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mavonic_private_repos | mavonic_private_repos/transformers/README_ko.md | <!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/>
<br>
</p>
<p align="center">
<a href="https://circleci.com/gh/huggingface/transformers">
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
</a>
<a href="https://huggingface.co/docs/transformers/index">
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
</a>
<a href="https://github.com/huggingface/transformers/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
</a>
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
</p>
<h4 align="center">
<p>
<a href="https://github.com/huggingface/transformers/">English</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
<b>한국어</b> |
<a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> |
</p>
</h4>
<h3 align="center">
<p> Jax, Pytorch, TensorFlow를 위한 최첨단 자연어처리</p>
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
</h3>
🤗 Transformers는 분류, 정보 추출, 질문 답변, 요약, 번역, 문장 생성 등을 100개 이상의 언어로 수행할 수 있는 수천개의 사전학습된 모델을 제공합니다. 우리의 목표는 모두가 최첨단의 NLP 기술을 쉽게 사용하는 것입니다.
🤗 Transformers는 이러한 사전학습 모델을 빠르게 다운로드해 특정 텍스트에 사용하고, 원하는 데이터로 fine-tuning해 커뮤니티나 우리의 [모델 허브](https://huggingface.co/models)에 공유할 수 있도록 API를 제공합니다. 또한, 모델 구조를 정의하는 각 파이썬 모듈은 완전히 독립적이여서 연구 실험을 위해 손쉽게 수정할 수 있습니다.
🤗 Transformers는 가장 유명한 3개의 딥러닝 라이브러리를 지원합니다. 이들은 서로 완벽히 연동됩니다 — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/). 간단하게 이 라이브러리 중 하나로 모델을 학습하고, 또 다른 라이브러리로 추론을 위해 모델을 불러올 수 있습니다.
## 온라인 데모
대부분의 모델을 [모델 허브](https://huggingface.co/models) 페이지에서 바로 테스트해볼 수 있습니다. 공개 및 비공개 모델을 위한 [비공개 모델 호스팅, 버전 관리, 추론 API](https://huggingface.co/pricing)도 제공합니다.
예시:
- [BERT로 마스킹된 단어 완성하기](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [Electra를 이용한 개체명 인식](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [GPT-2로 텍스트 생성하기](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
- [RoBERTa로 자연어 추론하기](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
- [BART를 이용한 요약](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [T5로 번역하기](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
**[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다.
## Hugging Face 팀의 커스텀 지원을 원한다면
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## 퀵 투어
원하는 텍스트에 바로 모델을 사용할 수 있도록, 우리는 `pipeline` API를 제공합니다. Pipeline은 사전학습 모델과 그 모델을 학습할 때 적용한 전처리 방식을 하나로 합칩니다. 다음은 긍정적인 텍스트와 부정적인 텍스트를 분류하기 위해 pipeline을 사용한 간단한 예시입니다:
```python
>>> from transformers import pipeline
# Allocate a pipeline for sentiment-analysis
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
```
코드의 두번째 줄은 pipeline이 사용하는 사전학습 모델을 다운로드하고 캐시로 저장합니다. 세번째 줄에선 그 모델이 주어진 텍스트를 평가합니다. 여기서 모델은 99.97%의 확률로 텍스트가 긍정적이라고 평가했습니다.
많은 NLP 과제들을 `pipeline`으로 바로 수행할 수 있습니다. 예를 들어, 질문과 문맥이 주어지면 손쉽게 답변을 추출할 수 있습니다:
``` python
>>> from transformers import pipeline
# Allocate a pipeline for question-answering
>>> question_answerer = pipeline('question-answering')
>>> question_answerer({
... 'question': 'What is the name of the repository ?',
... 'context': 'Pipeline has been included in the huggingface/transformers repository'
... })
{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
```
답변뿐만 아니라, 여기에 사용된 사전학습 모델은 확신도와 토크나이즈된 문장 속 답변의 시작점, 끝점까지 반환합니다. [이 튜토리얼](https://huggingface.co/docs/transformers/task_summary)에서 `pipeline` API가 지원하는 다양한 과제를 확인할 수 있습니다.
코드 3줄로 원하는 과제에 맞게 사전학습 모델을 다운로드 받고 사용할 수 있습니다. 다음은 PyTorch 버전입니다:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)
```
다음은 TensorFlow 버전입니다:
```python
>>> from transformers import AutoTokenizer, TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)
```
토크나이저는 사전학습 모델의 모든 전처리를 책임집니다. 그리고 (위의 예시처럼) 1개의 스트링이나 리스트도 처리할 수 있습니다. 토크나이저는 딕셔너리를 반환하는데, 이는 다운스트림 코드에 사용하거나 언패킹 연산자 ** 를 이용해 모델에 바로 전달할 수도 있습니다.
모델 자체는 일반적으로 사용되는 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)나 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)입니다. [이 튜토리얼](https://huggingface.co/transformers/training.html)은 이러한 모델을 표준적인 PyTorch나 TensorFlow 학습 과정에서 사용하는 방법, 또는 새로운 데이터로 fine-tune하기 위해 `Trainer` API를 사용하는 방법을 설명해줍니다.
## 왜 transformers를 사용해야 할까요?
1. 손쉽게 사용할 수 있는 최첨단 모델:
- NLU와 NLG 과제에서 뛰어난 성능을 보입니다.
- 교육자 실무자에게 진입 장벽이 낮습니다.
- 3개의 클래스만 배우면 바로 사용할 수 있습니다.
- 하나의 API로 모든 사전학습 모델을 사용할 수 있습니다.
1. 더 적은 계산 비용, 더 적은 탄소 발자국:
- 연구자들은 모델을 계속 다시 학습시키는 대신 학습된 모델을 공유할 수 있습니다.
- 실무자들은 학습에 필요한 시간과 비용을 절약할 수 있습니다.
- 수십개의 모델 구조, 2,000개 이상의 사전학습 모델, 100개 이상의 언어로 학습된 모델 등.
1. 모델의 각 생애주기에 적합한 프레임워크:
- 코드 3줄로 최첨단 모델을 학습하세요.
- 자유롭게 모델을 TF2.0나 PyTorch 프레임워크로 변환하세요.
- 학습, 평가, 공개 등 각 단계에 맞는 프레임워크를 원하는대로 선택하세요.
1. 필요한 대로 모델이나 예시를 커스터마이즈하세요:
- 우리는 저자가 공개한 결과를 재현하기 위해 각 모델 구조의 예시를 제공합니다.
- 모델 내부 구조는 가능한 일관적으로 공개되어 있습니다.
- 빠른 실험을 위해 모델 파일은 라이브러리와 독립적으로 사용될 수 있습니다.
## 왜 transformers를 사용하지 말아야 할까요?
- 이 라이브러리는 신경망 블록을 만들기 위한 모듈이 아닙니다. 연구자들이 여러 파일을 살펴보지 않고 바로 각 모델을 사용할 수 있도록, 모델 파일 코드의 추상화 수준을 적정하게 유지했습니다.
- 학습 API는 모든 모델에 적용할 수 있도록 만들어지진 않았지만, 라이브러리가 제공하는 모델들에 적용할 수 있도록 최적화되었습니다. 일반적인 머신 러닝을 위해선, 다른 라이브러리를 사용하세요.
- 가능한 많은 사용 예시를 보여드리고 싶어서, [예시 폴더](https://github.com/huggingface/transformers/tree/main/examples)의 스크립트를 준비했습니다. 이 스크립트들을 수정 없이 특정한 문제에 바로 적용하지 못할 수 있습니다. 필요에 맞게 일부 코드를 수정해야 할 수 있습니다.
## 설치
### pip로 설치하기
이 저장소는 Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, TensorFlow 2.6+에서 테스트 되었습니다.
[가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Transformers를 설치하세요. Python 가상 환경에 익숙하지 않다면, [사용자 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 확인하세요.
우선, 사용할 Python 버전으로 가상 환경을 만들고 실행하세요.
그 다음, Flax, PyTorch, TensorFlow 중 적어도 하나는 설치해야 합니다.
플랫폼에 맞는 설치 명령어를 확인하기 위해 [TensorFlow 설치 페이지](https://www.tensorflow.org/install/), [PyTorch 설치 페이지](https://pytorch.org/get-started/locally/#start-locally), [Flax 설치 페이지](https://github.com/google/flax#quick-install)를 확인하세요.
이들 중 적어도 하나가 설치되었다면, 🤗 Transformers는 다음과 같이 pip을 이용해 설치할 수 있습니다:
```bash
pip install transformers
```
예시들을 체험해보고 싶거나, 최최최첨단 코드를 원하거나, 새로운 버전이 나올 때까지 기다릴 수 없다면 [라이브러리를 소스에서 바로 설치](https://huggingface.co/docs/transformers/installation#installing-from-source)하셔야 합니다.
### conda로 설치하기
🤗 Transformers는 다음과 같이 conda로 설치할 수 있습니다:
```shell script
conda install conda-forge::transformers
```
> **_노트:_** `huggingface` 채널에서 `transformers`를 설치하는 것은 사용이 중단되었습니다.
Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는 방법을 확인하세요.
## 모델 구조
**🤗 Transformers가 제공하는 [모든 모델 체크포인트](https://huggingface.co/models)** 는 huggingface.co [모델 허브](https://huggingface.co)에 완벽히 연동되어 있습니다. [개인](https://huggingface.co/users)과 [기관](https://huggingface.co/organizations)이 모델 허브에 직접 업로드할 수 있습니다.
현재 사용 가능한 모델 체크포인트의 개수: 
🤗 Transformers는 다음 모델들을 제공합니다: 각 모델의 요약은 [여기](https://huggingface.co/docs/transformers/model_summary)서 확인하세요.
각 모델이 Flax, PyTorch, TensorFlow으로 구현되었는지 또는 🤗 Tokenizers 라이브러리가 지원하는 토크나이저를 사용하는지 확인하려면, [이 표](https://huggingface.co/docs/transformers/index#supported-frameworks)를 확인하세요.
이 구현은 여러 데이터로 검증되었고 (예시 스크립트를 참고하세요) 오리지널 구현의 성능과 같아야 합니다. [도큐먼트](https://huggingface.co/docs/transformers/examples)의 Examples 섹션에서 성능에 대한 자세한 설명을 확인할 수 있습니다.
## 더 알아보기
| 섹션 | 설명 |
|-|-|
| [도큐먼트](https://huggingface.co/transformers/) | 전체 API 도큐먼트와 튜토리얼 |
| [과제 요약](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers가 지원하는 과제들 |
| [전처리 튜토리얼](https://huggingface.co/docs/transformers/preprocessing) | `Tokenizer` 클래스를 이용해 모델을 위한 데이터 준비하기 |
| [학습과 fine-tuning](https://huggingface.co/docs/transformers/training) | 🤗 Transformers가 제공하는 모델 PyTorch/TensorFlow 학습 과정과 `Trainer` API에서 사용하기 |
| [퀵 투어: Fine-tuning/사용 스크립트](https://github.com/huggingface/transformers/tree/main/examples) | 다양한 과제에서 모델 fine-tuning하는 예시 스크립트 |
| [모델 공유 및 업로드](https://huggingface.co/docs/transformers/model_sharing) | 커뮤니티에 fine-tune된 모델을 업로드 및 공유하기 |
| [마이그레이션](https://huggingface.co/docs/transformers/migration) | `pytorch-transformers`나 `pytorch-pretrained-bert`에서 🤗 Transformers로 이동하기|
## 인용
🤗 Transformers 라이브러리를 인용하고 싶다면, 이 [논문](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)을 인용해 주세요:
```bibtex
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
```
| 0 |
mavonic_private_repos | mavonic_private_repos/transformers/CONTRIBUTING.md | <!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Contribute to 🤗 Transformers
Everyone is welcome to contribute, and we value everybody's contribution. Code
contributions are not the only way to help the community. Answering questions, helping
others, and improving the documentation are also immensely valuable.
It also helps us if you spread the word! Reference the library in blog posts
about the awesome projects it made possible, shout out on Twitter every time it has
helped you, or simply ⭐️ the repository to say thank you.
