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|
%global _empty_manifest_terminate_build 0
Name: python-happytransformer
Version: 2.4.1
Release: 1
Summary: Happy Transformer is an API built on top of Hugging Face's Transformer library that makes it easy to utilize state-of-the-art NLP models.
License: Apache 2.0
URL: https://github.com/EricFillion/happy-transformer
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7a/1c/01318ece577d3a7fdd6d9103dc6dcc9eaf20312aef76a3158c399ec8a207/happytransformer-2.4.1.tar.gz
BuildArch: noarch
Requires: python3-torch
Requires: python3-tqdm
Requires: python3-transformers
Requires: python3-datasets
Requires: python3-sentencepiece
Requires: python3-protobuf
Requires: python3-dataclasses
%description
[](https://opensource.org/licenses/Apache-2.0)
[](https://pepy.tech/project/happytransformer)
[](http://happytransformer.com)
[](https://github.com/EricFillion/happy-transformer/actions)
# Happy Transformer
**Documentation and news: [happytransformer.com](http://happytransformer.com)**
New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo [link](https://www.udemy.com/course/nlp-text-generation-python-web-app/?couponCode=LAUNCH)
Join our Discord server: [](https://discord.gg/psVwe3wfTb)
Happy Transformer is an package built on top of [Hugging Face's transformer library](https://huggingface.co/transformers/) that makes it easy to utilize state-of-the-art NLP models.
## Features
| Public Methods | Basic Usage | Training |
|------------------------------------|--------------|------------|
| Text Generation | ✔ | ✔ |
| Text Classification | ✔ | ✔ |
| Word Prediction | ✔ | ✔ |
| Question Answering | ✔ | ✔ |
| Text-to-Text | ✔ | ✔ |
| Next Sentence Prediction | ✔ | |
| Token Classification | ✔ | |
## Quick Start
```sh
pip install happytransformer
```
```python
from happytransformer import HappyWordPrediction
#--------------------------------------#
happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased
result = happy_wp.predict_mask("I think therefore I [MASK]")
print(result) # [WordPredictionResult(token='am', score=0.10172799974679947)]
print(result[0].token) # am
```
## Maintainers
- [Eric Fillion](https://github.com/ericfillion) Lead Maintainer
- [Ted Brownlow](https://github.com/ted537) Maintainer
## Tutorials
[Text generation with training (GPT-Neo)](https://youtu.be/GzHJ3NUVtV4)
[Text classification (training)](https://www.vennify.ai/train-text-classification-transformers/)
[Text classification (hate speech detection)](https://youtu.be/jti2sPQYzeQ)
[Text classification (sentiment analysis)](https://youtu.be/Ew72EAgM7FM)
[Word prediction with training (DistilBERT, RoBERTa)](https://youtu.be/AWe0PHsPc_M)
[Top T5 Models ](https://www.vennify.ai/top-t5-transformer-models/)
[Grammar Correction](https://www.vennify.ai/grammar-correction-python/)
[Fine-tune a Grammar Correction Model](https://www.vennify.ai/fine-tune-grammar-correction/)
%package -n python3-happytransformer
Summary: Happy Transformer is an API built on top of Hugging Face's Transformer library that makes it easy to utilize state-of-the-art NLP models.
Provides: python-happytransformer
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-happytransformer
[](https://opensource.org/licenses/Apache-2.0)
[](https://pepy.tech/project/happytransformer)
[](http://happytransformer.com)
[](https://github.com/EricFillion/happy-transformer/actions)
# Happy Transformer
**Documentation and news: [happytransformer.com](http://happytransformer.com)**
New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo [link](https://www.udemy.com/course/nlp-text-generation-python-web-app/?couponCode=LAUNCH)
Join our Discord server: [](https://discord.gg/psVwe3wfTb)
Happy Transformer is an package built on top of [Hugging Face's transformer library](https://huggingface.co/transformers/) that makes it easy to utilize state-of-the-art NLP models.
