Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## References
<ADD ANY REFERENCES HERE AS NEEDED>
[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
bert-base-multilingual-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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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}
}
}
| 328,585 | 2023-02-08T17:07:12Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 477.90 +/- 31.31
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bert-large-cased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
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}
}
}
| 8,214 | 2023-02-08T17:07:34Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### zmaroavatar Dreambooth model trained by zmaro with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
bert-large-cased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": null,
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 2,316 | 2023-02-08T17:15:45Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive_datetime-roberta-large-v1-2-0.82
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive_datetime-roberta-large-v1-2-0.82")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
bert-large-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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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},
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"max_length": null,
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}
}
}
| 388,769 | 2023-02-08T17:15:53Z |
---
language:
- rw
license: cc-by-4.0
library_name: nemo
datasets:
- mozilla-foundation/common_voice_11_0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- ASR
- Kinyarwanda
- Swahili
- Luganda
- Multilingual
- audio
- CTC
- Conformer
- Transformer
- NeMo
- pytorch
model-index:
- name: stt_rw_sw_lg_conformer_ctc_large
results: []
---
## Model Overview
<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
## NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
```
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("Izc/stt_rw_sw_lg_conformer_ctc_large")
```
### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="Izc/stt_rw_sw_lg_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>
## Training
<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
### Datasets
<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
## Performance
<LIST THE SCORES OF THE MODEL -
OR
USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
## Limitations
<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
Eg:
Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## References
<ADD ANY REFERENCES HERE AS NEEDED>
[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
bert-large-uncased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"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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}
}
}
| 480,510 | 2023-02-08T17:18:35Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.70 +/- 0.67
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bert-large-uncased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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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,
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}
}
}
| 76,685 | 2023-02-08T17:19:36Z |
---
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: 196.50 +/- 75.40
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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga frangiral
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 50000),
('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', 0.001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
distilbert-base-german-cased
|
[
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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"early_stopping": null,
"max_length": null,
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}
}
}
| 43,667 | 2023-02-08T17:41:35Z |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 10.00 +/- 5.67
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p50_e0.10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p50_e0.10]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p50_e0.10 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.10-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.10-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.10-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p50_e0.10 --start-policy-f 50000 --end-policy-f 1000 --evaluation-fraction 0.10 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 1000,
'env_id': 'Pong-v4',
'evaluation_fraction': 0.1,
'exp_name': 'DQPN_p50_e0.10',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 50000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
xlm-clm-enfr-1024
|
[
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"fr",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"XLMWithLMHeadModel"
],
"model_type": "xlm",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
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},
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},
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},
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}
}
}
| 196 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: sgoodfriend/poca-SoccerTwos-v3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
0x7194633/keyt5-large
|
[
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"ru",
"transformers",
"license:mit",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"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": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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"max_length": null,
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}
}
}
| 166 | 2023-02-08T19:06:28Z |
---
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: 7.32 +/- 2.89
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="augustogeog/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"])
```
|
ATGdev/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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
}
}
}
| 16 | 2023-02-09T00:50:14Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: dfm794/poca-SoccerTwos-2_6_3-l
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AVSilva/bertimbau-large-fine-tuned-md
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 8 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: hectorjelly/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdapterHub/roberta-base-pf-art
|
[
"roberta",
"en",
"dataset:art",
"arxiv:2104.08247",
"adapter-transformers"
] | null |
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| 1 | null |
---
license: creativeml-openrail-m
language:
- en
widget:
- text: 1girl, fate
- text: 1boy, league of
- text: 1girl, genshin
- text: 1boy, national basketball association
- text: 1girl, spy x
- text: 1girl, absurdres
tags:
- stable-diffusion
- anime
- anything-v4
- art
- arxiv:2210.14140
datasets:
- FredZhang7/anime-prompts-180K
---
## Fast Anime PromptGen
This model was trained on a dataset of **80,000** safe anime prompts for 3 epochs. I fetched the prompts from the [Safebooru API endpoint](https://safebooru.donmai.us/posts/random.json), but only accepted unique prompts with **up_score ≥ 8** and without any [blacklisted tags](./blacklist.txt).
I didn't release the V1 model because it often generated gibberish prompts. After trying all means to correct that behavior, I eventually figured that the cause of the gibberish prompts is not from the pipeline params, model structure or training duration, but rather from the random usernames in the training data.
Here's the complete [prompt preprocessing algorithm](./preprocess.py).