However you choose to contribute, please be mindful and respect our
[code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md).
**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
## Ways to contribute
There are several ways you can contribute to 🤗 Transformers:
* Fix outstanding issues with the existing code.
* Submit issues related to bugs or desired new features.
* Implement new models.
* Contribute to the examples or to the documentation.
If you don't know where to start, there is a special [Good First
Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of
open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over.
For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀
> All contributions are equally valuable to the community. 🥰
## Fixing outstanding issues
If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](#create-a-pull-request) and open a Pull Request!
## Submitting a bug-related issue or feature request
Do your best to follow these guidelines when submitting a bug-related issue or a feature
request. It will make it easier for us to come back to you quickly and with good
feedback.
### Did you find a bug?
The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter.
Before you report an issue, we would really appreciate it if you could **make sure the bug was not
already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the [forum](https://discuss.huggingface.co/) first. This helps us respond quicker to fixing issues related to the library versus general questions.
Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:
* Your **OS type and version** and **Python**, **PyTorch** and
**TensorFlow** versions when applicable.
* A short, self-contained, code snippet that allows us to reproduce the bug in
less than 30s.
* The *full* traceback if an exception is raised.
* Attach any other additional information, like screenshots, you think may help.
To get the OS and software versions automatically, run the following command:
```bash
transformers-cli env
```
You can also run the same command from the root of the repository:
```bash
python src/transformers/commands/transformers_cli.py env
```
### Do you want a new feature?
If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe:
1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community?
Whatever it is, we'd love to hear about it!
2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.
3. Provide a *code snippet* that demonstrates the features usage.
4. If the feature is related to a paper, please include a link.
If your issue is well written we're already 80% of the way there by the time you create it.
We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue.
## Do you want to implement a new model?
New models are constantly released and if you want to implement a new model, please provide the following information:
* A short description of the model and a link to the paper.
* Link to the implementation if it is open-sourced.
* Link to the model weights if they are available.
If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers!
We have a technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model).
## Do you want to add documentation?
We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested!
For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs).
## Create a Pull Request
Before writing any code, we strongly advise you to search through the existing PRs or
issues to make sure nobody is already working on the same thing. If you are
unsure, it is always a good idea to open an issue to get some feedback.
You will need basic `git` proficiency to contribute to
🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest
manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro
Git](https://git-scm.com/book/en/v2) is a very good reference.
You'll need **[Python 3.8](https://github.com/huggingface/transformers/blob/main/setup.py#L426)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing:
1. Fork the [repository](https://github.com/huggingface/transformers) by
clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code
under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
```bash
git clone git@github.com:<your Github handle>/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
```
3. Create a new branch to hold your development changes:
```bash
git checkout -b a-descriptive-name-for-my-changes
```
🚨 **Do not** work on the `main` branch!
4. Set up a development environment by running the following command in a virtual environment:
```bash
pip install -e ".[dev]"
```
If 🤗 Transformers was already installed in the virtual environment, remove
it with `pip uninstall transformers` before reinstalling it in editable
mode with the `-e` flag.
Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
(PyTorch, TensorFlow and/or Flax) then do:
```bash
pip install -e ".[quality]"
```
which should be enough for most use cases.
5. Develop the features in your branch.
As you work on your code, you should make sure the test suite
passes. Run the tests impacted by your changes like this:
```bash
pytest tests/<TEST_TO_RUN>.py
```
For more information about tests, check out the
[Testing](https://huggingface.co/docs/transformers/testing) guide.
🤗 Transformers relies on `black` and `ruff` to format its source code
consistently. After you make changes, apply automatic style corrections and code verifications
that can't be automated in one go with:
```bash
make fixup
```
This target is also optimized to only work with files modified by the PR you're working on.
If you prefer to run the checks one after the other, the following command applies the
style corrections:
```bash
make style
```
🤗 Transformers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality
controls are run by the CI, but you can run the same checks with:
```bash
make quality
```
Finally, we have a lot of scripts to make sure we don't forget to update
some files when adding a new model. You can run these scripts with:
```bash
make repo-consistency
```
To learn more about those checks and how to fix any issues with them, check out the
[Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide.
If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check
make sure you install the documentation builder:
```bash
pip install ".[docs]"
```
Run the following command from the root of the repository:
```bash
doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build
```
This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated
Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request.
Once you're happy with your changes, add the changed files with `git add` and
record your changes locally with `git commit`:
```bash
git add modified_file.py
git commit
```
Please remember to write [good commit
messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made!
To keep your copy of the code up to date with the original
repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer:
```bash
git fetch upstream
git rebase upstream/main
```
Push your changes to your branch:
```bash
git push -u origin a-descriptive-name-for-my-changes
```
If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally.
6. Now you can go to your fork of the repository on GitHub and click on **Pull Request** to open a pull request. Make sure you tick off all the boxes on our [checklist](#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review.
7. It's ok if maintainers request changes, it happens to our core contributors
too! So everyone can see the changes in the pull request, work in your local
branch and push the changes to your fork. They will automatically appear in
the pull request.
### Pull request checklist
☐ The pull request title should summarize your contribution.<br>
☐ If your pull request addresses an issue, please mention the issue number in the pull
request description to make sure they are linked (and people viewing the issue know you
are working on it).<br>
☐ To indicate a work in progress please prefix the title with `[WIP]`. These are
useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.<br>
☐ Make sure existing tests pass.<br>
☐ If adding a new feature, also add tests for it.<br>
- If you are adding a new model, make sure you use
`ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests.
- If you are adding new `@slow` tests, make sure they pass using
`RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`.
- If you are adding a new tokenizer, write tests and make sure
`RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes.
- CircleCI does not run the slow tests, but GitHub Actions does every night!<br>
☐ All public methods must have informative docstrings (see
[`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py)
for an example).<br>
☐ Due to the rapidly growing repository, don't add any images, videos and other
non-text files that'll significantly weigh down the repository. Instead, use a Hub
repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing)
to host these files and reference them by URL. We recommend placing documentation
related images in the following repository:
[huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
You can open a PR on this dataset repository and ask a Hugging Face member to merge it.
For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide.
### Tests
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the
[examples](https://github.com/huggingface/transformers/tree/main/examples) folder.
We like `pytest` and `pytest-xdist` because it's faster. From the root of the
repository, specify a *path to a subfolder or a test file* to run the test:
```bash
python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
```
Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory:
```bash
pip install -r examples/xxx/requirements.txt # only needed the first time
python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification
```
In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)!
You can also specify a smaller set of tests in order to test only the feature
you're working on.
By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to
`yes` to run them. This will download many gigabytes of models so make sure you
have enough disk space, a good internet connection or a lot of patience!
<Tip warning={true}>
Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time!
</Tip>
```bash
RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification
```
Like the slow tests, there are other environment variables available which not enabled by default during testing:
- `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers.
- `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration.
- `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration.
More environment variables and additional information can be found in the [testing_utils.py](src/transformers/testing_utils.py).
🤗 Transformers uses `pytest` as a test runner only. It doesn't use any
`pytest`-specific features in the test suite itself.
This means `unittest` is fully supported. Here's how to run tests with
`unittest`:
```bash
python -m unittest discover -s tests -t . -v
python -m unittest discover -s examples -t examples -v
```
### Style guide
For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification)
for more information.
### Develop on Windows
On Windows (unless you're working in [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings:
```bash
git config core.autocrlf input
```
One way to run the `make` command on Windows is with MSYS2:
1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`.
2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu).
3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`.
4. Add `C:\msys64\usr\bin` to your PATH environment variable.
You can now use `make` from any terminal (PowerShell, cmd.exe, etc.)! 🎉
### Sync a forked repository with upstream main (the Hugging Face repository)
When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs.
1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.
2. If a PR is absolutely necessary, use the following steps after checking out your branch:
```bash
git checkout -b your-branch-for-syncing
git pull --squash --no-commit upstream main
git commit -m '<your message without GitHub references>'
git push --set-upstream origin your-branch-for-syncing
```
| 0 |
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mavonic_private_repos | mavonic_private_repos/transformers/CODE_OF_CONDUCT.md |
# Contributor Covenant Code of Conduct
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| 0 |
mavonic_private_repos | mavonic_private_repos/transformers/README_hd.md | <!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
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<!---
A useful guide for English-Hindi translation of Hugging Face documentation
- Add space around English words and numbers when they appear between Hindi characters. E.g., कुल मिलाकर 100 से अधिक भाषाएँ; ट्रांसफॉर्मर लाइब्रेरी का उपयोग करता है।
