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
|
|---|---|---|---|---|---|---|
dccuchile/albert-xlarge-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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"AlbertForSequenceClassification"
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}
| 26
| 2023-03-28T15:51:16Z
|
# `cardiffnlp/xlm-v-base-tweet-sentiment-fr`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (french).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(french).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 69.31 | 69.31 | 69.31 | 68.84 | 69.31 | 69.87 | 69.31 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr/raw/main/eval.json).
|
dccuchile/albert-xlarge-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"AlbertForTokenClassification"
],
"model_type": "albert",
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}
| 5
| 2023-03-28T15:54:31Z
|
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: Isaacgv/PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/albert-xlarge-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
}
| 24
| 2023-03-28T15:55:39Z
|
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### yananet Dreambooth model trained by grisha2000 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:
|
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 7
| 2023-03-28T15:56:07Z
|
---
datasets:
- VSK1996/NER_Drugs
metrics:
- f1
- recall
- precision
library_name: flair
---
|
dccuchile/albert-xxlarge-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 26
| 2023-03-28T15:59:12Z
|
---
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: -4.53 +/- 1.41
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
...
```
|
dccuchile/albert-xxlarge-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 3
| 2023-03-28T16:03:03Z
|
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"num_beams": null,
"prefix": null
},
"text-generation": {
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 7
| 2023-03-28T16:03:14Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-qlearning
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="kvothe/taxi-v3-qlearning", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
dccuchile/albert-tiny-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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"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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},
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}
}
}
| 393
| 2023-03-28T16:07:17Z
|
---
license: unknown
---
<font size="64">Done :3</font><br>
Azur Lane Akashi LoRA<br>
<br>
I didn't put a link to go from 🤗 to there!<br>
|
dccuchile/albert-xxlarge-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 42
| 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: 1656.07 +/- 59.58
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
...
```
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx
|
[
"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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},
"translation_en_to_fr": {
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}
| 25
| null |
---
license: mit
language: en
---
=======
### ------ Update September 2018 ------
It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such that we can make improvements and design better models in the future.
You can help us achieve this by answering this [4-question Google Form](https://docs.google.com/forms/d/e/1FAIpQLSe1h4NSQD30YM8dsbJQEnki-02_9KVQD34qgP9to0bwAHBvBA/viewform "DeepMoji Google Form"). Thanks for your support!
# 😇 TorchMoji
> **Read our blog post about the implementation process [here](https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983).**
TorchMoji is a [pyTorch](http://pytorch.org/) implementation of the [DeepMoji](https://github.com/bfelbo/DeepMoji) model developped by Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan and Sune Lehmann.
This model trained on 1.2 billion tweets with emojis to understand how language is used to express emotions. Through transfer learning the model can obtain state-of-the-art performance on many emotion-related text modeling tasks.
Try the online demo of DeepMoji [http://deepmoji.mit.edu](http://deepmoji.mit.edu/)! See the [paper](https://arxiv.org/abs/1708.00524), [blog post](https://medium.com/@bjarkefelbo/what-can-we-learn-from-emojis-6beb165a5ea0) or [FAQ](https://www.media.mit.edu/projects/deepmoji/overview/) for more details.
## Overview
* [torchmoji/](torchmoji) contains all the underlying code needed to convert a dataset to the vocabulary and use the model.
* [examples/](examples) contains short code snippets showing how to convert a dataset to the vocabulary, load up the model and run it on that dataset.
* [scripts/](scripts) contains code for processing and analysing datasets to reproduce results in the paper.
* [model/](model) contains the pretrained model and vocabulary.
* [data/](data) contains raw and processed datasets that we include in this repository for testing.
* [tests/](tests) contains unit tests for the codebase.
To start out with, have a look inside the [examples/](examples) directory. See [score_texts_emojis.py](examples/score_texts_emojis.py) for how to use DeepMoji to extract emoji predictions, [encode_texts.py](examples/encode_texts.py) for how to convert text into 2304-dimensional emotional feature vectors or [finetune_youtube_last.py](examples/finetune_youtube_last.py) for how to use the model for transfer learning on a new dataset.
Please consider citing the [paper](https://arxiv.org/abs/1708.00524) of DeepMoji if you use the model or code (see below for citation).
## Installation
We assume that you're using [Python 2.7-3.5](https://www.python.org/downloads/) with [pip](https://pip.pypa.io/en/stable/installing/) installed.
First you need to install [pyTorch (version 0.2+)](http://pytorch.org/), currently by:
```bash
conda install pytorch -c pytorch
```
At the present stage the model can't make efficient use of CUDA. See details in the [Hugging Face blog post](https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983).
When pyTorch is installed, run the following in the root directory to install the remaining dependencies:
```bash
pip install -e .
```
This will install the following dependencies:
* [scikit-learn](https://github.com/scikit-learn/scikit-learn)
* [text-unidecode](https://github.com/kmike/text-unidecode)
* [emoji](https://github.com/carpedm20/emoji)
Then, run the download script to downloads the pretrained torchMoji weights (~85MB) from [here](https://www.dropbox.com/s/q8lax9ary32c7t9/pytorch_model.bin?dl=0) and put them in the model/ directory:
```bash
python scripts/download_weights.py
```
## Testing
To run the tests, install [nose](http://nose.readthedocs.io/en/latest/). After installing, navigate to the [tests/](tests) directory and run:
```bash
cd tests
nosetests -v
```
By default, this will also run finetuning tests. These tests train the model for one epoch and then check the resulting accuracy, which may take several minutes to finish. If you'd prefer to exclude those, run the following instead:
```bash
cd tests
nosetests -v -a '!slow'
```
## Disclaimer
This code has been tested to work with Python 2.7 and 3.5 on Ubuntu 16.04 and macOS Sierra machines. It has not been optimized for efficiency, but should be fast enough for most purposes. We do not give any guarantees that there are no bugs - use the code on your own responsibility!
## Contributions
We welcome pull requests if you feel like something could be improved. You can also greatly help us by telling us how you felt when writing your most recent tweets. Just click [here](http://deepmoji.mit.edu/contribute/) to contribute.
## License
This code and the pretrained model is licensed under the MIT license.
## Benchmark datasets
The benchmark datasets are uploaded to this repository for convenience purposes only. They were not released by us and we do not claim any rights on them. Use the datasets at your responsibility and make sure you fulfill the licenses that they were released with. If you use any of the benchmark datasets please consider citing the original authors.