## Text-to-image Examples
Prefix *1girl* | [Generated *1girl* prompts](./anime_girl_settings.txt) | Model *Anything V4*
Prefix *1boy* | [Generated *1boy* prompts](./anime_boy_settings.txt) | Model *Anything V4*
## Contrastive Search
```
pip install --upgrade transformers
```
```python
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-anything-promptgen-v2')
prompt = r'1girl, genshin'
# generate text using fine-tuned model
nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
# generate 10 samples using contrastive search
outs = nlp(prompt, max_length=76, num_return_sequences=10, do_sample=True, repetition_penalty=1.2, temperature=0.7, top_k=4, early_stopping=True)
print('\nInput:\n' + 100 * '-')
print('\033[96m' + prompt + '\033[0m')
print('\nOutput:\n' + 100 * '-')
for i in range(len(outs)):
# remove trailing commas and double spaces
outs[i] = str(outs[i]['generated_text']).replace(' ', '').rstrip(',')
print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n')
```
Output Example:
Please see [Fast GPT PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2) for more info on the pipeline parameters.
## Awesome Tips
- If you feel like a generated anime character doesn't show emotions, try emoticons like `;o`, `:o`, `;p`, `:d`, `:p`, and `;d` in the prompt.
I also use `happy smirk`, `happy smile`, `laughing closed eyes`, etc. to make the characters more lively and expressive.
- Adding `absurdres`, instead of `highres` and `masterpiece`, to a prompt can drastically increase the sharpness and resolution of a generated image.
## Danbooru
[Link to the Danbooru version](https://huggingface.co/FredZhang7/danbooru-tag-generator)
|
Ahmad/parsT5-base
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 25 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: denlip82
---
### denlip82 Dreambooth model trained by DL82 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
denlip82 (use that on your prompt)
|
AimB/mT5-en-kr-opus
|
[] | null |
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}
| 0 | 2023-02-09T11:14:40Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: DaniilSirota/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Akash7897/my-newtokenizer
|
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| 0 | null |
---
language: fr
datasets:
- nlpso/m1_fine_tuning_ref_cmbert_iob2
tag: token-classification
widget:
- text: 'Duflot, loueur de carrosses, r. de Paradis-
505
Poissonnière, 22.'
example_title: 'Noisy entry #1'
- text: 'Duſour el Besnard, march, de bois à bruler,
quai de la Tournelle, 17. etr. des Fossés-
SBernard. 11.
Dí'
example_title: 'Noisy entry #2'
- text: 'Dufour (Charles), épicier, r. St-Denis
☞
332'
example_title: 'Ground-truth entry #1'
---
# m1_ind_layers_ref_cmbert_iob2_level_1
## Introduction
This model is a model that was fine-tuned from [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** on a nested NER Paris trade directories dataset.
## Dataset
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## Experiment parameter
* Pretrained-model : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Dataset : ground-truth
* Tagging format : IOB2
* Recognised entities : level 1
## Load model from the Hugging Face
**Warning 1 ** : this model only recognises level-1 entities of dataset. It has to be used with [m1_ind_layers_ref_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_2) to recognise nested entities level-2.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m1_ind_layers_ref_cmbert_iob2_level_1")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m1_ind_layers_ref_cmbert_iob2_level_1")
|
Akash7897/test-clm
|
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| 0 | null |
---
tags:
- Pong-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v4
type: Pong-v4
metrics:
- type: mean_reward
value: 12.00 +/- 6.86
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p100_e0.10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p100_e0.10]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p100_e0.10 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_e0.10-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_e0.10-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_e0.10-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p100_e0.10 --start-policy-f 100000 --end-policy-f 1000 --evaluation-fraction 0.10 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 1000,
'env_id': 'Pong-v4',
'evaluation_fraction': 0.1,
'exp_name': 'DQPN_p100_e0.10',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 100000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
Akbarariza/Anjar
|
[] | null |
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| 0 | 2023-02-09T12:01:02Z |
---
language: fr
datasets:
- nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2
tag: token-classification
widget:
- text: 'Duflot, loueur de carrosses, r. de Paradis-
505
Poissonnière, 22.'
example_title: 'Noisy entry #1'
- text: 'Duſour el Besnard, march, de bois à bruler,
quai de la Tournelle, 17. etr. des Fossés-
SBernard. 11.
Dí'
example_title: 'Noisy entry #2'
- text: 'Dufour (Charles), épicier, r. St-Denis
☞
332'
example_title: 'Ground-truth entry #1'
---
# m1_ind_layers_ref_ptrn_cmbert_iob2_level_2
## Introduction
This model is a model that was fine-tuned from [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** on a nested NER Paris trade directories dataset.
## Dataset
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## Experiment parameter
* Pretrained-model : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Dataset : ground-truth
* Tagging format : IOB2
* Recognised entities : level 2
## Load model from the Hugging Face
**Warning 1 ** : this model only recognises level-2 entities of dataset. It has to be used with [m1_ind_layers_ref_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1) to recognise nested entities level-1.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2")
|
Akira-Yana/distilbert-base-uncased-finetuned-cola
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
model-index:
- name: distillbert-base-uncased-finetuned-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distillbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Aleksandar/electra-srb-oscar
|
[
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
fill-mask
|
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"ElectraForMaskedLM"
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| 6 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### skirt_made_out_of_flowers Dreambooth model trained by Erpix3lt with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
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