- वर्गाकार उद्धरणों का प्रयोग करें, जैसे, "उद्धरण"
Dictionary
Hugging Face: गले लगाओ चेहरा
token: शब्द (और मूल अंग्रेजी को कोष्ठक में चिह्नित करें）
tokenize: टोकननाइज़ करें (और मूल अंग्रेज़ी को चिह्नित करने के लिए कोष्ठक का उपयोग करें)
tokenizer: Tokenizer (मूल अंग्रेजी में कोष्ठक के साथ)
transformer: transformer
pipeline: समनुक्रम
API: API (अनुवाद के बिना)
inference: विचार
Trainer: प्रशिक्षक। कक्षा के नाम के रूप में प्रस्तुत किए जाने पर अनुवादित नहीं किया गया।
pretrained/pretrain: पूर्व प्रशिक्षण
finetune: फ़ाइन ट्यूनिंग
community: समुदाय
example: जब विशिष्ट गोदाम example कैटलॉग करते समय "केस केस" के रूप में अनुवादित
Python data structures (e.g., list, set, dict): मूल अंग्रेजी को चिह्नित करने के लिए सूचियों, सेटों, शब्दकोशों में अनुवाद करें और कोष्ठक का उपयोग करें
NLP/Natural Language Processing: द्वारा NLP अनुवाद के बिना प्रकट होते हैं Natural Language Processing प्रस्तुत किए जाने पर प्राकृतिक भाषा संसाधन में अनुवाद करें
checkpoint: जाँच बिंदु
-->
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/>
<br>
</p>
<p align="center">
<a href="https://circleci.com/gh/huggingface/transformers">
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
</a>
<a href="https://huggingface.co/docs/transformers/index">
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
</a>
<a href="https://github.com/huggingface/transformers/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
</a>
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
</p>
<h4 align="center">
<p>
<a href="https://github.com/huggingface/transformers/">English</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> |
<b>हिन्दी</b> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> |
</p>
</h4>
<h3 align="center">
<p>Jax, PyTorch और TensorFlow के लिए उन्नत मशीन लर्निंग</p>
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
</h3>
🤗 Transformers 100 से अधिक भाषाओं में पाठ वर्गीकरण, सूचना निष्कर्षण, प्रश्न उत्तर, सारांशीकरण, अनुवाद, पाठ निर्माण का समर्थन करने के लिए हजारों पूर्व-प्रशिक्षित मॉडल प्रदान करता है। इसका उद्देश्य सबसे उन्नत एनएलपी तकनीक को सभी के लिए सुलभ बनाना है।
🤗 Transformers त्वरित डाउनलोड और उपयोग के लिए एक एपीआई प्रदान करता है, जिससे आप किसी दिए गए पाठ पर एक पूर्व-प्रशिक्षित मॉडल ले सकते हैं, इसे अपने डेटासेट पर ठीक कर सकते हैं और इसे [मॉडल हब](https://huggingface.co/models) के माध्यम से समुदाय के साथ साझा कर सकते हैं। इसी समय, प्रत्येक परिभाषित पायथन मॉड्यूल पूरी तरह से स्वतंत्र है, जो संशोधन और तेजी से अनुसंधान प्रयोगों के लिए सुविधाजनक है।
🤗 Transformers तीन सबसे लोकप्रिय गहन शिक्षण पुस्तकालयों का समर्थन करता है： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — और इसके साथ निर्बाध रूप से एकीकृत होता है। आप अपने मॉडल को सीधे एक ढांचे के साथ प्रशिक्षित कर सकते हैं और दूसरे के साथ लोड और अनुमान लगा सकते हैं।
## ऑनलाइन डेमो
आप सबसे सीधे मॉडल पृष्ठ पर परीक्षण कर सकते हैं [model hub](https://huggingface.co/models) मॉडल पर। हम [निजी मॉडल होस्टिंग, मॉडल संस्करण, और अनुमान एपीआई](https://huggingface.co/pricing) भी प्रदान करते हैं।。
यहाँ कुछ उदाहरण हैं：
- [शब्द को भरने के लिए मास्क के रूप में BERT का प्रयोग करें](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [इलेक्ट्रा के साथ नामित इकाई पहचान](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [जीपीटी-2 के साथ टेक्स्ट जनरेशन](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
- [रॉबर्टा के साथ प्राकृतिक भाषा निष्कर्ष](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
- [बार्ट के साथ पाठ सारांश](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [डिस्टिलबर्ट के साथ प्रश्नोत्तर](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [अनुवाद के लिए T5 का प्रयोग करें](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
**[Write With Transformer](https://transformer.huggingface.co)**，हगिंग फेस टीम द्वारा बनाया गया, यह एक आधिकारिक पाठ पीढ़ी है demo。
## यदि आप हगिंग फेस टीम से बीस्पोक समर्थन की तलाश कर रहे हैं
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## जल्दी शुरू करें
हम त्वरित उपयोग के लिए मॉडल प्रदान करते हैं `pipeline` (पाइपलाइन) एपीआई। पाइपलाइन पूर्व-प्रशिक्षित मॉडल और संबंधित पाठ प्रीप्रोसेसिंग को एकत्रित करती है। सकारात्मक और नकारात्मक भावना को निर्धारित करने के लिए पाइपलाइनों का उपयोग करने का एक त्वरित उदाहरण यहां दिया गया है:
```python
>>> from transformers import pipeline
# भावना विश्लेषण पाइपलाइन का उपयोग करना
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
```
कोड की दूसरी पंक्ति पाइपलाइन द्वारा उपयोग किए गए पूर्व-प्रशिक्षित मॉडल को डाउनलोड और कैश करती है, जबकि कोड की तीसरी पंक्ति दिए गए पाठ पर मूल्यांकन करती है। यहां उत्तर 99 आत्मविश्वास के स्तर के साथ "सकारात्मक" है।
कई एनएलपी कार्यों में आउट ऑफ़ द बॉक्स पाइपलाइनों का पूर्व-प्रशिक्षण होता है। उदाहरण के लिए, हम किसी दिए गए पाठ से किसी प्रश्न का उत्तर आसानी से निकाल सकते हैं:
``` python
>>> from transformers import pipeline
# प्रश्नोत्तर पाइपलाइन का उपयोग करना
>>> question_answerer = pipeline('question-answering')
>>> question_answerer({
... 'question': 'What is the name of the repository ?',
... 'context': 'Pipeline has been included in the huggingface/transformers repository'
... })
{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
```
उत्तर देने के अलावा, पूर्व-प्रशिक्षित मॉडल संगत आत्मविश्वास स्कोर भी देता है, जहां उत्तर टोकनयुक्त पाठ में शुरू और समाप्त होता है। आप [इस ट्यूटोरियल](https://huggingface.co/docs/transformers/task_summary) से पाइपलाइन एपीआई द्वारा समर्थित कार्यों के बारे में अधिक जान सकते हैं।
अपने कार्य पर किसी भी पूर्व-प्रशिक्षित मॉडल को डाउनलोड करना और उसका उपयोग करना भी कोड की तीन पंक्तियों की तरह सरल है। यहाँ PyTorch संस्करण के लिए एक उदाहरण दिया गया है:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)
```
यहाँ समकक्ष है TensorFlow कोड:
```python
>>> from transformers import AutoTokenizer, TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)
```
टोकननाइज़र सभी पूर्व-प्रशिक्षित मॉडलों के लिए प्रीप्रोसेसिंग प्रदान करता है और इसे सीधे एक स्ट्रिंग (जैसे ऊपर दिए गए उदाहरण) या किसी सूची पर बुलाया जा सकता है। यह एक डिक्शनरी (तानाशाही) को आउटपुट करता है जिसे आप डाउनस्ट्रीम कोड में उपयोग कर सकते हैं या `**` अनपैकिंग एक्सप्रेशन के माध्यम से सीधे मॉडल को पास कर सकते हैं।
मॉडल स्वयं एक नियमित [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) या [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (आपके बैकएंड के आधार पर), जो हो सकता है सामान्य तरीके से उपयोग किया जाता है। [यह ट्यूटोरियल](https://huggingface.co/transformers/training.html) बताता है कि इस तरह के मॉडल को क्लासिक PyTorch या TensorFlow प्रशिक्षण लूप में कैसे एकीकृत किया जाए, या हमारे `ट्रेनर` एपीआई का उपयोग कैसे करें ताकि इसे जल्दी से फ़ाइन ट्यून किया जा सके।एक नया डेटासेट पे।
## ट्रांसफार्मर का उपयोग क्यों करें?
1. उपयोग में आसानी के लिए उन्नत मॉडल:
- एनएलयू और एनएलजी पर बेहतर प्रदर्शन
- प्रवेश के लिए कम बाधाओं के साथ शिक्षण और अभ्यास के अनुकूल
- उपयोगकर्ता-सामना करने वाले सार तत्व, केवल तीन वर्गों को जानने की जरूरत है
- सभी मॉडलों के लिए एकीकृत एपीआई
1. कम कम्प्यूटेशनल ओवरहेड और कम कार्बन उत्सर्जन:
- शोधकर्ता हर बार नए सिरे से प्रशिक्षण देने के बजाय प्रशिक्षित मॉडल साझा कर सकते हैं
- इंजीनियर गणना समय और उत्पादन ओवरहेड को कम कर सकते हैं
- दर्जनों मॉडल आर्किटेक्चर, 2,000 से अधिक पूर्व-प्रशिक्षित मॉडल, 100 से अधिक भाषाओं का समर्थन
1.मॉडल जीवनचक्र के हर हिस्से को शामिल करता है:
- कोड की केवल 3 पंक्तियों में उन्नत मॉडलों को प्रशिक्षित करें
- मॉडल को मनमाने ढंग से विभिन्न डीप लर्निंग फ्रेमवर्क के बीच स्थानांतरित किया जा सकता है, जैसा आप चाहते हैं
- निर्बाध रूप से प्रशिक्षण, मूल्यांकन और उत्पादन के लिए सबसे उपयुक्त ढांचा चुनें
1. आसानी से अनन्य मॉडल को अनुकूलित करें और अपनी आवश्यकताओं के लिए मामलों का उपयोग करें:
- हम मूल पेपर परिणामों को पुन: पेश करने के लिए प्रत्येक मॉडल आर्किटेक्चर के लिए कई उपयोग के मामले प्रदान करते हैं
- मॉडल की आंतरिक संरचना पारदर्शी और सुसंगत रहती है
- मॉडल फ़ाइल को अलग से इस्तेमाल किया जा सकता है, जो संशोधन और त्वरित प्रयोग के लिए सुविधाजनक है
## मुझे ट्रांसफॉर्मर का उपयोग कब नहीं करना चाहिए?
- यह लाइब्रेरी मॉड्यूलर न्यूरल नेटवर्क टूलबॉक्स नहीं है। मॉडल फ़ाइल में कोड जानबूझकर अल्पविकसित है, बिना अतिरिक्त सार इनकैप्सुलेशन के, ताकि शोधकर्ता अमूर्तता और फ़ाइल जंपिंग में शामिल हुए जल्दी से पुनरावृति कर सकें।
- `ट्रेनर` एपीआई किसी भी मॉडल के साथ संगत नहीं है, यह केवल इस पुस्तकालय के मॉडल के लिए अनुकूलित है। यदि आप सामान्य मशीन लर्निंग के लिए उपयुक्त प्रशिक्षण लूप कार्यान्वयन की तलाश में हैं, तो कहीं और देखें।
- हमारे सर्वोत्तम प्रयासों के बावजूद, [उदाहरण निर्देशिका](https://github.com/huggingface/transformers/tree/main/examples) में स्क्रिप्ट केवल उपयोग के मामले हैं। आपकी विशिष्ट समस्या के लिए, वे जरूरी नहीं कि बॉक्स से बाहर काम करें, और आपको कोड की कुछ पंक्तियों को सूट करने की आवश्यकता हो सकती है।
## स्थापित करना
### पिप का उपयोग करना
इस रिपॉजिटरी का परीक्षण Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ और TensorFlow 2.6+ के तहत किया गया है।
आप [वर्चुअल एनवायरनमेंट](https://docs.python.org/3/library/venv.html) में 🤗 ट्रांसफॉर्मर इंस्टॉल कर सकते हैं। यदि आप अभी तक पायथन के वर्चुअल एनवायरनमेंट से परिचित नहीं हैं, तो कृपया इसे [उपयोगकर्ता निर्देश](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) पढ़ें।
सबसे पहले, पायथन के उस संस्करण के साथ एक आभासी वातावरण बनाएं जिसका आप उपयोग करने और उसे सक्रिय करने की योजना बना रहे हैं।
फिर, आपको Flax, PyTorch या TensorFlow में से किसी एक को स्थापित करने की आवश्यकता है। अपने प्लेटफ़ॉर्म पर इन फ़्रेमवर्क को स्थापित करने के लिए, [TensorFlow स्थापना पृष्ठ](https://www.tensorflow.org/install/), [PyTorch स्थापना पृष्ठ](https://pytorch.org/get-started/locally)
देखें start-locally या [Flax स्थापना पृष्ठ](https://github.com/google/flax#quick-install).
जब इनमें से कोई एक बैकएंड सफलतापूर्वक स्थापित हो जाता है, तो ट्रांसफॉर्मर निम्नानुसार स्थापित किए जा सकते हैं:
```bash
pip install transformers
```
यदि आप उपयोग के मामलों को आज़माना चाहते हैं या आधिकारिक रिलीज़ से पहले नवीनतम इन-डेवलपमेंट कोड का उपयोग करना चाहते हैं, तो आपको [सोर्स से इंस्टॉल करना होगा](https://huggingface.co/docs/transformers/installation#installing-from-) स्रोत।
### कोंडा का उपयोग करना
ट्रांसफॉर्मर कोंडा के माध्यम से निम्नानुसार स्थापित किया जा सकता है:
```shell script
conda install conda-forge::transformers
```
> **_नोट:_** `huggingface` चैनल से `transformers` इंस्टॉल करना पुराना पड़ चुका है।
कोंडा के माध्यम से Flax, PyTorch, या TensorFlow में से किसी एक को स्थापित करने के लिए, निर्देशों के लिए उनके संबंधित स्थापना पृष्ठ देखें।
## मॉडल आर्किटेक्चर
[उपयोगकर्ता](https://huggingface.co/users) और [organization](https://huggingface.co) द्वारा ट्रांसफॉर्मर समर्थित [**सभी मॉडल चौकियों**](https://huggingface.co/models/users) हगिंगफेस.को/ऑर्गनाइजेशन), सभी को बिना किसी बाधा के हगिंगफेस.को [मॉडल हब](https://huggingface.co) के साथ एकीकृत किया गया है।
चौकियों की वर्तमान संख्या: 
🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं: मॉडल के अवलोकन के लिए [यहां देखें](https://huggingface.co/docs/transformers/model_summary)：
यह जांचने के लिए कि क्या किसी मॉडल में पहले से ही Flax, PyTorch या TensorFlow का कार्यान्वयन है, या यदि उसके पास Tokenizers लाइब्रेरी में संबंधित टोकन है, तो [यह तालिका](https://huggingface.co/docs/transformers/index#supported) देखें। -फ्रेमवर्क)।
इन कार्यान्वयनों का परीक्षण कई डेटासेट पर किया गया है (देखें केस स्क्रिप्ट का उपयोग करें) और वैनिला कार्यान्वयन के लिए तुलनात्मक रूप से प्रदर्शन करना चाहिए। आप उपयोग के मामले के दस्तावेज़ [इस अनुभाग](https://huggingface.co/docs/transformers/examples) में व्यवहार का विवरण पढ़ सकते हैं।
## अधिक समझें
|अध्याय | विवरण |
|-|-|
| [दस्तावेज़ीकरण](https://huggingface.co/transformers/) | पूरा एपीआई दस्तावेज़ीकरण और ट्यूटोरियल |
| [कार्य सारांश](https://huggingface.co/docs/transformers/task_summary) | ट्रांसफॉर्मर समर्थित कार्य |
| [प्रीप्रोसेसिंग ट्यूटोरियल](https://huggingface.co/docs/transformers/preprocessing) | मॉडल के लिए डेटा तैयार करने के लिए `टोकनाइज़र` का उपयोग करना |
| [प्रशिक्षण और फाइन-ट्यूनिंग](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow के ट्रेनिंग लूप या `ट्रेनर` API में ट्रांसफॉर्मर द्वारा दिए गए मॉडल का उपयोग करें |
| [क्विक स्टार्ट: ट्वीकिंग एंड यूज़ केस स्क्रिप्ट्स](https://github.com/huggingface/transformers/tree/main/examples) | विभिन्न कार्यों के लिए केस स्क्रिप्ट का उपयोग करें |
| [मॉडल साझा करना और अपलोड करना](https://huggingface.co/docs/transformers/model_sharing) | समुदाय के साथ अपने फाइन टूनड मॉडल अपलोड और साझा करें |
| [माइग्रेशन](https://huggingface.co/docs/transformers/migration) | `पाइटोरच-ट्रांसफॉर्मर्स` या `पाइटोरच-प्रीट्रेनड-बर्ट` से ट्रांसफॉर्मर में माइग्रेट करना |
## उद्धरण
हमने आधिकारिक तौर पर इस लाइब्रेरी का [पेपर](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) प्रकाशित किया है, अगर आप ट्रान्सफ़ॉर्मर्स लाइब्रेरी का उपयोग करते हैं, तो कृपया उद्धृत करें:
```bibtex
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
```
| 0 |
mavonic_private_repos | mavonic_private_repos/transformers/awesome-transformers.md | # Awesome projects built with Transformers
This page lists awesome projects built on top of Transformers. Transformers is more than a toolkit to use pretrained
models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable
developers, researchers, students, professors, engineers, and anyone else to build their dream projects.