## Citation
```
@inproceedings{felbo2017,
title={Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm},
author={Felbo, Bjarke and Mislove, Alan and S{\o}gaard, Anders and Rahwan, Iyad and Lehmann, Sune},
booktitle={Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2017}
}
```
>>>>>>> tm/master
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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| 1
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_bert_dreadit
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. -->
# fine_tuned_bert_dreadit
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6081
- Accuracy: 0.7528
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0037 | 1.0 | 178 | 1.8515 | 0.7163 |
| 0.0017 | 2.0 | 356 | 1.7404 | 0.7163 |
| 0.001 | 3.0 | 534 | 1.2895 | 0.7921 |
| 0.0012 | 4.0 | 712 | 1.3320 | 0.7669 |
| 0.0005 | 5.0 | 890 | 1.3646 | 0.7949 |
| 0.0002 | 6.0 | 1068 | 1.5997 | 0.7809 |
| 0.0001 | 7.0 | 1246 | 1.5772 | 0.7753 |
| 0.0003 | 8.0 | 1424 | 1.7599 | 0.7556 |
| 0.0001 | 9.0 | 1602 | 1.7494 | 0.7640 |
| 0.0001 | 10.0 | 1780 | 1.9942 | 0.7556 |
| 0.0001 | 11.0 | 1958 | 1.9370 | 0.75 |
| 0.0 | 12.0 | 2136 | 1.9671 | 0.7781 |
| 0.0001 | 13.0 | 2314 | 2.1223 | 0.7640 |
| 0.0 | 14.0 | 2492 | 2.1653 | 0.7472 |
| 0.0001 | 15.0 | 2670 | 1.9924 | 0.75 |
| 0.0 | 16.0 | 2848 | 2.1778 | 0.7528 |
| 0.0 | 17.0 | 3026 | 2.3010 | 0.7612 |
| 0.0 | 18.0 | 3204 | 2.2210 | 0.7669 |
| 0.0 | 19.0 | 3382 | 2.3333 | 0.7556 |
| 0.0 | 20.0 | 3560 | 1.8684 | 0.7697 |
| 0.0976 | 21.0 | 3738 | 1.9417 | 0.7584 |
| 0.0 | 22.0 | 3916 | 2.1385 | 0.7472 |
| 0.0 | 23.0 | 4094 | 1.9774 | 0.7669 |
| 0.0 | 24.0 | 4272 | 2.0778 | 0.75 |
| 0.0001 | 25.0 | 4450 | 2.4343 | 0.7331 |
| 0.0 | 26.0 | 4628 | 2.1331 | 0.7528 |
| 0.0 | 27.0 | 4806 | 2.2511 | 0.7640 |
| 0.0 | 28.0 | 4984 | 2.2422 | 0.7584 |
| 0.0 | 29.0 | 5162 | 2.1228 | 0.7669 |
| 0.0006 | 30.0 | 5340 | 2.0973 | 0.7725 |
| 0.0 | 31.0 | 5518 | 1.9392 | 0.7809 |
| 0.0 | 32.0 | 5696 | 2.2996 | 0.7107 |
| 0.4186 | 33.0 | 5874 | 2.2191 | 0.7584 |
| 0.0 | 34.0 | 6052 | 2.2233 | 0.75 |
| 0.0 | 35.0 | 6230 | 2.2263 | 0.7584 |
| 0.0 | 36.0 | 6408 | 2.2205 | 0.7584 |
| 0.0 | 37.0 | 6586 | 2.4488 | 0.7444 |
| 0.0 | 38.0 | 6764 | 2.5616 | 0.7360 |
| 0.0 | 39.0 | 6942 | 2.5941 | 0.7416 |
| 0.0 | 40.0 | 7120 | 2.5129 | 0.7528 |
| 0.0 | 41.0 | 7298 | 2.4978 | 0.7360 |
| 0.0 | 42.0 | 7476 | 2.3089 | 0.7528 |
| 0.0 | 43.0 | 7654 | 2.5056 | 0.7472 |
| 0.0 | 44.0 | 7832 | 2.5786 | 0.7416 |
| 0.0 | 45.0 | 8010 | 2.2956 | 0.7640 |
| 0.0 | 46.0 | 8188 | 2.5265 | 0.7472 |
| 0.0 | 47.0 | 8366 | 2.4396 | 0.7584 |
| 0.0 | 48.0 | 8544 | 2.5547 | 0.7472 |
| 0.0 | 49.0 | 8722 | 2.5556 | 0.7528 |
| 0.0 | 50.0 | 8900 | 2.5732 | 0.7528 |
| 0.0 | 51.0 | 9078 | 2.5062 | 0.7556 |
| 0.0 | 52.0 | 9256 | 2.5504 | 0.7528 |
| 0.0 | 53.0 | 9434 | 2.5602 | 0.7528 |
| 0.0 | 54.0 | 9612 | 2.5627 | 0.7472 |
| 0.0 | 55.0 | 9790 | 2.6575 | 0.75 |
| 0.0 | 56.0 | 9968 | 2.6239 | 0.7528 |
| 0.0 | 57.0 | 10146 | 2.4757 | 0.7697 |
| 0.0 | 58.0 | 10324 | 2.4862 | 0.7612 |
| 0.0 | 59.0 | 10502 | 3.2968 | 0.6938 |
| 0.0 | 60.0 | 10680 | 2.5265 | 0.7472 |
| 0.0 | 61.0 | 10858 | 2.1426 | 0.7978 |
| 0.0 | 62.0 | 11036 | 2.4674 | 0.7640 |
| 0.0 | 63.0 | 11214 | 2.3496 | 0.7640 |
| 0.0 | 64.0 | 11392 | 2.4010 | 0.7556 |
| 0.0 | 65.0 | 11570 | 2.4081 | 0.7725 |
| 0.0 | 66.0 | 11748 | 2.4022 | 0.7753 |
| 0.0 | 67.0 | 11926 | 2.2982 | 0.7753 |
| 0.0 | 68.0 | 12104 | 2.4628 | 0.7612 |
| 0.0 | 69.0 | 12282 | 2.5764 | 0.7640 |
| 0.0 | 70.0 | 12460 | 2.4056 | 0.7781 |
| 0.0 | 71.0 | 12638 | 2.3265 | 0.7865 |
| 0.0 | 72.0 | 12816 | 2.5182 | 0.7640 |
| 0.0 | 73.0 | 12994 | 2.3872 | 0.7556 |
| 0.0 | 74.0 | 13172 | 2.7281 | 0.7388 |
| 0.0 | 75.0 | 13350 | 2.4907 | 0.7612 |
| 0.0 | 76.0 | 13528 | 2.5323 | 0.7584 |
| 0.0 | 77.0 | 13706 | 2.2055 | 0.7837 |
| 0.0 | 78.0 | 13884 | 2.2227 | 0.7865 |
| 0.0 | 79.0 | 14062 | 2.2794 | 0.7753 |
| 0.0 | 80.0 | 14240 | 2.2886 | 0.7753 |
| 0.0 | 81.0 | 14418 | 2.8320 | 0.7444 |
| 0.0 | 82.0 | 14596 | 2.8252 | 0.7472 |
| 0.0 | 83.0 | 14774 | 2.2986 | 0.7837 |
| 0.0 | 84.0 | 14952 | 2.7879 | 0.7416 |
| 0.0 | 85.0 | 15130 | 2.7926 | 0.7416 |
| 0.0 | 86.0 | 15308 | 2.7656 | 0.7472 |
| 0.0 | 87.0 | 15486 | 2.7336 | 0.7444 |
| 0.0 | 88.0 | 15664 | 2.7320 | 0.7444 |
| 0.0 | 89.0 | 15842 | 2.7402 | 0.7444 |
| 0.0 | 90.0 | 16020 | 2.7415 | 0.7444 |
| 0.0 | 91.0 | 16198 | 2.7406 | 0.7444 |
| 0.0 | 92.0 | 16376 | 2.7327 | 0.7444 |
| 0.0 | 93.0 | 16554 | 2.4082 | 0.7781 |
| 0.0 | 94.0 | 16732 | 2.4077 | 0.7753 |
| 0.0 | 95.0 | 16910 | 2.4185 | 0.7781 |
| 0.0 | 96.0 | 17088 | 2.6096 | 0.7528 |
| 0.0 | 97.0 | 17266 | 2.5907 | 0.7669 |
| 0.0 | 98.0 | 17444 | 2.6030 | 0.7556 |
| 0.0 | 99.0 | 17622 | 2.6081 | 0.7528 |
| 0.0 | 100.0 | 17800 | 2.6081 | 0.7528 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"BertForQuestionAnswering"
],
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}
| 5
| 2023-03-28T16:14:26Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9375621066578337
- name: Recall
type: recall
value: 0.9527095254123191
- name: F1
type: f1
value: 0.9450751252086811
- name: Accuracy
type: accuracy
value: 0.9867251427562254
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.9376
- Recall: 0.9527
- F1: 0.9451
- Accuracy: 0.9867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0858 | 1.0 | 1756 | 0.0682 | 0.9096 | 0.9330 | 0.9212 | 0.9817 |
| 0.0387 | 2.0 | 3512 | 0.0609 | 0.9310 | 0.9487 | 0.9397 | 0.9859 |
| 0.0209 | 3.0 | 5268 | 0.0606 | 0.9376 | 0.9527 | 0.9451 | 0.9867 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.10.2+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 28
| 2023-03-28T16:16:58Z
|
---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
inference: true
---
this is a model that puts heavy emphasis on artistic and surreal elements with extremely high image detail, extremely flexible, can create beautiful images with simple prompts, negative prompts are not very important. Works best with step count greater than 30 preferably 50 steps, and native resolution up to 768x768, for example 2:3 frame resolution is 768x1024. CFG to around 11 to 15, highres fix will look better but not necessarily if you feel it takes too long
For those of you who don't have a pc or a weak computer, you can consider using my model via sinkin and mage website using the link below:
https://sinkin.ai/m/EYWOblK
negative prompt:
sketch, (worst quality:1.5), (low quality:1.5), (normal quality:1.5), lowres, bad anatomy, bad hands, ((monochrome)), ((grayscale)), collapsed eyeshadow, multiple eyeblows, vaginas in breasts, (cropped), oversaturated, extra limb, missing limbs, deformed hands, long neck, long body, imperfect, (bad hands), signature, watermark, username, artist name, conjoined fingers, deformed fingers, ugly eyes, imperfect eyes, skewed eyes, unnatural face, unnatural body, error
Please support me by becoming a patron:
https://www.patreon.com/duchaitenreal
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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}
| 24
| 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: grinsepilz/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
| 5
| 2023-03-28T16:19:27Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-IBM-argQ-30k-finetuned-convincingness-acl2016
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-IBM-argQ-30k-finetuned-convincingness-acl2016
This model is a fine-tuned version of [jakub014/bert-base-uncased-IBM-argQ-30k](https://huggingface.co/jakub014/bert-base-uncased-IBM-argQ-30k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4143
- Accuracy: 0.9266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3496 | 1.0 | 583 | 0.2207 | 0.9133 |
| 0.1779 | 2.0 | 1166 | 0.2128 | 0.9159 |
| 0.1439 | 3.0 | 1749 | 0.3202 | 0.9262 |
| 0.0903 | 4.0 | 2332 | 0.4013 | 0.9258 |
| 0.051 | 5.0 | 2915 | 0.4143 | 0.9266 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-xnli