## Features
| Public Methods | Basic Usage | Training |
|------------------------------------|--------------|------------|
| Text Generation | ✔ | ✔ |
| Text Classification | ✔ | ✔ |
| Word Prediction | ✔ | ✔ |
| Question Answering | ✔ | ✔ |
| Text-to-Text | ✔ | ✔ |
| Next Sentence Prediction | ✔ | |
| Token Classification | ✔ | |
## Quick Start
```sh
pip install happytransformer
```
```python
from happytransformer import HappyWordPrediction
#--------------------------------------#
happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased
result = happy_wp.predict_mask("I think therefore I [MASK]")
print(result) # [WordPredictionResult(token='am', score=0.10172799974679947)]
print(result[0].token) # am
```
## Maintainers
- [Eric Fillion](https://github.com/ericfillion) Lead Maintainer
- [Ted Brownlow](https://github.com/ted537) Maintainer
## Tutorials
[Text generation with training (GPT-Neo)](https://youtu.be/GzHJ3NUVtV4)
[Text classification (training)](https://www.vennify.ai/train-text-classification-transformers/)
[Text classification (hate speech detection)](https://youtu.be/jti2sPQYzeQ)
[Text classification (sentiment analysis)](https://youtu.be/Ew72EAgM7FM)
[Word prediction with training (DistilBERT, RoBERTa)](https://youtu.be/AWe0PHsPc_M)
[Top T5 Models ](https://www.vennify.ai/top-t5-transformer-models/)
[Grammar Correction](https://www.vennify.ai/grammar-correction-python/)
[Fine-tune a Grammar Correction Model](https://www.vennify.ai/fine-tune-grammar-correction/)
%package help
Summary: Development documents and examples for happytransformer
Provides: python3-happytransformer-doc
%description help
[](https://opensource.org/licenses/Apache-2.0)
[](https://pepy.tech/project/happytransformer)
[](http://happytransformer.com)
[](https://github.com/EricFillion/happy-transformer/actions)
# Happy Transformer
**Documentation and news: [happytransformer.com](http://happytransformer.com)**
New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo [link](https://www.udemy.com/course/nlp-text-generation-python-web-app/?couponCode=LAUNCH)
Join our Discord server: [](https://discord.gg/psVwe3wfTb)
Happy Transformer is an package built on top of [Hugging Face's transformer library](https://huggingface.co/transformers/) that makes it easy to utilize state-of-the-art NLP models.
## Features
| Public Methods | Basic Usage | Training |
|------------------------------------|--------------|------------|
| Text Generation | ✔ | ✔ |
| Text Classification | ✔ | ✔ |
| Word Prediction | ✔ | ✔ |
| Question Answering | ✔ | ✔ |
| Text-to-Text | ✔ | ✔ |
| Next Sentence Prediction | ✔ | |
| Token Classification | ✔ | |
## Quick Start
```sh
pip install happytransformer
```
```python
from happytransformer import HappyWordPrediction
#--------------------------------------#
happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased
result = happy_wp.predict_mask("I think therefore I [MASK]")
print(result) # [WordPredictionResult(token='am', score=0.10172799974679947)]
print(result[0].token) # am
```
## Maintainers
- [Eric Fillion](https://github.com/ericfillion) Lead Maintainer
- [Ted Brownlow](https://github.com/ted537) Maintainer
## Tutorials
[Text generation with training (GPT-Neo)](https://youtu.be/GzHJ3NUVtV4)
[Text classification (training)](https://www.vennify.ai/train-text-classification-transformers/)
[Text classification (hate speech detection)](https://youtu.be/jti2sPQYzeQ)
[Text classification (sentiment analysis)](https://youtu.be/Ew72EAgM7FM)
[Word prediction with training (DistilBERT, RoBERTa)](https://youtu.be/AWe0PHsPc_M)
[Top T5 Models ](https://www.vennify.ai/top-t5-transformer-models/)
[Grammar Correction](https://www.vennify.ai/grammar-correction-python/)
[Fine-tune a Grammar Correction Model](https://www.vennify.ai/fine-tune-grammar-correction/)
%prep
%autosetup -n happytransformer-2.4.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-happytransformer -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 2.4.1-1
- Package Spec generated
|