In this list, we showcase incredibly impactful and novel projects that have pushed the field forward. We celebrate
100 of these projects as we reach the milestone of 100k stars as a community; but we're very open to pull requests
adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR
to add it.
## [gpt4all](https://github.com/nomic-ai/gpt4all)
[gpt4all](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on massive collections of clean assistant data including code, stories and dialogue. It offers open-source, large language models such as LLaMA and GPT-J trained in an assistant-style.
Keywords: Open-source, LLaMa, GPT-J, instruction, assistant
## [recommenders](https://github.com/microsoft/recommenders)
This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization
Keywords: Recommender systems, AzureML
## [IOPaint](https://github.com/Sanster/IOPaint)
Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures.
Keywords: inpainting, SD, Stable Diffusion
## [flair](https://github.com/flairNLP/flair)
FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis
## [mindsdb](https://github.com/mindsdb/mindsdb)
MindsDB is a low-code ML platform, which automates and integrates several ML frameworks into the data stack as "AI Tables" to streamline the integration of AI into applications, making it accessible to developers of all skill levels.
Keywords: Database, low-code, AI table
## [langchain](https://github.com/hwchase17/langchain)
[langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
Keywords: LLMs, Large Language Models, Agents, Chains
## [LlamaIndex](https://github.com/jerryjliu/llama_index)
[LlamaIndex](https://github.com/jerryjliu/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation
## [ParlAI](https://github.com/facebookresearch/ParlAI)
[ParlAI](https://github.com/facebookresearch/ParlAI) is a python framework for sharing, training and testing dialogue models, from open-domain chitchat, to task-oriented dialogue, to visual question answering. It provides more than 100 datasets under the same API, a large zoo of pretrained models, a set of agents, and has several integrations.
Keywords: Dialogue, Chatbots, VQA, Datasets, Agents
## [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity.
Keywords: Dense vector representations, Text embeddings, Sentence embeddings
## [ludwig](https://github.com/ludwig-ai/ludwig)
Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is targeted at a wide variety of AI tasks. It provides a data-driven configuration system, training, prediction, and evaluation scripts, as well as a programmatic API.
Keywords: Declarative, Data-driven, ML Framework
## [InvokeAI](https://github.com/invoke-ai/InvokeAI)
[InvokeAI](https://github.com/invoke-ai/InvokeAI) is an engine for Stable Diffusion models, aimed at professionals, artists, and enthusiasts. It leverages the latest AI-driven technologies through CLI as well as a WebUI.
Keywords: Stable-Diffusion, WebUI, CLI
## [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP)
[PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) is an easy-to-use and powerful NLP library particularly targeted at the Chinese languages. It has support for multiple pre-trained model zoos, and supports a wide-range of NLP tasks from research to industrial applications.
Keywords: NLP, Chinese, Research, Industry
## [stanza](https://github.com/stanfordnlp/stanza)
The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python.
Keywords: NLP, Multilingual, CoreNLP
## [DeepPavlov](https://github.com/deeppavlov/DeepPavlov)
[DeepPavlov](https://github.com/deeppavlov/DeepPavlov) is an open-source conversational AI library. It is designed for the development of production ready chat-bots and complex conversational systems, as well as research in the area of NLP and, particularly, of dialog systems.
Keywords: Conversational, Chatbot, Dialog
## [alpaca-lora](https://github.com/tloen/alpaca-lora)
Alpaca-lora contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). The repository provides training (fine-tuning) as well as generation scripts.
Keywords: LoRA, Parameter-efficient fine-tuning
## [imagen-pytorch](https://github.com/lucidrains/imagen-pytorch)
An open-source Implementation of Imagen, Google's closed-source Text-to-Image Neural Network that beats DALL-E2. As of release, it is the new SOTA for text-to-image synthesis.
Keywords: Imagen, Text-to-image
## [adapters](https://github.com/adapter-hub/adapters)
[adapters](https://github.com/adapter-hub/adapters) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers.
Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub
## [NeMo](https://github.com/NVIDIA/NeMo)
NVIDIA [NeMo](https://github.com/NVIDIA/NeMo) is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), text-to-speech synthesis (TTS), large language models (LLMs), and natural language processing (NLP). The primary objective of [NeMo](https://github.com/NVIDIA/NeMo) is to help researchers from industry and academia to reuse prior work (code and pretrained models) and make it easier to create new https://developer.nvidia.com/conversational-ai#started.
Keywords: Conversational, ASR, TTS, LLMs, NLP
## [Runhouse](https://github.com/run-house/runhouse)
[Runhouse](https://github.com/run-house/runhouse) allows to send code and data to any of your compute or data infra, all in Python, and continue to interact with them normally from your existing code and environment. Runhouse developers mention:
> Think of it as an expansion pack to your Python interpreter that lets it take detours to remote machines or manipulate remote data.
Keywords: MLOps, Infrastructure, Data storage, Modeling
## [MONAI](https://github.com/Project-MONAI/MONAI)
[MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are:
- developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
- providing researchers with the optimized and standardized way to create and evaluate deep learning models.
Keywords: Healthcare imaging, Training, Evaluation
## [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers)
Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize, train, and evaluate a model. It supports a wide variety of NLP tasks.
Keywords: Framework, simplicity, NLP
## [JARVIS](https://github.com/microsoft/JARVIS)
[JARVIS](https://github.com/microsoft/JARVIS) is a system attempting to merge LLMs such as GPT-4 with the rest of the open-source ML community: leveraging up to 60 downstream models in order to perform tasks identified by the LLM.
Keywords: LLM, Agents, HF Hub
## [transformers.js](https://xenova.github.io/transformers.js/)
[transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
Keywords: Transformers, JavaScript, browser
## [bumblebee](https://github.com/elixir-nx/bumblebee)
Bumblebee provides pre-trained Neural Network models on top of Axon, a neural networks library for the Elixir language. It includes integration with 🤗 Models, allowing anyone to download and perform Machine Learning tasks with few lines of code.
Keywords: Elixir, Axon
## [argilla](https://github.com/argilla-io/argilla)
Argilla is an open-source platform providing advanced NLP labeling, monitoring, and workspaces. It is compatible with many open source ecosystems such as Hugging Face, Stanza, FLAIR, and others.
Keywords: NLP, Labeling, Monitoring, Workspaces
## [haystack](https://github.com/deepset-ai/haystack)
Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs. It offers production-ready tools to quickly build complex decision making, question answering, semantic search, text generation applications, and more.
Keywords: NLP, Framework, LLM
## [spaCy](https://github.com/explosion/spaCy)
[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It offers support for transformers models through its third party package, spacy-transformers.
Keywords: NLP, Framework
## [speechbrain](https://github.com/speechbrain/speechbrain)
SpeechBrain is an open-source and all-in-one conversational AI toolkit based on PyTorch.
The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, speech separation, language identification, multi-microphone signal processing, and many others.
Keywords: Conversational, Speech
## [skorch](https://github.com/skorch-dev/skorch)
Skorch is a scikit-learn compatible neural network library that wraps PyTorch. It has support for models within transformers, and tokenizers from tokenizers.
Keywords: Scikit-Learn, PyTorch
## [bertviz](https://github.com/jessevig/bertviz)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models.
Keywords: Visualization, Transformers
## [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax)
[mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) is a haiku library using the xmap/pjit operators in JAX for model parallelism of transformers. This library is designed for scalability up to approximately 40B parameters on TPUv3s. It was the library used to train the GPT-J model.
Keywords: Haiku, Model parallelism, LLM, TPU
## [deepchem](https://github.com/deepchem/deepchem)
DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology.
Keywords: Drug discovery, Materials Science, Quantum Chemistry, Biology
## [OpenNRE](https://github.com/thunlp/OpenNRE)
An Open-Source Package for Neural Relation Extraction (NRE). It is targeted at a wide range of users, from newcomers to relation extraction, to developers, researchers, or students.
Keywords: Neural Relation Extraction, Framework
## [pycorrector](https://github.com/shibing624/pycorrector)
PyCorrector is a Chinese Text Error Correction Tool. It uses a language model to detect errors, pinyin feature and shape feature to correct Chinese text errors. it can be used for Chinese Pinyin and stroke input method.
Keywords: Chinese, Error correction tool, Language model, Pinyin
## [nlpaug](https://github.com/makcedward/nlpaug)
This python library helps you with augmenting nlp for machine learning projects. It is a lightweight library featuring synthetic data generation for improving model performance, support for audio and text, and compatibility with several ecosystems (scikit-learn, pytorch, tensorflow).
Keywords: Data augmentation, Synthetic data generation, Audio, NLP
## [dream-textures](https://github.com/carson-katri/dream-textures)
[dream-textures](https://github.com/carson-katri/dream-textures) is a library targeted at bringing stable-diffusion support within Blender. It supports several use-cases, such as image generation, texture projection, inpainting/outpainting, ControlNet, and upscaling.
Keywords: Stable-Diffusion, Blender
## [seldon-core](https://github.com/SeldonIO/seldon-core)
Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.
Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more.
Keywords: Microservices, Modeling, Language wrappers
## [open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo)
This repository includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Use these free pre-trained models instead of training your own models to speed-up the development and production deployment process.
Keywords: Optimized models, Demos
## [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion)
ML-Stable-Diffusion is a repository by Apple bringing Stable Diffusion support to Core ML, on Apple Silicon devices. It supports stable diffusion checkpoints hosted on the Hugging Face Hub.
Keywords: Stable Diffusion, Apple Silicon, Core ML
## [stable-dreamfusion](https://github.com/ashawkey/stable-dreamfusion)
Stable-Dreamfusion is a pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model.
Keywords: Text-to-3D, Stable Diffusion
## [txtai](https://github.com/neuml/txtai)
[txtai](https://github.com/neuml/txtai) is an open-source platform for semantic search and workflows powered by language models. txtai builds embeddings databases, which are a union of vector indexes and relational databases enabling similarity search with SQL. Semantic workflows connect language models together into unified applications.