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
| 36
| 2023-03-28T16:30:43Z
|
---
tags:
- generated_from_trainer
datasets:
- poquad
model-index:
- name: model_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_v1
This model is a fine-tuned version of [henryk/bert-base-multilingual-cased-finetuned-polish-squad2](https://huggingface.co/henryk/bert-base-multilingual-cased-finetuned-polish-squad2) on the poquad 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 1.5455 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"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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}
| 27
| 2023-03-28T16:31:12Z
|
---
library_name: transformers
pipeline_tag: image-classification
---
|
dccuchile/distilbert-base-spanish-uncased-finetuned-ner
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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"translation_en_to_ro": {
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}
}
| 28
| 2023-03-28T16:33:07Z
|
---
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
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pos
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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}
}
| 3
| 2023-03-28T16:35:00Z
|
# `vocabtrimmer/xlm-roberta-base-trimmed-en-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 68.28 | 68.28 | 68.28 | 67.86 | 68.28 | 68.19 | 68.28 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-tweet-sentiment-en/raw/main/eval.json).
|
dccuchile/distilbert-base-spanish-uncased-finetuned-xnli
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
| 31
| 2023-03-28T16:37:40Z
|
---
title: VN
emoji: 🚀
colorFrom: purple
colorTo: gray
sdk: gradio
app_file: app.py
pinned: false
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`sdk_version` : _string_
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate
|
[
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
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},
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}
}
| 7
| null |
---
language:
- 'no'
- nb
- nn
inference: false
tags:
- BERT
- NorBERT
- Norwegian
- encoder
license: cc-by-4.0
---
# NorBERT 3 small
## Other sizes:
- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
## Example usage
This model currently needs a custom wrapper from `modeling_norbert.py`. Then you can use it like this:
```python
import torch
from transformers import AutoTokenizer
from modeling_norbert import NorbertForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("path/to/folder")
bert = NorbertForMaskedLM.from_pretrained("path/to/folder")
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
output_p = bert(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `NorbertForMaskedLM`, `NorbertForSequenceClassification`, `NorbertForTokenClassification`, `NorbertForQuestionAnswering` and `NorbertForMultipleChoice`.
|
CennetOguz/distilbert-base-uncased-finetuned-recipe
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 2
| 2023-03-28T16:47:58Z
|
---
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: 987.20 +/- 91.86
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
...
```
|
Chaddmckay/Cdm
|
[] | null |
{
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},
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"early_stopping": null,
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}
}
}
| 0
| null |
---
language:
- 'no'
- nb
- nn
inference: false
tags:
- BERT
- NorBERT
- Norwegian
- encoder
license: cc-by-4.0
---
# NorBERT 3 xs
## Other sizes:
- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
## Example usage
This model currently needs a custom wrapper from `modeling_norbert.py`. Then you can use it like this:
```python
import torch
from transformers import AutoTokenizer
from modeling_norbert import NorbertForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("path/to/folder")
bert = NorbertForMaskedLM.from_pretrained("path/to/folder")
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
output_p = bert(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `NorbertForMaskedLM`, `NorbertForSequenceClassification`, `NorbertForTokenClassification`, `NorbertForQuestionAnswering` and `NorbertForMultipleChoice`.
|
Chaewon/mnmt_decoder_en_gpt2
|
[] | null |
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},
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},
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"early_stopping": null,
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},
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"early_stopping": 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: 267.89 +/- 20.96
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
...
```
|
Chaima/TunBerto
|
[] | null |
{
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}
}
| 0
| null |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1-5 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
### Original GitHub Repository
1. Download the weights
- [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
- [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
Currently six Stable Diffusion checkpoints are provided, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
Chakita/Friends
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
}
| 8
| null |
# `vocabtrimmer/xlm-roberta-base-trimmed-en-5000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-5000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-5000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 64.83 | 64.83 | 64.83 | 64.56 | 64.83 | 65.35 | 64.83 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-5000-tweet-sentiment-en/raw/main/eval.json).
|
Champion/test_upload_vox2_wavlm_epoch8
|
[
"sidekit",
"audio"
] | null |
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| 0
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.62 +/- 0.28
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
...
```
|
Chan/distilgpt2-finetuned-wikitext2
|
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}
| 0
| null |
# `vocabtrimmer/xlm-roberta-base-trimmed-en-10000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-10000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-10000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 66.9 | 66.9 | 66.9 | 66.64 | 66.9 | 66.71 | 66.9 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-10000-tweet-sentiment-en/raw/main/eval.json).
|
Chan/distilroberta-base-finetuned-wikitext2
|
[] | 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: 593.00 +/- 152.34
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 dhorbach -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 dhorbach -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 dhorbach
```
## 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)])
```
|
Charlotte/text2dm_models
|
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}
}
| 0
| null |
white woman, light brown eyes, blond hair jumping with a parachute
|
Charlotte77/model_test
|
[] | null |
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}
| 0
| null |
---
tags:
- endpoints-template
library_name: generic
duplicated_from: philschmid/gpt-j-6B-fp16-sharded
---
# Shareded fp16 copy of [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B)
> This is fork of [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) with shareded fp16 weights implementing a custom `handler.py` as an example for how to use `gpt-j` [inference-endpoints](https://hf.co/inference-endpoints)
|
ChaseBread/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 9
| null |
---
datasets:
- natural_instructions
- the_pile
- cot
- Muennighoff/P3
inference:
parameters:
max_new_tokens: 5
temperature: 1
top_k: 1
license: apache-2.0
language:
- en
pipeline_tag: text-generation
widget:
- example_title: Sentiment Analysis
text: >-
The task is to label the post's emotion as sadness, joy, love, anger, fear,
or surprise.
