happytransformer


Namehappytransformer JSON
Version 2.2.2 PyPI version JSON
download
home_pagehttps://github.com/EricFillion/happy-transformer
SummaryHappy 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.
upload_time2021-05-08 03:03:41
maintainer
docs_urlNone
authorThe Happy Transformer Development Team
requires_python
licenseApache 2.0
keywords bert roberta xlnet transformer happy happytransformer classification nlp nlu natural language processing understanding
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 
[![Downloads](https://pepy.tech/badge/happytransformer)](https://pepy.tech/project/happytransformer)
[![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](http://happytransformer.com)
![PyPI](https://img.shields.io/pypi/v/happytransformer)
[![](https://github.com/EricFillion/happy-transformer/workflows/build/badge.svg)](https://github.com/EricFillion/happy-transformer/actions)

# Happy Transformer 
**Documentation and news: [happytransformer.com](http://happytransformer.com)**


Join our brand new Discord server: [![Support Server](https://img.shields.io/discord/839263772312862740.svg?label=Discord&logo=Discord&colorB=7289da&style=?style=flat-square&logo=appveyor)](https://discord.gg/psVwe3wfTb)



![HappyTransformer](logo.png)

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   |
|------------------------------------|--------------|------------|
| Word Prediction                    | ✔            | ✔          |
| Text Generation                    | ✔            | ✔          |
| Text Classification                | ✔            | ✔          | 
| Question Answering                 | ✔            | ✔          | 
| 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



            

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    "description": "[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) \n[![Downloads](https://pepy.tech/badge/happytransformer)](https://pepy.tech/project/happytransformer)\n[![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](http://happytransformer.com)\n![PyPI](https://img.shields.io/pypi/v/happytransformer)\n[![](https://github.com/EricFillion/happy-transformer/workflows/build/badge.svg)](https://github.com/EricFillion/happy-transformer/actions)\n\n# Happy Transformer \n**Documentation and news: [happytransformer.com](http://happytransformer.com)**\n\n\nJoin our brand new Discord server: [![Support Server](https://img.shields.io/discord/839263772312862740.svg?label=Discord&logo=Discord&colorB=7289da&style=?style=flat-square&logo=appveyor)](https://discord.gg/psVwe3wfTb)\n\n\n\n![HappyTransformer](logo.png)\n\nHappy 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. \n\n## Features \n\n| Public Methods                     | Basic Usage  | Training   |\n|------------------------------------|--------------|------------|\n| Word Prediction                    | \u2714            | \u2714          |\n| Text Generation                    | \u2714            | \u2714          |\n| Text Classification                | \u2714            | \u2714          | \n| Question Answering                 | \u2714            | \u2714          | \n| Next Sentence Prediction           | \u2714            |            | \n| Token Classification               | \u2714            |            | \n\n## Quick Start\n```sh\npip install happytransformer\n```\n\n```python\n\nfrom happytransformer import HappyWordPrediction\n#--------------------------------------#\n    happy_wp = HappyWordPrediction()  # default uses distilbert-base-uncased\n    result = happy_wp.predict_mask(\"I think therefore I [MASK]\")\n    print(result)  # [WordPredictionResult(token='am', score=0.10172799974679947)]\n    print(result[0].token)  # am\n\n\n## Maintainers\n- [Eric Fillion](https://github.com/ericfillion)  Lead Maintainer\n- [Ted Brownlow](https://github.com/ted537) Maintainer\n\n\n",
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