Keywords: Semantic search, LLM
## [djl](https://github.com/deepjavalibrary/djl)
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for developers. DJL provides a native Java development experience and functions like any other regular Java library. DJL offers [a Java binding](https://github.com/deepjavalibrary/djl/tree/master/extensions/tokenizers) for HuggingFace Tokenizers and easy conversion toolkit for HuggingFace model to deploy in Java.
Keywords: Java, Framework
## [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/)
This project provides a unified framework to test generative language models on a large number of different evaluation tasks. It has support for more than 200 tasks, and supports different ecosystems: HF Transformers, GPT-NeoX, DeepSpeed, as well as the OpenAI API.
Keywords: LLM, Evaluation, Few-shot
## [gpt-neox](https://github.com/EleutherAI/gpt-neox)
This repository records EleutherAI's library for training large-scale language models on GPUs. The framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. It is focused on training multi-billion-parameter models.
Keywords: Training, LLM, Megatron, DeepSpeed
## [muzic](https://github.com/microsoft/muzic)
Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence. Muzic was created by researchers from Microsoft Research Asia.
Keywords: Music understanding, Music generation
## [dalle-flow](https://github.com/jina-ai/dalle-flow)
DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt.
The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR.
Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR
## [lightseq](https://github.com/bytedance/lightseq)
LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP and CV models such as BERT, GPT, Transformer, etc. It is therefore best useful for machine translation, text generation, image classification, and other sequence related tasks.
Keywords: Training, Inference, Sequence Processing, Sequence Generation
## [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)
The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code.
Keywords: OCR, LaTeX, Math formula
## [open_clip](https://github.com/mlfoundations/open_clip)
OpenCLIP is an open source implementation of OpenAI's CLIP.
The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift.
The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset.
Specifically, a ResNet-50 model trained with this codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet.
Keywords: CLIP, Open-source, Contrastive, Image-text
## [dalle-playground](https://github.com/saharmor/dalle-playground)
A playground to generate images from any text prompt using Stable Diffusion and Dall-E mini.
Keywords: WebUI, Stable Diffusion, Dall-E mini
## [FedML](https://github.com/FedML-AI/FedML)
[FedML](https://github.com/FedML-AI/FedML) is a federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale.
It supports large-scale cross-silo federated learning, and cross-device federated learning on smartphones/IoTs, and research simulation.
Keywords: Federated Learning, Analytics, Collaborative ML, Decentralized
## [gpt-code-clippy](https://github.com/CodedotAl/gpt-code-clippy)
GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.
Keywords: LLM, Code
## [TextAttack](https://github.com/QData/TextAttack)
[TextAttack](https://github.com/QData/TextAttack) 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
Keywords: Adversarial attacks, Data augmentation, NLP
## [OpenPrompt](https://github.com/thunlp/OpenPrompt)
Prompt-learning is a paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modify the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. This library provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. [OpenPrompt](https://github.com/thunlp/OpenPrompt) supports loading PLMs directly from https://github.com/huggingface/transformers.
## [text-generation-webui](https://github.com/oobabooga/text-generation-webui/)
[text-generation-webui](https://github.com/oobabooga/text-generation-webui/) is a Gradio Web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
Keywords: LLM, WebUI
## [libra](https://github.com/Palashio/libra)
An ergonomic machine learning [libra](https://github.com/Palashio/libra)ry for non-technical users. It focuses on ergonomics and on ensuring that training a model is as simple as it can be.
Keywords: Ergonomic, Non-technical
## [alibi](https://github.com/SeldonIO/alibi)
Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.
Keywords: Model inspection, Model interpretation, Black-box, White-box
## [tortoise-tts](https://github.com/neonbjb/tortoise-tts)
Tortoise is a text-to-speech program built with the following priorities: strong multi-voice capabilities, and highly realistic prosody and intonation.
Keywords: Text-to-speech
## [flower](https://github.com/adap/flower)
Flower (flwr) is a framework for building federated learning systems. The design of Flower is based on a few guiding principles: customizability, extendability, framework agnosticity, and ease-of-use.
Keywords: Federated learning systems, Customizable, Extendable, Framework-agnostic, Simplicity
## [fast-bert](https://github.com/utterworks/fast-bert)
Fast-Bert is a deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification. It is aimed at simplicity.
Keywords: Deployment, BERT, XLNet
## [towhee](https://github.com/towhee-io/towhee)
Towhee makes it easy to build neural data processing pipelines for AI applications. We provide hundreds of models, algorithms, and transformations that can be used as standard pipeline building blocks. Users can use Towhee's Pythonic API to build a prototype of their pipeline and automatically optimize it for production-ready environments.
Keywords: Data processing pipeline, Optimization
## [alibi-detect](https://github.com/SeldonIO/alibi-detect)
Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection.
Keywords: Adversarial, Outlier, Drift detection
## [FARM](https://github.com/deepset-ai/FARM)
[FARM](https://github.com/deepset-ai/FARM) makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker.
Keywords: Transfer Learning, Modular design, Multi-task learning, Experiment tracking
## [aitextgen](https://github.com/minimaxir/aitextgen)
A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 and EleutherAI's GPT Neo/GPT-3 architecture.
[aitextgen](https://github.com/minimaxir/aitextgen) is a Python package that leverages PyTorch, Hugging Face Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features.
Keywords: Training, Generation
## [diffgram](https://github.com/diffgram/diffgram)
Diffgram aims to integrate human supervision into platforms. We support your team programmatically changing the UI (Schema, layout, etc.) like in Streamlit. This means that you can collect and annotate timely data from users. In other words, we are the platform behind your platform, an integrated part of your application, to ship new & better AI products faster.
Keywords: Human supervision, Platform
## [ecco](https://github.com/jalammar/ecco)
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
Keywords: Model explainability
## [s3prl](https://github.com/s3prl/s3prl)
[s3prl](https://github.com/s3prl/s3prl) stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit, and are utilized in various downstream tasks.
Keywords: Speech, Training
## [ru-dalle](https://github.com/ai-forever/ru-dalle)
RuDALL-E aims to be similar to DALL-E, targeted to Russian.
Keywords: DALL-E, Russian
## [DeepKE](https://github.com/zjunlp/DeepKE)
[DeepKE](https://github.com/zjunlp/DeepKE) is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema，low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction.
Keywords: Knowledge Extraction, Knowledge Graphs
## [Nebuly](https://github.com/nebuly-ai/nebuly)
Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances.
Keywords: Optimization, Performance, Monitoring
## [imaginAIry](https://github.com/brycedrennan/imaginAIry)
Offers a CLI and a Python API to generate images with Stable Diffusion. It has support for many tools, like image structure control (controlnet), instruction-based image edits (InstructPix2Pix), prompt-based masking (clipseg), among others.
Keywords: Stable Diffusion, CLI, Python API
## [sparseml](https://github.com/neuralmagic/sparseml)
SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware.
Keywords: Model optimization, Pruning, Quantization, Distillation
## [opacus](https://github.com/pytorch/opacus)
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment.
Keywords: Differential privacy
## [LAVIS](https://github.com/salesforce/LAVIS)
[LAVIS](https://github.com/salesforce/LAVIS) is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. It features a unified interface design to access
Keywords: Multimodal, NLP, Vision
## [buzz](https://github.com/chidiwilliams/buzz)
Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper.
Keywords: Audio transcription, Translation
## [rust-bert](https://github.com/guillaume-be/rust-bert)
Rust-native state-of-the-art Natural Language Processing models and pipelines. Port of Hugging Face's Transformers library, using the tch-rs crate and pre-processing from rust-tokenizers. Supports multi-threaded tokenization and GPU inference. This repository exposes the model base architecture, task-specific heads and ready-to-use pipelines.
Keywords: Rust, BERT, Inference
## [EasyNLP](https://github.com/alibaba/EasyNLP)
[EasyNLP](https://github.com/alibaba/EasyNLP) is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. [EasyNLP](https://github.com/alibaba/EasyNLP) integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications.
Keywords: NLP, Knowledge distillation, Few-shot learning, Multi-modality, Training, Inference, Deployment
## [TurboTransformers](https://github.com/Tencent/TurboTransformers)
A fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU.
Keywords: Optimization, Performance
## [hivemind](https://github.com/learning-at-home/hivemind)
Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers.
Keywords: Decentralized training
## [docquery](https://github.com/impira/docquery)
DocQuery is a library and command-line tool that makes it easy to analyze semi-structured and unstructured documents (PDFs, scanned images, etc.) using large language models (LLMs). You simply point DocQuery at one or more documents and specify a question you want to ask. DocQuery is created by the team at Impira.
Keywords: Semi-structured documents, Unstructured documents, LLM, Document Question Answering
## [CodeGeeX](https://github.com/THUDM/CodeGeeX)
[CodeGeeX](https://github.com/THUDM/CodeGeeX) is a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. It has several unique features:
- Multilingual code generation
- Crosslingual code translation
- Is a customizable programming assistant
Keywords: Code Generation Model
## [ktrain](https://github.com/amaiya/ktrain)
[ktrain](https://github.com/amaiya/ktrain) is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, [ktrain](https://github.com/amaiya/ktrain) is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners.
Keywords: Keras wrapper, Model building, Training, Deployment
## [FastDeploy](https://github.com/PaddlePaddle/FastDeploy)
[FastDeploy](https://github.com/PaddlePaddle/FastDeploy) is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with packageout-of-the-box and unified experience, endend-to-end optimization for over fire160+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, OCR, face detection, matting, pp-tracking, NLP, stable diffusion, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform.
Keywords: Model deployment, CLoud, Mobile, Edge
## [underthesea](https://github.com/undertheseanlp/underthesea)
[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
Keywords: Vietnamese, NLP
## [hasktorch](https://github.com/hasktorch/hasktorch)
Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch.
Keywords: Haskell, Neural Networks
## [donut](https://github.com/clovaai/donut)
Donut, or Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model.
Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing).
Keywords: Document Understanding
## [transformers-interpret](https://github.com/cdpierse/transformers-interpret)
Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package.
In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files
Keywords: Model interpretation, Visualization
## [mlrun](https://github.com/mlrun/mlrun)
MLRun is an open MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. With MLRun, you can choose any IDE on your local machine or on the cloud. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements.
Keywords: MLOps
## [FederatedScope](https://github.com/alibaba/FederatedScope)
[FederatedScope](https://github.com/alibaba/FederatedScope) is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry. Based on an event-driven architecture, [FederatedScope](https://github.com/alibaba/FederatedScope) integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively.
Keywords: Federated learning, Event-driven
## [pythainlp](https://github.com/PyThaiNLP/pythainlp)
PyThaiNLP is a Python package for text processing and linguistic analysis, similar to NLTK with focus on Thai language.
Keywords: Thai, NLP, NLTK
## [FlagAI](https://github.com/FlagAI-Open/FlagAI)
[FlagAI](https://github.com/FlagAI-Open/FlagAI) (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality.
Keywords: Large models, Training, Fine-tuning, Deployment, Multi-modal
## [pyserini](https://github.com/castorini/pyserini)
[pyserini](https://github.com/castorini/pyserini) is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with the group's Anserini IR toolkit. Retrieval using dense representations is provided via integration with Facebook's Faiss library.
Keywords: IR, Information Retrieval, Dense, Sparse
## [baal](https://github.com/baal-org/baal)
[baal](https://github.com/baal-org/baal) is an active learning library that supports both industrial applications and research usecases. [baal](https://github.com/baal-org/baal) currently supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning.
Keywords: Active Learning, Research, Labeling
## [cleanlab](https://github.com/cleanlab/cleanlab)
[cleanlab](https://github.com/cleanlab/cleanlab) is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels. For text, image, tabular, audio (among others) datasets, you can use cleanlab to automatically: detect data issues (outliers, label errors, near duplicates, etc), train robust ML models, infer consensus + annotator-quality for multi-annotator data, suggest data to (re)label next (active learning).
Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active Learning
## [BentoML](https://github.com/bentoml/BentoML)
[BentoML](https://github.com/bentoml) is the unified framework for for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models.
All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage.
Keywords: BentoML, Framework, Deployment, AI Applications
## [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory)
[LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning).
Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen
| 0 |
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Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg">
<img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;">
</picture>
<br/>
<br/>
</p>
<p align="center">
<a href="https://circleci.com/gh/huggingface/transformers">
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
</a>
<a href="https://huggingface.co/docs/transformers/index">
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
</a>
<a href="https://github.com/huggingface/transformers/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
</a>
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
</p>
<h4 align="center">
<p>
<a href="https://github.com/huggingface/transformers/">English</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> |
<b>Рortuguês</b> |
<a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> |
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<a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> |
</p>
</h4>
<h3 align="center">
<p>Aprendizado de máquina de última geração para JAX, PyTorch e TensorFlow</p>
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
</h3>
A biblioteca 🤗 Transformers oferece milhares de modelos pré-treinados para executar tarefas em diferentes modalidades, como texto, visão e áudio.
Esses modelos podem ser aplicados a:
* 📝 Texto, para tarefas como classificação de texto, extração de informações, resposta a perguntas, sumarização, tradução, geração de texto, em mais de 100 idiomas.
* 🖼️ Imagens, para tarefas como classificação de imagens, detecção de objetos e segmentação.
* 🗣️ Áudio, para tarefas como reconhecimento de fala e classificação de áudio.
Os modelos Transformer também podem executar tarefas em diversas modalidades combinadas, como responder a perguntas em tabelas, reconhecimento óptico de caracteres, extração de informações de documentos digitalizados, classificação de vídeo e resposta a perguntas visuais.
A biblioteca 🤗 Transformers oferece APIs para baixar e usar rapidamente esses modelos pré-treinados em um texto específico, ajustá-los em seus próprios conjuntos de dados e, em seguida, compartilhá-los com a comunidade em nosso [model hub](https://huggingface.co/models). Ao mesmo tempo, cada módulo Python que define uma arquitetura é totalmente independente e pode ser modificado para permitir experimentos de pesquisa rápidos.
A biblioteca 🤗 Transformers é respaldada pelas três bibliotecas de aprendizado profundo mais populares — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) e [TensorFlow](https://www.tensorflow.org/) — com uma integração perfeita entre elas. É simples treinar seus modelos com uma delas antes de carregá-los para inferência com a outra
## Demonstração Online
Você pode testar a maioria de nossos modelos diretamente em suas páginas a partir do [model hub](https://huggingface.co/models). Também oferecemos [hospedagem de modelos privados, versionamento e uma API de inferência](https://huggingface.co/pricing)
para modelos públicos e privados.
Aqui estão alguns exemplos:
Em Processamento de Linguagem Natural:
- [Completar palavra mascarada com BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [Reconhecimento de Entidades Nomeadas com Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [Geração de texto com GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C)
- [Inferência de Linguagem Natural com RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
- [Sumarização com BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [Resposta a perguntas com DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [Tradução com T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
Em Visão Computacional:
- [Classificação de Imagens com ViT](https://huggingface.co/google/vit-base-patch16-224)
- [Detecção de Objetos com DETR](https://huggingface.co/facebook/detr-resnet-50)
- [Segmentação Semântica com SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
- [Segmentação Panóptica com MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco)
- [Estimativa de Profundidade com DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
- [Classificação de Vídeo com VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
- [Segmentação Universal com OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
Em Áudio:
- [Reconhecimento Automático de Fala com Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
- [Detecção de Palavras-Chave com Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
- [Classificação de Áudio com Transformer de Espectrograma de Áudio](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
Em Tarefas Multimodais:
- [Respostas de Perguntas em Tabelas com TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
- [Respostas de Perguntas Visuais com ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
- [Classificação de Imagens sem Anotação com CLIP](https://huggingface.co/openai/clip-vit-large-patch14)
- [Respostas de Perguntas em Documentos com LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
- [Classificação de Vídeo sem Anotação com X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
## 100 Projetos Usando Transformers
Transformers é mais do que um conjunto de ferramentas para usar modelos pré-treinados: é uma comunidade de projetos construídos ao seu redor e o Hugging Face Hub. Queremos que o Transformers permita que desenvolvedores, pesquisadores, estudantes, professores, engenheiros e qualquer outra pessoa construa seus projetos dos sonhos.
Para celebrar as 100.000 estrelas do Transformers, decidimos destacar a comunidade e criamos a página [awesome-transformers](./awesome-transformers.md), que lista 100 projetos incríveis construídos nas proximidades dos Transformers.
Se você possui ou utiliza um projeto que acredita que deveria fazer parte da lista, abra um PR para adicioná-lo!
## Se você está procurando suporte personalizado da equipe Hugging Face
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## Tour Rápido
Para usar imediatamente um modelo em uma entrada específica (texto, imagem, áudio, ...), oferecemos a API `pipeline`. Os pipelines agrupam um modelo pré-treinado com o pré-processamento que foi usado durante o treinamento desse modelo. Aqui está como usar rapidamente um pipeline para classificar textos como positivos ou negativos:
```python
from transformers import pipeline
# Carregue o pipeline de classificação de texto
>>> classifier = pipeline("sentiment-analysis")
# Classifique o texto como positivo ou negativo
>>> classifier("Estamos muito felizes em apresentar o pipeline no repositório dos transformers.")
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
```
A segunda linha de código baixa e armazena em cache o modelo pré-treinado usado pelo pipeline, enquanto a terceira linha o avalia no texto fornecido. Neste exemplo, a resposta é "positiva" com uma confiança de 99,97%.
Muitas tarefas têm um `pipeline` pré-treinado pronto para uso, não apenas em PNL, mas também em visão computacional e processamento de áudio. Por exemplo, podemos facilmente extrair objetos detectados em uma imagem:
``` python
>>> import requests
>>> from PIL import Image
>>> from transformers import pipeline
# Download an image with cute cats
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
>>> image_data = requests.get(url, stream=True).raw
>>> image = Image.open(image_data)
# Allocate a pipeline for object detection
>>> object_detector = pipeline('object-detection')
>>> object_detector(image)
[{'score': 0.9982201457023621,
'label': 'remote',
'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960021376609802,
'label': 'remote',
'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9954745173454285,
'label': 'couch',
'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988006353378296,
'label': 'cat',
'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9986783862113953,
'label': 'cat',
'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
```
Aqui obtemos uma lista de objetos detectados na imagem, com uma caixa envolvendo o objeto e uma pontuação de confiança. Aqui está a imagem original à esquerda, com as previsões exibidas à direita:
<h3 align="center">
<a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a>
<a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a>
</h3>
Você pode aprender mais sobre as tarefas suportadas pela API `pipeline` em [este tutorial](https://huggingface.co/docs/transformers/task_summary).
Além do `pipeline`, para baixar e usar qualquer um dos modelos pré-treinados em sua tarefa específica, tudo o que é necessário são três linhas de código. Aqui está a versão em PyTorch:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)
```
E aqui está o código equivalente para TensorFlow:
```python
>>> from transformers import AutoTokenizer, TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)
```
O tokenizador é responsável por todo o pré-processamento que o modelo pré-treinado espera, e pode ser chamado diretamente em uma única string (como nos exemplos acima) ou em uma lista. Ele produzirá um dicionário que você pode usar no código subsequente ou simplesmente passar diretamente para o seu modelo usando o operador de descompactação de argumentos **.
O modelo em si é um [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) ou um [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)(dependendo do seu back-end) que você pode usar como de costume. [Este tutorial](https://huggingface.co/docs/transformers/training) explica como integrar esse modelo em um ciclo de treinamento clássico do PyTorch ou TensorFlow, ou como usar nossa API `Trainer` para ajuste fino rápido em um novo conjunto de dados.
## Por que devo usar transformers?
1. Modelos state-of-the-art fáceis de usar:
- Alto desempenho em compreensão e geração de linguagem natural, visão computacional e tarefas de áudio.
- Barreira de entrada baixa para educadores e profissionais.
- Poucas abstrações visíveis para o usuário, com apenas três classes para aprender.
- Uma API unificada para usar todos os nossos modelos pré-treinados.
1. Menores custos de computação, menor pegada de carbono:
- Pesquisadores podem compartilhar modelos treinados em vez de treinar sempre do zero.
- Profissionais podem reduzir o tempo de computação e os custos de produção.
- Dezenas de arquiteturas com mais de 60.000 modelos pré-treinados em todas as modalidades.
1. Escolha o framework certo para cada parte da vida de um modelo:
- Treine modelos state-of-the-art em 3 linhas de código.
- Mova um único modelo entre frameworks TF2.0/PyTorch/JAX à vontade.
- Escolha o framework certo de forma contínua para treinamento, avaliação e produção.
1. Personalize facilmente um modelo ou um exemplo para atender às suas necessidades:
- Fornecemos exemplos para cada arquitetura para reproduzir os resultados publicados pelos autores originais.
- Os detalhes internos do modelo são expostos de maneira consistente.
- Os arquivos do modelo podem ser usados de forma independente da biblioteca para experimentos rápidos.
## Por que não devo usar transformers?
- Esta biblioteca não é uma caixa de ferramentas modular para construir redes neurais. O código nos arquivos do modelo não é refatorado com abstrações adicionais de propósito, para que os pesquisadores possam iterar rapidamente em cada um dos modelos sem se aprofundar em abstrações/arquivos adicionais.
- A API de treinamento não é projetada para funcionar com qualquer modelo, mas é otimizada para funcionar com os modelos fornecidos pela biblioteca. Para loops de aprendizado de máquina genéricos, você deve usar outra biblioteca (possivelmente, [Accelerate](https://huggingface.co/docs/accelerate)).
- Embora nos esforcemos para apresentar o maior número possível de casos de uso, os scripts em nossa [pasta de exemplos](https://github.com/huggingface/transformers/tree/main/examples) são apenas isso: exemplos. É esperado que eles não funcionem prontos para uso em seu problema específico e que seja necessário modificar algumas linhas de código para adaptá-los às suas necessidades.
### Com pip
Este repositório é testado no Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ e TensorFlow 2.6+.
Você deve instalar o 🤗 Transformers em um [ambiente virtual](https://docs.python.org/3/library/venv.html). Se você não está familiarizado com ambientes virtuais em Python, confira o [guia do usuário](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
Primeiro, crie um ambiente virtual com a versão do Python que você vai usar e ative-o.
Em seguida, você precisará instalar pelo menos um dos back-ends Flax, PyTorch ou TensorFlow.
Consulte a [página de instalação do TensorFlow](https://www.tensorflow.org/install/), a [página de instalação do PyTorch](https://pytorch.org/get-started/locally/#start-locally) e/ou [Flax](https://github.com/google/flax#quick-install) e [Jax](https://github.com/google/jax#installation) páginas de instalação para obter o comando de instalação específico para a sua plataforma.
Quando um desses back-ends estiver instalado, o 🤗 Transformers pode ser instalado usando pip da seguinte forma:
```bash
pip install transformers
```
Se você deseja experimentar com os exemplos ou precisa da versão mais recente do código e não pode esperar por um novo lançamento, você deve instalar a [biblioteca a partir do código-fonte](https://huggingface.co/docs/transformers/installation#installing-from-source).
### Com conda
O 🤗 Transformers pode ser instalado com conda da seguinte forma:
```bash
conda install conda-forge::transformers
```
> **_NOTA:_** Instalar `transformers` pelo canal `huggingface` está obsoleto.
Siga as páginas de instalação do Flax, PyTorch ou TensorFlow para ver como instalá-los com conda.
Siga as páginas de instalação do Flax, PyTorch ou TensorFlow para ver como instalá-los com o conda.
> **_NOTA:_** No Windows, você pode ser solicitado a ativar o Modo de Desenvolvedor para aproveitar o cache. Se isso não for uma opção para você, por favor nos avise [neste problema](https://github.com/huggingface/huggingface_hub/issues/1062).
## Arquiteturas de Modelos
**[Todos os pontos de verificação de modelo](https://huggingface.co/models)** fornecidos pelo 🤗 Transformers são integrados de forma transparente do [model hub](https://huggingface.co/models) do huggingface.co, onde são carregados diretamente por [usuários](https://huggingface.co/users) e [organizações](https://huggingface.co/organizations).