Input: I'm feeling quite sad and sorry for myself but ill snap out of it
soon.
Output: sadness
Input: I am just feeling cranky and blue.
Output: anger
Input: I can have for a treat or if i am feeling festive.
Output:
- example_title: Country Currency
text: |-
Return the currency of the given country.
Input: Switzerland
Output: Swiss Franc
Input: India
Output:
- example_title: Tweet Eval Hate
text: >-
Label whether the following tweet contains hate speech against either
immigrants or women. Hate Speech (HS) is commonly defined as any
communication that disparages a person or a group on the basis of some
characteristic such as race, color, ethnicity, gender, sexual orientation,
nationality, religion, or other characteristics.
Possible labels:
1. hate speech
2. not hate speech
Tweet: HOW REFRESHING! In South Korea, there is no such thing as 'political
correctness" when it comes to dealing with Muslim refugee wannabes via @user
Label: hate speech
Tweet: New to Twitter-- any men on here know what the process is to get
#verified?
Label: not hate speech
Tweet: Dont worry @user you are and will always be the most hysterical
woman.
Label:
- example_title: Entity Recognition
text: >-
Extract all the names of people, places, and organizations from the
following sentences.
Sentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas last
May.
Entities: Satya Nadella, Microsoft, Bahamas
Sentence: Pacific Northwest cities include Seattle and Portland, which I
have visited with Vikash.
Entities:
- example_title: Data Clearning
text: |-
Format the data into a CSV file:
Input: Jane Doe jane.doe@gmail.com (520) 382 2435
Output: Jane Doe,jane.doe@gmail.com,520-382-2435
Input: Peter Lee (510) 333-2429 email: peter@yahoo.com
Output:
duplicated_from: togethercomputer/GPT-JT-6B-v1
---
<h1 style="font-size: 42px">GPT-JT<h1/>
***<p style="font-size: 24px">Feel free to try out our [Online Demo](https://huggingface.co/spaces/togethercomputer/GPT-JT)!</p>***
# Model Summary
> With a new decentralized training algorithm, we fine-tuned GPT-J (6B) on 3.53 billion tokens, resulting in GPT-JT (6B), a model that outperforms many 100B+ parameter models on classification benchmarks.
We incorporated a collection of open techniques and datasets to build GPT-JT:
- GPT-JT is a fork of [EleutherAI](https://www.eleuther.ai)'s [GPT-J (6B)](https://huggingface.co/EleutherAI/gpt-j-6B);
- We used [UL2](https://github.com/google-research/google-research/tree/master/ul2)'s training objective, allowing the model to see bidirectional context of the prompt;
- The model was trained on a large collection of diverse data, including [Chain-of-Thought (CoT)](https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html), [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3), [Natural-Instructions (NI) dataset](https://github.com/allenai/natural-instructions).
With the help of techniques mentioned above, GPT-JT significantly improves the performance of classification tasks over the original GPT-J, and even outperforms most 100B+ parameter models!
# Quick Start
```python
from transformers import pipeline
pipe = pipeline(model='togethercomputer/GPT-JT-6B-v1')
pipe('''"I love this!" Is it positive? A:''')
```
or
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-JT-6B-v1")
```
# License
The weights of GPT-JT-6B-v1 are licensed under version 2.0 of the Apache License.
# Training Details
## UL2 Training Objective
We train GPT-JT using UL2 training objective [1][2].
The original GPT-J uses causal mask (as shown below left) for autoregressive generation. So for each token, it can only see its previous context.
In order to fully leverage the context information, we continue to train GPT-J with UL2 training objectives, and uses causal mask with prefix (as shown below right) -- using bidirectional attention for the prompt / input and causal attention for token generation.
Intuitively, being able to see context bidirectionally might improve downstream tasks that require this information.
$$
\begin{bmatrix}
1 & 0 & 0 & 0 & 0 \\
1 & 1 & 0 & 0 & 0 \\
1 & 1 & 1 & 0 & 0 \\
1 & 1 & 1 & 1 & 0 \\
1 & 1 & 1 & 1 & 1
\end{bmatrix}
\begin{bmatrix}
1 & 1 & 1 & 0 & 0 \\
1 & 1 & 1 & 0 & 0 \\
1 & 1 & 1 & 0 & 0 \\
1 & 1 & 1 & 1 & 0 \\
1 & 1 & 1 & 1 & 1
\end{bmatrix}
$$
Furthermore, we leverage a large collection of data, including [Natural-Instructions](https://github.com/allenai/natural-instructions), [P3](https://huggingface.co/datasets/Muennighoff/P3), [MMLU-COT](https://github.com/jasonwei20/flan-2/blob/main/mmlu-cot.json), and [the Pile](https://huggingface.co/datasets/the_pile)
Specifically, we first conduct training for 2.62 billion tokens using the UL2 loss on the Pile, followed by 0.92 billion tokens with a mixture of the above datasets: 5% of COT, 20% of P3, 20% of NI, and 55% of the Pile.
## Hyperparameters
We used AdamW with a learning rate of 1e-5 and global batch size of 64 (16 for each data parallel worker).
We used mix-precision training where the activation is in FP16 while the optimizer states are kept in FP32.
We use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 2048 tokens, and for input sequence that contains less than 2048 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.
## Infrastructure
We used [the Together Research Computer](https://together.xyz/) to conduct training.
# References
[1]: Tay, Yi, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, and Donald Metzler. "Unifying Language Learning Paradigms." arXiv preprint arXiv:2205.05131 (2022).
[2]: Tay, Yi, Jason Wei, Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia et al. "Transcending scaling laws with 0.1% extra compute." arXiv preprint arXiv:2210.11399 (2022).
|
ChauhanVipul/BERT
|
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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: 529.50 +/- 195.35
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga joe-hug -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 joe-hug -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 joe-hug
```
## 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)])
```
|
Cheatham/xlm-roberta-base-finetuned
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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}
| 20
| null |
# `vocabtrimmer/xlm-roberta-base-trimmed-en-15000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-15000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-15000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 67.59 | 67.59 | 67.59 | 67.69 | 67.59 | 68.04 | 67.59 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-15000-tweet-sentiment-en/raw/main/eval.json).
|
Cheatham/xlm-roberta-large-finetuned-d12
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
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}
| 20
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 36.80 +/- 25.61
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
|
Cheatham/xlm-roberta-large-finetuned-d1r01
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"XLMRobertaForSequenceClassification"
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}
| 21
| null |
# `cardiffnlp/xlm-v-base-tweet-sentiment-it`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (italian).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(italian).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 70.34 | 70.34 | 70.34 | 70.23 | 70.34 | 73.33 | 70.34 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-it/raw/main/eval.json).
|
Cheatham/xlm-roberta-large-finetuned-r01
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"XLMRobertaForSequenceClassification"
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}
| 23
| null |
---
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: -2.95 +/- 0.32
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
...
```
|
Cheatham/xlm-roberta-large-finetuned
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
| 20
| null |
# `cardiffnlp/xlm-v-base-tweet-sentiment-pt`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (portuguese).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(portuguese).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 67.01 | 67.01 | 67.01 | 66.6 | 67.01 | 67.49 | 67.01 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-pt/raw/main/eval.json).
|
Cheatham/xlm-roberta-large-finetuned4
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"XLMRobertaForSequenceClassification"
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}
| 20
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Prgrg/ja-en-dataset-v3.0-subset-v1.0
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Prgrg/ja-en-dataset-v3.0-subset-v1.0
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-jap-en](https://huggingface.co/Helsinki-NLP/opus-mt-jap-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9548
- Validation Loss: 1.7305
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.0, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'LinearWarmupCosineSchedule', 'config': {'initial_learning_rate': 0.0005, 'num_warmup_steps': 56362, 'num_training_steps': 563628}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7629 | 1.9424 | 0 |
| 1.9548 | 1.7305 | 1 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Check/vaw2tmp
|
[
"tensorboard"
] | null |
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}
| 0
| null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-audio-generation
- diffusion-models-class
---
# Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional audio generation of music in the genre Electronic
## Usage
```python
from IPython.display import Audio
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("jackoyoungblood/audio-diffusion-electronic")
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
|
CheonggyeMountain-Sherpa/kogpt-trinity-poem
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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}
}
| 15
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-IBM-argQ-30k-finetuned-convincingness-IBM
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-IBM-argQ-30k-finetuned-convincingness-IBM
This model is a fine-tuned version of [jakub014/bert-base-uncased-IBM-argQ-30k](https://huggingface.co/jakub014/bert-base-uncased-IBM-argQ-30k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9264
- Accuracy: 0.7598
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 270 | 0.5303 | 0.7533 |
| 0.397 | 2.0 | 540 | 0.5559 | 0.7533 |
| 0.397 | 3.0 | 810 | 0.7691 | 0.7533 |
| 0.1903 | 4.0 | 1080 | 0.9264 | 0.7598 |
| 0.1903 | 5.0 | 1350 | 1.0564 | 0.7576 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper
|
[
"ko",
"gpt2",
"license:cc-by-nc-sa-4.0"
] | 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="manuelmaiorano/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"])
```
|
Chertilasus/main
|
[] | null |
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| 0
| null |
# `vocabtrimmer/xlm-roberta-base-trimmed-en-30000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-30000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-30000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 66.55 | 66.55 | 66.55 | 66.02 | 66.55 | 66.71 | 66.55 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-30000-tweet-sentiment-en/raw/main/eval.json).
|
Chinat/test-classifier
|
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| 0
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
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="manuelmaiorano/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"])
```
|
Ching/negation_detector
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
],
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| 9
| null |
---
license: openrail
tags:
- stable-diffusion
- stable-diffusion-diffusers
- controlnet
- endpoints-template
thumbnail: >-
https://huggingface.co/philschmid/ControlNet-endpoint/resolve/main/thumbnail.png
inference: true
duplicated_from: philschmid/ControlNet-endpoint
---
# Inference Endpoint for [ControlNet](https://huggingface.co/lllyasviel/ControlNet) using [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
> ControlNet is a neural network structure to control diffusion models by adding extra conditions.
> Official repository: https://github.com/lllyasviel/ControlNet
---
Blog post: [Controlled text to image generation with Inference Endpoints]()
This repository implements a custom `handler` task for `controlled text-to-image` generation on 🤗 Inference Endpoints. The code for the customized pipeline is in the [handler.py](https://huggingface.co/philschmid/ControlNet-endpoint/blob/main/handler.py).
There is also a [notebook](https://huggingface.co/philschmid/ControlNet-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py`
### expected Request payload
```json
{
"inputs": "A prompt used for image generation",
"negative_prompt": "low res, bad anatomy, worst quality, low quality",
"controlnet_type": "depth",
"image" : "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
}
```
supported `controlnet_type` are: `canny_edge`, `pose`, `depth`, `scribble`, `segmentation`, `normal`, `hed`, `hough`
below is an example on how to run a request using Python and `requests`.
## Use Python to send requests
1. Get image
```
wget https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_imgvar/input_image_vermeer.png
```
2. Use the following code to send a request to the endpoint
```python
import json
from typing import List
import requests as r
import base64
from PIL import Image
from io import BytesIO
ENDPOINT_URL = "" # your endpoint url
HF_TOKEN = "" # your huggingface token `hf_xxx`
# helper image utils
def encode_image(image_path):
with open(image_path, "rb") as i:
b64 = base64.b64encode(i.read())
return b64.decode("utf-8")
def predict(prompt, image, negative_prompt=None, controlnet_type = "normal"):
image = encode_image(image)
# prepare sample payload
request = {"inputs": prompt, "image": image, "negative_prompt": negative_prompt, "controlnet_type": controlnet_type}
# headers
headers = {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": "application/json",
"Accept": "image/png" # important to get an image back
}
response = r.post(ENDPOINT_URL, headers=headers, json=request)
if response.status_code != 200:
print(response.text)
raise Exception("Prediction failed")
img = Image.open(BytesIO(response.content))
return img
prediction = predict(
prompt = "cloudy sky background lush landscape house and green trees, RAW photo (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
negative_prompt ="lowres, bad anatomy, worst quality, low quality, city, traffic",
controlnet_type = "hed",
image = "huggingface.png"
)
prediction.save("result.png")
```
```
expected output
[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.
The abstract of the paper is the following:
We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.
|
Chinmay/mlindia
|
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| 0
| null |
---
license: creativeml-openrail-m
tags:
- text generation
- conversational
- gptq
- 4bit
inference: false
language:
- en
pipeline_tag: text-generation
---
GPTQ quantization of https://huggingface.co/PygmalionAI/pygmalion-6b/commit/b8344bb4eb76a437797ad3b19420a13922aaabe1
Using this repository: https://github.com/mayaeary/GPTQ-for-LLaMa/tree/gptj-v2
Command:
```
python3 gptj.py models/pygmalion-6b_b8344bb4eb76a437797ad3b19420a13922aaabe1 c4 --wbits 4 --groupsize 128 --save_safetensors models/pygmalion-6b-4bit-128g.safetensors
```
|
ChrisP/xlm-roberta-base-finetuned-marc-en
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Regression_bert_7
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Regression_bert_7
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1702
- Train Mae: 0.2696
- Train Mse: 0.1221
- Train R2-score: 0.7766
- Validation Loss: 0.3290
- Validation Mae: 0.2756
- Validation Mse: 0.1076
- Validation R2-score: 0.8214
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.5303 | 0.3176 | 0.1540 | 0.7493 | 0.6752 | 0.3537 | 0.1857 | 0.6758 | 0 |
| 0.2316 | 0.2775 | 0.1261 | 0.7746 | 0.2451 | 0.3060 | 0.1466 | 0.7473 | 1 |
| 0.2780 | 0.2930 | 0.1373 | 0.8061 | 0.1807 | 0.2593 | 0.1127 | 0.8102 | 2 |
| 0.1776 | 0.2673 | 0.1177 | 0.6536 | 0.1407 | 0.2617 | 0.1181 | 0.7975 | 3 |
| 0.2248 | 0.2906 | 0.1349 | 0.7639 | 0.1896 | 0.2915 | 0.1364 | 0.7665 | 4 |
| 0.2295 | 0.2718 | 0.1196 | 0.7991 | 0.2038 | 0.2757 | 0.1248 | 0.7882 | 5 |
| 0.2443 | 0.2460 | 0.0975 | 0.7298 | 0.1509 | 0.2779 | 0.1301 | 0.7783 | 6 |
| 0.2538 | 0.2907 | 0.1343 | 0.7783 | 0.1930 | 0.2984 | 0.1426 | 0.7559 | 7 |
| 0.2067 | 0.2777 | 0.1281 | 0.7605 | 0.1537 | 0.2809 | 0.1318 | 0.7756 | 8 |
| 0.1702 | 0.2696 | 0.1221 | 0.7766 | 0.3290 | 0.2756 | 0.1076 | 0.8214 | 9 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ChristianOrr/madnet_keras
|
[
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
] |
depth-estimation
|
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}
| 0
| null |
# `vocabtrimmer/xlm-roberta-base-trimmed-en-60000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-60000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-roberta-base-trimmed-en-60000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 69.31 | 69.31 | 69.31 | 68.42 | 69.31 | 68.83 | 69.31 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-roberta-base-trimmed-en-60000-tweet-sentiment-en/raw/main/eval.json).