Número atual de pontos de verificação: 
🤗 Transformers atualmente fornece as seguintes arquiteturas: veja [aqui](https://huggingface.co/docs/transformers/model_summary) para um resumo de alto nível de cada uma delas.
Para verificar se cada modelo tem uma implementação em Flax, PyTorch ou TensorFlow, ou possui um tokenizador associado com a biblioteca 🤗 Tokenizers, consulte [esta tabela](https://huggingface.co/docs/transformers/index#supported-frameworks).
Essas implementações foram testadas em vários conjuntos de dados (veja os scripts de exemplo) e devem corresponder ao desempenho das implementações originais. Você pode encontrar mais detalhes sobre o desempenho na seção de Exemplos da [documentação](https://github.com/huggingface/transformers/tree/main/examples).
## Saiba mais
| Seção | Descrição |
|-|-|
| [Documentação](https://huggingface.co/docs/transformers/) | Documentação completa da API e tutoriais |
| [Resumo de Tarefas](https://huggingface.co/docs/transformers/task_summary) | Tarefas suportadas pelo 🤗 Transformers |
| [Tutorial de Pré-processamento](https://huggingface.co/docs/transformers/preprocessing) | Usando a classe `Tokenizer` para preparar dados para os modelos |
| [Treinamento e Ajuste Fino](https://huggingface.co/docs/transformers/training) | Usando os modelos fornecidos pelo 🤗 Transformers em um loop de treinamento PyTorch/TensorFlow e a API `Trainer` |
| [Tour Rápido: Scripts de Ajuste Fino/Utilização](https://github.com/huggingface/transformers/tree/main/examples) | Scripts de exemplo para ajuste fino de modelos em uma ampla gama de tarefas |
| [Compartilhamento e Envio de Modelos](https://huggingface.co/docs/transformers/model_sharing) | Envie e compartilhe seus modelos ajustados com a comunidade |
## Citação
Agora temos um [artigo](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que você pode citar para a biblioteca 🤗 Transformers:
```bibtex
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = out,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
```
| 0 |
mavonic_private_repos | mavonic_private_repos/transformers/SECURITY.md | # Security Policy
## Hugging Face Hub, remote artefacts, and remote code
Transformers is open-source software that is tightly coupled to the Hugging Face Hub. While you have the ability to use it
offline with pre-downloaded model weights, it provides a very simple way to download, use, and manage models locally.
When downloading artefacts that have been uploaded by others on any platform, you expose yourself to risks. Please
read below for the security recommendations in order to keep your runtime and local environment safe.
### Remote artefacts
Models uploaded on the Hugging Face Hub come in different formats. We heavily recommend uploading and downloading
models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized
by the transformers library), as developed specifically to prevent arbitrary code execution on your system.
To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetenstors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
### Remote code
#### Modeling
Transformers supports many model architectures, but is also the bridge between your Python runtime and models that
are stored in model repositories on the Hugging Face Hub.
These models require the `trust_remote_code=True` parameter to be set when using them; please **always** verify
the content of the modeling files when using this argument. We recommend setting a revision in order to ensure you
protect yourself from updates on the repository.
#### Tools
Through the `Agent` framework, remote tools can be downloaded to be used by the Agent. You're to specify these tools
yourself, but please keep in mind that their code will be run on your machine if the Agent chooses to run them.
Please inspect the code of the tools before passing them to the Agent to protect your runtime and local setup.
## Reporting a Vulnerability
🤗 Please feel free to submit vulnerability reports to our private bug bounty program at https://hackerone.com/hugging_face. You'll need to request access to the program by emailing security@huggingface.co.
Note that you'll need to be invited to our program, so send us a quick email at security@huggingface.co if you've found a vulnerability.
| 0 |
mavonic_private_repos | mavonic_private_repos/transformers/conftest.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
import pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
NOT_DEVICE_TESTS = {
"test_tokenization",
"test_processor",
"test_processing",
"test_beam_constraints",
"test_configuration_utils",
"test_data_collator",
"test_trainer_callback",
"test_trainer_utils",
"test_feature_extraction",
"test_image_processing",
"test_image_processor",
"test_image_transforms",
"test_optimization",
"test_retrieval",
"test_config",
"test_from_pretrained_no_checkpoint",
"test_keep_in_fp32_modules",
"test_gradient_checkpointing_backward_compatibility",
"test_gradient_checkpointing_enable_disable",
"test_save_load_fast_init_from_base",
"test_fast_init_context_manager",
"test_fast_init_tied_embeddings",
"test_save_load_fast_init_to_base",
"test_torch_save_load",
"test_initialization",
"test_forward_signature",
"test_model_common_attributes",
"test_model_main_input_name",
"test_correct_missing_keys",
"test_tie_model_weights",
"test_can_use_safetensors",
"test_load_save_without_tied_weights",
"test_tied_weights_keys",
"test_model_weights_reload_no_missing_tied_weights",
"test_pt_tf_model_equivalence",
"test_mismatched_shapes_have_properly_initialized_weights",
"test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist",
"test_model_is_small",
"test_tf_from_pt_safetensors",
"test_flax_from_pt_safetensors",
"ModelTest::test_pipeline_", # None of the pipeline tests from PipelineTesterMixin (of which XxxModelTest inherits from) are running on device
"ModelTester::test_pipeline_",
"/repo_utils/",
"/utils/",
"/tools/",
}
# allow having multiple repository checkouts and not needing to remember to rerun
# `pip install -e '.[dev]'` when switching between checkouts and running tests.
git_repo_path = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def pytest_configure(config):
config.addinivalue_line(
"markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"
)
config.addinivalue_line(
"markers", "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"
)
config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers", "tool_tests: mark the tool tests that are run on their specific schedule")
config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu")
def pytest_collection_modifyitems(items):
for item in items:
if any(test_name in item.nodeid for test_name in NOT_DEVICE_TESTS):
item.add_marker(pytest.mark.not_device_test)
def pytest_addoption(parser):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(parser)
def pytest_terminal_summary(terminalreporter):
from transformers.testing_utils import pytest_terminal_summary_main
make_reports = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(terminalreporter, id=make_reports)
def pytest_sessionfinish(session, exitstatus):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
session.exitstatus = 0
# Doctest custom flag to ignore output.
IGNORE_RESULT = doctest.register_optionflag("IGNORE_RESULT")
OutputChecker = doctest.OutputChecker
class CustomOutputChecker(OutputChecker):
def check_output(self, want, got, optionflags):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self, want, got, optionflags)
doctest.OutputChecker = CustomOutputChecker
_pytest.doctest.DoctestModule = HfDoctestModule
doctest.DocTestParser = HfDocTestParser
| 0 |
mavonic_private_repos | mavonic_private_repos/transformers/.coveragerc | [run]
source=transformers
omit =
# skip convertion scripts from testing for now
*/convert_*
*/__main__.py
[report]
exclude_lines =
pragma: no cover
raise
except
register_parameter | 0 |
mavonic_private_repos | mavonic_private_repos/transformers/README_vi.md | <!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg">
<img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;">
</picture>
<br/>
<br/>
</p>
<p align="center">
<a href="https://circleci.com/gh/huggingface/transformers">
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
</a>
<a href="https://huggingface.co/docs/transformers/index">
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
</a>
<a href="https://github.com/huggingface/transformers/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
</a>
<a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
</a>
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
</p>
<h4 align="center">
<p>
<a href="https://github.com/huggingface/transformers/">English</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> |
<a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> |
<b>Tiếng việt</b> |
</p>
</h4>
<h3 align="center">
<p>Công nghệ Học máy tiên tiến cho JAX, PyTorch và TensorFlow</p>
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
</h3>
🤗 Transformers cung cấp hàng ngàn mô hình được huấn luyện trước để thực hiện các nhiệm vụ trên các modalities khác nhau như văn bản, hình ảnh và âm thanh.
Các mô hình này có thể được áp dụng vào:
* 📝 Văn bản, cho các nhiệm vụ như phân loại văn bản, trích xuất thông tin, trả lời câu hỏi, tóm tắt, dịch thuật và sinh văn bản, trong hơn 100 ngôn ngữ.
* 🖼️ Hình ảnh, cho các nhiệm vụ như phân loại hình ảnh, nhận diện đối tượng và phân đoạn.
* 🗣️ Âm thanh, cho các nhiệm vụ như nhận dạng giọng nói và phân loại âm thanh.
Các mô hình Transformer cũng có thể thực hiện các nhiệm vụ trên **nhiều modalities kết hợp**, như trả lời câu hỏi về bảng, nhận dạng ký tự quang học, trích xuất thông tin từ tài liệu quét, phân loại video và trả lời câu hỏi hình ảnh.
🤗 Transformers cung cấp các API để tải xuống và sử dụng nhanh chóng các mô hình được huấn luyện trước đó trên văn bản cụ thể, điều chỉnh chúng trên tập dữ liệu của riêng bạn và sau đó chia sẻ chúng với cộng đồng trên [model hub](https://huggingface.co/models) của chúng tôi. Đồng thời, mỗi module python xác định một kiến trúc là hoàn toàn độc lập và có thể được sửa đổi để cho phép thực hiện nhanh các thí nghiệm nghiên cứu.
🤗 Transformers được hỗ trợ bởi ba thư viện học sâu phổ biến nhất — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) và [TensorFlow](https://www.tensorflow.org/) — với tích hợp mượt mà giữa chúng. Việc huấn luyện mô hình của bạn với một thư viện trước khi tải chúng để sử dụng trong suy luận với thư viện khác là rất dễ dàng.
## Các demo trực tuyến
Bạn có thể kiểm tra hầu hết các mô hình của chúng tôi trực tiếp trên trang của chúng từ [model hub](https://huggingface.co/models). Chúng tôi cũng cung cấp [dịch vụ lưu trữ mô hình riêng tư, phiên bản và API suy luận](https://huggingface.co/pricing) cho các mô hình công khai và riêng tư.
Dưới đây là một số ví dụ:
Trong Xử lý Ngôn ngữ Tự nhiên:
- [Hoàn thành từ vụng về từ với BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
- [Nhận dạng thực thể đặt tên với Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
- [Tạo văn bản tự nhiên với Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- [Suy luận Ngôn ngữ Tự nhiên với RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
- [Tóm tắt văn bản với BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
- [Trả lời câu hỏi với DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
- [Dịch văn bản với T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
Trong Thị giác Máy tính:
- [Phân loại hình ảnh với ViT](https://huggingface.co/google/vit-base-patch16-224)
- [Phát hiện đối tượng với DETR](https://huggingface.co/facebook/detr-resnet-50)
- [Phân đoạn ngữ nghĩa với SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
- [Phân đoạn toàn diện với Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
- [Ước lượng độ sâu với Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
- [Phân loại video với VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
- [Phân đoạn toàn cầu với OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
Trong âm thanh:
- [Nhận dạng giọng nói tự động với Whisper](https://huggingface.co/openai/whisper-large-v3)
- [Phát hiện từ khóa với Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
- [Phân loại âm thanh với Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
Trong các nhiệm vụ đa phương thức:
- [Trả lời câu hỏi về bảng với TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
- [Trả lời câu hỏi hình ảnh với ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
- [Mô tả hình ảnh với LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
- [Phân loại hình ảnh không cần nhãn với SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384)
- [Trả lời câu hỏi văn bản tài liệu với LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
- [Phân loại video không cần nhãn với X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
- [Phát hiện đối tượng không cần nhãn với OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
- [Phân đoạn hình ảnh không cần nhãn với CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)
- [Tạo mặt nạ tự động với SAM](https://huggingface.co/docs/transformers/model_doc/sam)
## 100 dự án sử dụng Transformers
Transformers không chỉ là một bộ công cụ để sử dụng các mô hình được huấn luyện trước: đó là một cộng đồng các dự án xây dựng xung quanh nó và Hugging Face Hub. Chúng tôi muốn Transformers giúp các nhà phát triển, nhà nghiên cứu, sinh viên, giáo sư, kỹ sư và bất kỳ ai khác xây dựng những dự án mơ ước của họ.