|
Chuah/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 9
| null |
---
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: 795.50 +/- 263.19
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga GillesEverling -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 GillesEverling -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 GillesEverling
```
## 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', 3000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Chun/DialoGPT-large-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
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| 6
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-IBM-argQ-30k-finetuned-effectiveness-dagstuhl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-IBM-argQ-30k-finetuned-effectiveness-dagstuhl
This model is a fine-tuned version of [jakub014/bert-base-uncased-IBM-argQ-30k](https://huggingface.co/jakub014/bert-base-uncased-IBM-argQ-30k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5516
- Accuracy: 0.7302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 16 | 0.5516 | 0.7302 |
| No log | 2.0 | 32 | 0.5431 | 0.6825 |
| No log | 3.0 | 48 | 0.5942 | 0.6349 |
| No log | 4.0 | 64 | 0.6533 | 0.6349 |
| No log | 5.0 | 80 | 0.6509 | 0.6667 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Chun/DialoGPT-small-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
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| 10
| null |
args = Seq2SeqTrainingArguments(
output_dir='dialogue_summarization_bart',
evaluation_strategy='steps',
eval_steps=500,
learning_rate=3e-5,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=4,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=5,
predict_with_generate=True,
fp16=True, # available only with CUDA
warmup_steps=500,
lr_scheduler_type='linear'
)
|
Chun/w-en2zh-mtm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 7
| 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="Zaleks/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"])
```
|
Chun/w-en2zh-otm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MBartForConditionalGeneration"
],
"model_type": "mbart",
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}
| 7
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.36 +/- 0.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **PandaReachDense-v2**
This is a trained model of a **PPO** 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
...
```
|
Chun/w-zh2en-mtm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 8
| 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: 242.58 +/- 13.50
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
...
```
|
Chun/w-zh2en-mto
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
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},
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},
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},
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"max_length": null,
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}
}
}
| 7
| 2023-03-28T18:39:53Z
|
# Vocabulary Trimmed [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en): `vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en`
This model is a trimmed version of [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-roberta-base-tweet-sentiment-en | vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en |
|:---------------------------|:-------------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 278,045,955 | 219,090,435 |
| parameter_size_embedding | 192,001,536 | 133,046,016 |
| vocab_size | 250,002 | 173,237 |
| compression_rate_full | 100.0 | 78.8 |
| compression_rate_embedding | 100.0 | 69.29 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | | 2 |
|
Chungu424/DATA
|
[] | 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: 576.00 +/- 303.16
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 msthil -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 msthil -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 msthil
```
## 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)])
```
|
Chungu424/qazwsx
|
[] | null |
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| 0
| 2023-03-28T18:42:08Z
|
---
license: creativeml-openrail-m
tags:
- text generation
- conversational
- gptq
- 4bit
inference: false
language:
- en
pipeline_tag: text-generation
---
GPTQ quantization of https://huggingface.co/PygmalionAI/pygmalion-6b/commit/30e2405100eac6bd53f75964cc7345eeafd19f7d
Using this repository: https://github.com/mayaeary/GPTQ-for-LLaMa/tree/gptj-v2
Command:
```
python3 gptj.py models/pygmalion-6b_dev c4 --wbits 4 --groupsize 128 --save_safetensors models/pygmalion-6b_dev-4bit-128g.safetensors
```
|
Chungu424/repo
|
[] | null |
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}
}
| 0
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Zaleks/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"])
```
|
Chuu/Chumar
|
[] | null |
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}
| 0
| 2023-03-28T18:46:14Z
|
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.82 +/- 5.64
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r SpookyWooky5/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Cilan/dalle-knockoff
|
[] | null |
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| 0
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en): `vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-5000`
This model is a trimmed version of [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-roberta-base-tweet-sentiment-en | vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-5000 |
|:---------------------------|:-------------------------------------------------|:-------------------------------------------------------------------|
| parameter_size_full | 278,045,955 | 89,885,955 |
| parameter_size_embedding | 192,001,536 | 3,841,536 |
| vocab_size | 250,002 | 5,002 |
| compression_rate_full | 100.0 | 32.33 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 5000 | 2 |
|
Ciruzzo/DialoGPT-medium-harrypotter
|
[] | null |
{
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}
| 0
| 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: 2095.95 +/- 44.51
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
...
```
|
Ciruzzo/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,
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},
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},
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},
"translation_en_to_ro": {
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}
}
}
| 9
| 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: 1514.91 +/- 134.78
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
...
```
|
Ciruzzo/DialoGPT-small-hattypotter
|
[] | null |
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},
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}
}
}
| 0
| 2023-03-28T18:53:21Z
|
# Vocabulary Trimmed [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en): `vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-10000`
This model is a trimmed version of [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-roberta-base-tweet-sentiment-en | vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-10000 |
|:---------------------------|:-------------------------------------------------|:--------------------------------------------------------------------|
| parameter_size_full | 278,045,955 | 93,725,955 |
| parameter_size_embedding | 192,001,536 | 7,681,536 |
| vocab_size | 250,002 | 10,002 |
| compression_rate_full | 100.0 | 33.71 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 10000 | 2 |
|
Clarianliz30/Caitlyn
|
[] | null |
{
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}
}
| 0
| 2023-03-28T18:56:27Z
|
# `cardiffnlp/xlm-v-base-tweet-sentiment-es`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (spanish).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(spanish).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 63.22 | 63.22 | 63.22 | 60.73 | 63.22 | 62.15 | 63.22 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-es/raw/main/eval.json).
|
ClaudeCOULOMBE/RickBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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"max_length": null,
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},
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}
| 9
| 2023-03-28T18:56:30Z
|
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.80 +/- 4.98
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r JfuentesR/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
ClaudeYang/awesome_fb_model
|
[
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
] |
zero-shot-classification
|
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 26
| 2023-03-28T18:56:45Z
|
# Vocabulary Trimmed [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en): `vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-15000`
This model is a trimmed version of [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-roberta-base-tweet-sentiment-en | vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-15000 |
|:---------------------------|:-------------------------------------------------|:--------------------------------------------------------------------|
| parameter_size_full | 278,045,955 | 97,565,955 |
| parameter_size_embedding | 192,001,536 | 11,521,536 |
| vocab_size | 250,002 | 15,002 |
| compression_rate_full | 100.0 | 35.09 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 15000 | 2 |
|
CleveGreen/FieldClassifier
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"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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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
}
| 34
| null |
# `cardiffnlp/xlm-v-base-tweet-sentiment-ar`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 60 | 60 | 60 | 59.83 | 60 | 59.94 | 60 |
Check the result file [here](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-ar/raw/main/eval.json).
|
CleveGreen/JobClassifier
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"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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"prefix": null
},
"translation_en_to_fr": {
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},
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"early_stopping": null,
"max_length": null,
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}
}
}
| 31
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en): `vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-30000`
This model is a trimmed version of [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-roberta-base-tweet-sentiment-en | vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-30000 |
|:---------------------------|:-------------------------------------------------|:--------------------------------------------------------------------|
| parameter_size_full | 278,045,955 | 109,085,955 |
| parameter_size_embedding | 192,001,536 | 23,041,536 |
| vocab_size | 250,002 | 30,002 |
| compression_rate_full | 100.0 | 39.23 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 30000 | 2 |
|
CleveGreen/JobClassifier_v2
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"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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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
| 37
| 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="msthil2/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"])
```
|
CleveGreen/JobClassifier_v2_gpt
|
[
"pytorch",
"gpt2",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"GPT2ForSequenceClassification"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 27
| null |
---
tags:
- Taxi-v3-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Unit2-RL-Course-Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3-4x4-no_slippery
type: Taxi-v3-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
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="msthil2/Unit2-RL-Course-Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Clint/clinton
|
[] | 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: 225.06 +/- 46.44
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
...
```
|
Cloudy/DialoGPT-CJ-large
|
[
"pytorch",
"conversational"
] |
conversational
|
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| 1
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en): `vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-60000`
This model is a trimmed version of [cardiffnlp/xlm-roberta-base-tweet-sentiment-en](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-en) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-roberta-base-tweet-sentiment-en | vocabtrimmer/xlm-roberta-base-tweet-sentiment-en-trimmed-en-60000 |
|:---------------------------|:-------------------------------------------------|:--------------------------------------------------------------------|
| parameter_size_full | 278,045,955 | 132,125,955 |
| parameter_size_embedding | 192,001,536 | 46,081,536 |
| vocab_size | 250,002 | 60,002 |
| compression_rate_full | 100.0 | 47.52 |
| compression_rate_embedding | 100.0 | 24.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 60000 | 2 |
|
CoShin/XLM-roberta-large_ko_en_nil_sts
|
[] | 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: 625.50 +/- 164.61
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 Infatum -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 Infatum -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 Infatum
```
## 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)])
```
|
CodeNinja1126/test-model
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 24
| null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.29 +/- 4.46
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r JfuentesR/rl_course_vizdoom_health_gathering_supreme_v
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_v
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_v --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Venkatakrishnan-Ramesh/Text_gen
|
[] | null |
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| 0
| 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: npit/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CoffeeAddict93/gpt1-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 8
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 36.30 +/- 23.39
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CoffeeAddict93/gpt1-modest-proposal
|
[
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
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"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
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}
| 11
| null |
---
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
---
# DreamBooth model for the groot concept traine on the LinoyTsaban/dreambooth-hackathon-images-groot dataset.
This is a Stable Diffusion model fine-tuned on the groot concept with DreamBooth. It can be used by modifying the `instance_prompt`: groot figurine
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
params used:
scheduler- DDIMScheduler
resolution=512
learning_rate=2e-06
max_train_steps= 500
train_batch_size=1
gradient_accumulation_steps=2
max_grad_norm=1.0
gradient_checkpointing=True
use_8bit_adam=True
seed=14071995,
sample_batch_size=2
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('LinoyTsaban/dreambooth-groot')
name_of_concept = "groot"
type_of_thing = "figurine"
prompt = f"a photo of groot figurine in the Louvre museum"
# Tune the guidance to control how closely the generations follow the prompt.
# Values between 7-11 usually work best
guidance_scale = 8 #
image = pipeline(prompt, guidance_scale=guidance_scale).images[0]
image
```
|
CoffeeAddict93/gpt2-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 6
| null |
<p><strong><font size="5">Information</font></strong></p>
Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.
<p>Quantized using <i>--true-sequential</i> and <i>--act-order</i> optimizations.</p>
This was made using Chansung's 30B Alpaca Lora: https://huggingface.co/chansung/alpaca-lora-30b
<p><strong><font size="5">Update 04.06.2023</font></strong></p>
<p>This is a more recent merge of Chansung's Alpaca Lora which was updated using the clean alpaca dataset as of 04/06/2023 with refined training parameters</p>
<p><strong>Training Parameters</strong></p>
<ul><li>num_epochs=10</li><li>cutoff_len=512</li><li>group_by_length</li><li>lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'</li><li>lora_r=16</li><li>micro_batch_size=8</li></ul>
<p><strong><font size="5">Benchmarks</font></strong></p>
<strong>Wikitext2</strong>: 4.608365058898926
<strong>Ptb-New</strong>: 8.69663143157959
<strong>C4-New</strong>: 6.624773979187012
<strong>Note</strong>: This version does not use <i>--groupsize 128</i>, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
|
CoffeeAddict93/gpt2-medium-modest-proposal
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"max_length": 50
},
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| 7
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.86 +/- 0.79
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
...
```
|
CogComp/roberta-temporal-predictor
|
[
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
| 14
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-test
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. -->
# gpt-test
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.0
- Datasets 2.9.0
- Tokenizers 0.13.1
|
ComCom/gpt2-medium
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"GPT2Model"
],
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}
| 5
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
|
Connorvr/BrightBot-small
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 7
| 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: 269.59 +/- 20.52
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
...
```
|
Coolhand/Sentiment
|
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| 0
| null |
---
datasets:
- IlyaGusev/ru_turbo_alpaca
language:
- ru
widget:
- text: |-
Задание: Сочини длинный рассказ, обязательно упоминая следующие объекты.
Вход: Таня, мяч
Выход:
example_title: Таня и мяч
- text: |-
Задание: Заполни пропуск в предложении, выведи только одно слово.
Вход: Я пытался ____ от маньяка, но он меня настиг.
Выход:
example_title: Маньяк
- text: |-
Как приготовить лазанью?
Ответ:
example_title: Лазанья
- text: |-
Вопрос: Почему трава зелёная?
Ответ:
example_title: Зелёная трава
- text: >-
Могут ли в природе встретиться в одном месте белый медведь и пингвин? Если
нет, то почему?
Выход:
example_title: Медведь и пигвин
- text: |-
Задание: Реши уравнение: 4x + 5 = 21
Выход:
example_title: Уравнение
pipeline_tag: text2text-generation
---
|
CrayonShinchan/bart_fine_tune_test
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.56
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0337
- Meanabsoluteerror: 0.532
- Accuracy: 0.56
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 1.1731 | 1.0 | 313 | 1.0337 | 0.532 | 0.56 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CrayonShinchan/fine_tune_try_1
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: meetings_summaries__t5-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. -->
# meetings_summaries__t5-base
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 1.11.0+cu113
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CrisLeaf/generador-de-historias-de-tolkien
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
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| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-IBM-argQ-30k-finetuned-sufficiency-ukp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-IBM-argQ-30k-finetuned-sufficiency-ukp
This model is a fine-tuned version of [jakub014/bert-base-uncased-IBM-argQ-30k](https://huggingface.co/jakub014/bert-base-uncased-IBM-argQ-30k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5490
- Accuracy: 0.8835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 52 | 0.4372 | 0.8107 |
| No log | 2.0 | 104 | 0.3424 | 0.8786 |
| No log | 3.0 | 156 | 0.4970 | 0.8689 |
| No log | 4.0 | 208 | 0.5267 | 0.8786 |
| No log | 5.0 | 260 | 0.5490 | 0.8835 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Cthyllax/DialoGPT-medium-PaladinDanse
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 10
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi-vit
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9774436090225563
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.138 | 3.85 | 500 | 0.0639 | 0.9774 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/XLMR-ENIS-finetuned-ner
|
[
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
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"XLMRobertaForTokenClassification"
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}
| 6
| 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: Nazzyk/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.549
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0177
- Meanabsoluteerror: 0.538
- Accuracy: 0.549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 1.1226 | 1.0 | 313 | 1.0177 | 0.538 | 0.549 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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| 1
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.553
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0082
- Meanabsoluteerror: 0.531
- Accuracy: 0.553
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 1.1274 | 1.0 | 313 | 1.0082 | 0.531 | 0.553 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.55
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0092
- Meanabsoluteerror: 0.527
- Accuracy: 0.55
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 1.1059 | 1.0 | 313 | 1.0092 | 0.527 | 0.55 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_1
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.551
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0037
- Meanabsoluteerror: 0.527
- Accuracy: 0.551
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 0.9999 | 1.0 | 313 | 1.0037 | 0.527 | 0.551 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ancc
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.557
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0169