Để kỷ niệm 100.000 sao của transformers, chúng tôi đã quyết định tập trung vào cộng đồng và tạo ra trang [awesome-transformers](./awesome-transformers.md) liệt kê 100 dự án tuyệt vời được xây dựng xung quanh transformers.
Nếu bạn sở hữu hoặc sử dụng một dự án mà bạn tin rằng nên được thêm vào danh sách, vui lòng mở một PR để thêm nó!
## Nếu bạn đang tìm kiếm hỗ trợ tùy chỉnh từ đội ngũ Hugging Face
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## Hành trình nhanh
Để ngay lập tức sử dụng một mô hình trên một đầu vào cụ thể (văn bản, hình ảnh, âm thanh, ...), chúng tôi cung cấp API `pipeline`. Pipelines nhóm một mô hình được huấn luyện trước với quá trình tiền xử lý đã được sử dụng trong quá trình huấn luyện của mô hình đó. Dưới đây là cách sử dụng nhanh một pipeline để phân loại văn bản tích cực so với tiêu cực:
```python
>>> from transformers import pipeline
# Cấp phát một pipeline cho phân tích cảm xúc
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
```
Dòng code thứ hai tải xuống và lưu trữ bộ mô hình được huấn luyện được sử dụng bởi pipeline, trong khi dòng thứ ba đánh giá nó trên văn bản đã cho. Ở đây, câu trả lời là "tích cực" với độ tin cậy là 99,97%.
Nhiều nhiệm vụ có sẵn một `pipeline` được huấn luyện trước, trong NLP nhưng cũng trong thị giác máy tính và giọng nói. Ví dụ, chúng ta có thể dễ dàng trích xuất các đối tượng được phát hiện trong một hình ảnh:
``` python
>>> import requests
>>> from PIL import Image
>>> from transformers import pipeline
# Tải xuống một hình ảnh với những con mèo dễ thương
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
>>> image_data = requests.get(url, stream=True).raw
>>> image = Image.open(image_data)
# Cấp phát một pipeline cho phát hiện đối tượng
>>> object_detector = pipeline('object-detection')
>>> object_detector(image)
[{'score': 0.9982201457023621,
'label': 'remote',
'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960021376609802,
'label': 'remote',
'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9954745173454285,
'label': 'couch',
'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988006353378296,
'label': 'cat',
'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9986783862113953,
'label': 'cat',
'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
```
Ở đây, chúng ta nhận được một danh sách các đối tượng được phát hiện trong hình ảnh, với một hộp bao quanh đối tượng và một điểm đánh giá độ tin cậy. Đây là hình ảnh gốc ở bên trái, với các dự đoán hiển thị ở bên phải:
<h3 align="center">
<a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a>
<a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a>
</h3>
Bạn có thể tìm hiểu thêm về các nhiệm vụ được hỗ trợ bởi API `pipeline` trong [hướng dẫn này](https://huggingface.co/docs/transformers/task_summary).
Ngoài `pipeline`, để tải xuống và sử dụng bất kỳ mô hình được huấn luyện trước nào cho nhiệm vụ cụ thể của bạn, chỉ cần ba dòng code. Đây là phiên bản PyTorch:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
>>> outputs = model(**inputs)
```
Và đây là mã tương đương cho TensorFlow:
```python
>>> from transformers import AutoTokenizer, TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
>>> outputs = model(**inputs)
```
Tokenizer là thành phần chịu trách nhiệm cho việc tiền xử lý mà mô hình được huấn luyện trước mong đợi và có thể được gọi trực tiếp trên một chuỗi đơn (như trong các ví dụ trên) hoặc một danh sách. Nó sẽ xuất ra một từ điển mà bạn có thể sử dụng trong mã phụ thuộc hoặc đơn giản là truyền trực tiếp cho mô hình của bạn bằng cách sử dụng toán tử ** để giải nén đối số.
Chính mô hình là một [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) thông thường hoặc một [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (tùy thuộc vào backend của bạn) mà bạn có thể sử dụng như bình thường. [Hướng dẫn này](https://huggingface.co/docs/transformers/training) giải thích cách tích hợp một mô hình như vậy vào một vòng lặp huấn luyện cổ điển PyTorch hoặc TensorFlow, hoặc cách sử dụng API `Trainer` của chúng tôi để tinh chỉnh nhanh chóng trên một bộ dữ liệu mới.
## Tại sao tôi nên sử dụng transformers?
1. Các mô hình tiên tiến dễ sử dụng:
- Hiệu suất cao trong việc hiểu và tạo ra ngôn ngữ tự nhiên, thị giác máy tính và âm thanh.
- Ngưỡng vào thấp cho giảng viên và người thực hành.
- Ít trừu tượng dành cho người dùng với chỉ ba lớp học.
- Một API thống nhất để sử dụng tất cả các mô hình được huấn luyện trước của chúng tôi.
2. Giảm chi phí tính toán, làm giảm lượng khí thải carbon:
- Các nhà nghiên cứu có thể chia sẻ các mô hình đã được huấn luyện thay vì luôn luôn huấn luyện lại.
- Người thực hành có thể giảm thời gian tính toán và chi phí sản xuất.
- Hàng chục kiến trúc với hơn 400.000 mô hình được huấn luyện trước trên tất cả các phương pháp.
3. Lựa chọn framework phù hợp cho mọi giai đoạn của mô hình:
- Huấn luyện các mô hình tiên tiến chỉ trong 3 dòng code.
- Di chuyển một mô hình duy nhất giữa các framework TF2.0/PyTorch/JAX theo ý muốn.
- Dễ dàng chọn framework phù hợp cho huấn luyện, đánh giá và sản xuất.
4. Dễ dàng tùy chỉnh một mô hình hoặc một ví dụ theo nhu cầu của bạn:
- Chúng tôi cung cấp các ví dụ cho mỗi kiến trúc để tái tạo kết quả được công bố bởi các tác giả gốc.
- Các thành phần nội tại của mô hình được tiết lộ một cách nhất quán nhất có thể.
- Các tệp mô hình có thể được sử dụng độc lập với thư viện để thực hiện các thử nghiệm nhanh chóng.
## Tại sao tôi không nên sử dụng transformers?
- Thư viện này không phải là một bộ công cụ modul cho các khối xây dựng mạng neural. Mã trong các tệp mô hình không được tái cấu trúc với các trừu tượng bổ sung một cách cố ý, để các nhà nghiên cứu có thể lặp nhanh trên từng mô hình mà không cần đào sâu vào các trừu tượng/tệp bổ sung.
- API huấn luyện không được thiết kế để hoạt động trên bất kỳ mô hình nào, mà được tối ưu hóa để hoạt động với các mô hình được cung cấp bởi thư viện. Đối với vòng lặp học máy chung, bạn nên sử dụng một thư viện khác (có thể là [Accelerate](https://huggingface.co/docs/accelerate)).
- Mặc dù chúng tôi cố gắng trình bày càng nhiều trường hợp sử dụng càng tốt, nhưng các tập lệnh trong thư mục [examples](https://github.com/huggingface/transformers/tree/main/examples) chỉ là ví dụ. Dự kiến rằng chúng sẽ không hoạt động ngay tức khắc trên vấn đề cụ thể của bạn và bạn sẽ phải thay đổi một số dòng mã để thích nghi với nhu cầu của bạn.
## Cài đặt
### Sử dụng pip
Thư viện này được kiểm tra trên Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ và TensorFlow 2.6+.
Bạn nên cài đặt 🤗 Transformers trong một [môi trường ảo Python](https://docs.python.org/3/library/venv.html). Nếu bạn chưa quen với môi trường ảo Python, hãy xem [hướng dẫn sử dụng](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
Trước tiên, tạo một môi trường ảo với phiên bản Python bạn sẽ sử dụng và kích hoạt nó.
Sau đó, bạn sẽ cần cài đặt ít nhất một trong số các framework Flax, PyTorch hoặc TensorFlow.
Vui lòng tham khảo [trang cài đặt TensorFlow](https://www.tensorflow.org/install/), [trang cài đặt PyTorch](https://pytorch.org/get-started/locally/#start-locally) và/hoặc [Flax](https://github.com/google/flax#quick-install) và [Jax](https://github.com/google/jax#installation) để biết lệnh cài đặt cụ thể cho nền tảng của bạn.
Khi đã cài đặt một trong các backend đó, 🤗 Transformers có thể được cài đặt bằng pip như sau:
```bash
pip install transformers
```
Nếu bạn muốn thực hiện các ví dụ hoặc cần phiên bản mới nhất của mã và không thể chờ đợi cho một phiên bản mới, bạn phải [cài đặt thư viện từ nguồn](https://huggingface.co/docs/transformers/installation#installing-from-source).
### Với conda
🤗 Transformers có thể được cài đặt bằng conda như sau:
```shell script
conda install conda-forge::transformers
```
> **_GHI CHÚ:_** Cài đặt `transformers` từ kênh `huggingface` đã bị lỗi thời.
Hãy làm theo trang cài đặt của Flax, PyTorch hoặc TensorFlow để xem cách cài đặt chúng bằng conda.
> **_GHI CHÚ:_** Trên Windows, bạn có thể được yêu cầu kích hoạt Chế độ phát triển để tận dụng việc lưu cache. Nếu điều này không phải là một lựa chọn cho bạn, hãy cho chúng tôi biết trong [vấn đề này](https://github.com/huggingface/huggingface_hub/issues/1062).
## Kiến trúc mô hình
**[Tất cả các điểm kiểm tra mô hình](https://huggingface.co/models)** được cung cấp bởi 🤗 Transformers được tích hợp một cách mượt mà từ trung tâm mô hình huggingface.co [model hub](https://huggingface.co/models), nơi chúng được tải lên trực tiếp bởi [người dùng](https://huggingface.co/users) và [tổ chức](https://huggingface.co/organizations).
Số lượng điểm kiểm tra hiện tại: 
🤗 Transformers hiện đang cung cấp các kiến trúc sau đây: xem [ở đây](https://huggingface.co/docs/transformers/model_summary) để có một tóm tắt tổng quan về mỗi kiến trúc.
Để kiểm tra xem mỗi mô hình có một phiên bản thực hiện trong Flax, PyTorch hoặc TensorFlow, hoặc có một tokenizer liên quan được hỗ trợ bởi thư viện 🤗 Tokenizers, vui lòng tham khảo [bảng này](https://huggingface.co/docs/transformers/index#supported-frameworks).
Những phiên bản này đã được kiểm tra trên một số tập dữ liệu (xem các tập lệnh ví dụ) và nên tương đương với hiệu suất của các phiên bản gốc. Bạn có thể tìm thấy thêm thông tin về hiệu suất trong phần Ví dụ của [tài liệu](https://github.com/huggingface/transformers/tree/main/examples).
## Tìm hiểu thêm
| Phần | Mô tả |
|-|-|
| [Tài liệu](https://huggingface.co/docs/transformers/) | Toàn bộ tài liệu API và hướng dẫn |
| [Tóm tắt nhiệm vụ](https://huggingface.co/docs/transformers/task_summary) | Các nhiệm vụ được hỗ trợ bởi 🤗 Transformers |
| [Hướng dẫn tiền xử lý](https://huggingface.co/docs/transformers/preprocessing) | Sử dụng lớp `Tokenizer` để chuẩn bị dữ liệu cho các mô hình |
| [Huấn luyện và điều chỉnh](https://huggingface.co/docs/transformers/training) | Sử dụng các mô hình được cung cấp bởi 🤗 Transformers trong vòng lặp huấn luyện PyTorch/TensorFlow và API `Trainer` |
| [Hướng dẫn nhanh: Điều chỉnh/sử dụng các kịch bản](https://github.com/huggingface/transformers/tree/main/examples) | Các kịch bản ví dụ để điều chỉnh mô hình trên nhiều nhiệm vụ khác nhau |
| [Chia sẻ và tải lên mô hình](https://huggingface.co/docs/transformers/model_sharing) | Tải lên và chia sẻ các mô hình đã điều chỉnh của bạn với cộng đồng |
## Trích dẫn
Bây giờ chúng ta có một [bài báo](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) mà bạn có thể trích dẫn cho thư viện 🤗 Transformers:
```bibtex
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
```
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