- Meanabsoluteerror: 0.533
- Accuracy: 0.557
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 1.1711 | 1.0 | 313 | 1.0169 | 0.533 | 0.557 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-IBM-argQ-30k-finetuned-sufficiency-dagstuhl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-IBM-argQ-30k-finetuned-sufficiency-dagstuhl
This model is a fine-tuned version of [jakub014/bert-base-uncased-IBM-argQ-30k](https://huggingface.co/jakub014/bert-base-uncased-IBM-argQ-30k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5933
- Accuracy: 0.6984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 16 | 0.5933 | 0.6984 |
| No log | 2.0 | 32 | 0.6388 | 0.6190 |
| No log | 3.0 | 48 | 0.7638 | 0.6349 |
| No log | 4.0 | 64 | 0.8638 | 0.6190 |
| No log | 5.0 | 80 | 0.9086 | 0.6349 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
"model_type": "marian",
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| 1
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: nli-roberta-base-finetuned-for-amazon-review-ratings
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: validation
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.33
---
<!-- 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. -->
# nli-roberta-base-finetuned-for-amazon-review-ratings
This model is a fine-tuned version of [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6148
- Meanabsoluteerror: 1.215
- Accuracy: 0.33
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meanabsoluteerror | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|
| 1.679 | 1.0 | 32 | 1.6148 | 1.215 | 0.33 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora-two
|
[
"pytorch",
"conversational"
] |
conversational
|
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}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93156
---
<!-- 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_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2315
- Accuracy: 0.9316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2327 | 1.0 | 1563 | 0.1883 | 0.9281 |
| 0.152 | 2.0 | 3126 | 0.2315 | 0.9316 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 7
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 24.20 +/- 17.40
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
|
CurtisBowser/DialoGPT-small-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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}
| 7
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: results_v4c_gpt_medium_original_no_eval
results: []
datasets:
- squad
- squad_v1_pt
language:
- pt
library_name: transformers
inference:
parameters:
do_sample: false
max_new_tokens: 120
widget:
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Onde foi descoberta a Covid-19?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Onde a COVID-19 foi identificada pela primeira vez?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando a COVID-19 foi identificada pela primeira vez?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando a doença foi reportada pela primeira vez?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Em que ano a doença foi identificada pela primeira vez?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Quem foi o autor dos livros Game of Thrones?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Quem foi o escritor dos livros Game of Thrones?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Quem são os produtores executivos da série de TV Game of Thrones?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Onde foram realizadas as filmagens da série Game of Thrones?<|assistant|>"
- text: '<|prompter|>O sistema de bibliotecas da universidade é dividido entre a biblioteca principal e cada uma das faculdades e escolas. O edifício principal é a Biblioteca Theodore M. Hesburgh, de 14 andares, concluída em 1963, que é o terceiro edifício a abrigar a principal coleção de livros. A frente da biblioteca é decorada com o mural da Palavra da Vida, projetado pelo artista Millard Sheets. Este mural é conhecido popularmente como "Touchdown Jesus" devido à sua proximidade com o Estádio Notre Dame e os braços de Jesus aparecendo para sinalizar um touchdown. Quantos andares possui a Biblioteca Theodore M. Hesburgh?<|assistant|>'
- text: '<|prompter|>O sistema de bibliotecas da universidade é dividido entre a biblioteca principal e cada uma das faculdades e escolas. O edifício principal é a Biblioteca Theodore M. Hesburgh, de 14 andares, concluída em 1963, que é o terceiro edifício a abrigar a principal coleção de livros. A frente da biblioteca é decorada com o mural da Palavra da Vida, projetado pelo artista Millard Sheets. Este mural é conhecido popularmente como "Touchdown Jesus" devido à sua proximidade com o Estádio Notre Dame e os braços de Jesus aparecendo para sinalizar um touchdown. Em que ano a Biblioteca Theodore M. Hesburgh em Notre Dame terminou?<|assistant|>'
- text: '<|prompter|>Rick Grimes é o xerife de uma pequena cidade do estado da Georgia, quando certo dia, é baleado por criminosos durante uma perseguição e entra em coma. Semanas depois, ele acorda em um hospital abandonado e totalmente danificado. Ao sair do hospital, Rick se encontra em um mundo pós-apocalíptico dominado por mortos-vivos. Depois de conhecer Morgan Jones e seu filho, Duane, que lhe explica o novo mundo, Rick decide ir para Atlanta atrás de sua família, onde um possível centro de refugiados foi montado pela Guarda Nacional. Ao chegar em Atlanta, ele logo descobre que a cidade está vazia e foi dominada pelos mortos. Quem o xerife Rick Grimes conheceu?<|assistant|>'
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-medium-squadv11-portuguese
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on squad_v1.1_pt dataset.
** It's a chatbot experiment. ;)
The model was trained in 12 hours on a NVIDIA RTX 3060 12GB.
### Usage:
```
$ python3
>>> from transformers import pipeline, set_seed
>>> set_seed(42)
>>> generator = pipeline('text-generation', model="egonrp/gpt2-medium-squadv11-portuguese")
>>> result = generator('<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>', max_new_tokens=110, num_return_sequences=1, do_sample=False)
>>> print(result)
[{'generated_text': '<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>31 de dezembro do mesmo ano'}]
```
### Usage.2:
```
$ python3
>>> from transformers import GPT2LMHeadModel, GPT2Tokenizer, set_seed
>>> set_seed(42)
>>> model = GPT2LMHeadModel.from_pretrained("egonrp/gpt2-medium-squadv11-portuguese")
>>> tokenizer = GPT2Tokenizer.from_pretrained("egonrp/gpt2-medium-squadv11-portuguese")
>>> tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
>>> model.config.pad_token_id = tokenizer.eos_token_id
>>> prompt_text = '<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>'
>>> encoded_prompt = tokenizer.encode(prompt_text, return_tensors="pt")
>>> output_sequences = model.generate(
input_ids=encoded_prompt,
do_sample=False,
num_return_sequences=1,
max_new_tokens=110,
eos_token_id=model.config.eos_token_id,
pad_token_id=model.config.eos_token_id
)
>>> decoded_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
>>> print(decoded_text)
<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>31 de dezembro do mesmo ano
```
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
```
git clone -b v4.27-release https://github.com/huggingface/transformers.git
cd transformers/examples/pytorch/language-modeling/
pip install -r requirements.txt
pip install transformers==v4.27.3
python3 run_clm.py \
--model_name_or_path gpt2-medium \
--train_file /home/egon/dev/gptsquad_data/converted_squad_merged_out_v4c.txt \
--do_train \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--output_dir /home/egon/dev/gptsquad_model/results_v4c_gpt_medium_original_no_eval \
--fp16
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CyberMuffin/DialoGPT-small-ChandlerBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
}
| 9
| 2023-03-28T22:56:44Z
|
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1620236289197883393/Ocb1dH3B_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1541698302231601152/UOfICVts_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1628703245224001540/GOgDmlQj_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Wifey & Ninja & PROFIT ₿LUE</div>
<div style="text-align: center; font-size: 14px;">@ninjascalp-profit8lue-wifeyalpha</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 Wifey & Ninja & PROFIT ₿LUE.
| Data | Wifey | Ninja | PROFIT ₿LUE |
| --- | --- | --- | --- |
| Tweets downloaded | 3220 | 3250 | 3249 |
| Retweets | 273 | 24 | 70 |
| Short tweets | 718 | 584 | 406 |
| Tweets kept | 2229 | 2642 | 2773 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bna4xc9/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 @ninjascalp-profit8lue-wifeyalpha's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/i08fcyyd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/i08fcyyd/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/ninjascalp-profit8lue-wifeyalpha')
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)
|
Cyrell/Cyrell
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"max_length": null,
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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
}
}
}
| 0
| null |
# Introdução
# Git
Clonar o projeto
```
git clone git@ssh.dev.azure.com:v3/gcp-projects/jornada-dados/bq_load
```
# Instalação
Python e CLI Google abaixo
```
python -m venv .\venv
.\venv\Scripts\activate
pip install -r requirements.txt
python -m pip install --upgrade pip
```
Teste instalação
```bash
python -c "from huggingface_hub import model_info; print(model_info('gpt2'))"
```
# Login
```bash
huggingface-cli login
git credential helpers
```
```bash
.\venv\Scripts\activate && python py_src\repo.py
.\venv\Scripts\activate && python py_src\up.py
```
# Links de apoio
- [Quickstart](https://huggingface.co/docs/huggingface_hub/quick-start#quickstart)
- [Installation](https://huggingface.co/docs/huggingface_hub/installation#installation)
- []()
- []()
- []()
|
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