Metadata-Version: 2.1
Name: text2topicloss
Version: 1.0.0
Summary: Text2topic loss for bi-encoder models
Author-email: Marc-Alexis Azaïs <azaismarc.pro@gmail.com>
Project-URL: Repository, https://github.com/azaismarc/text2topic
Project-URL: Paper, https://aclanthology.org/2023.emnlp-industry.10/
Keywords: text2topic,nlp,topic modeling,BERT,BERTopic
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Customer Service
Classifier: Topic :: Text Processing
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: sentence-transformers

# Text2Topic

Implementation of bi-encoder Text2Topic architecture describe in *Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities*

**Read the paper & the original repository for details about the algorithm !**

- PAPER : https://aclanthology.org/2023.emnlp-industry.10/

![Text2topic schema](https://raw.githubusercontent.com/azaismarc/text2topic/master/text2topic.png)

## Installation

```bash
pip install text2topicloss
```

or

```bash
git clone
python -m pip install .
```
## Citations

**I'm not the author of the original paper**, so if you use this library, please cite the original paper :

```bibtex
@inproceedings{wang-etal-2023-text2topic,
    title = "{T}ext2{T}opic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities",
    author = "Wang, Fengjun  and
      Beladev, Moran  and
      Kleinfeld, Ofri  and
      Frayerman, Elina  and
      Shachar, Tal  and
      Fainman, Eran  and
      Lastmann Assaraf, Karen  and
      Mizrachi, Sarai  and
      Wang, Benjamin",
    editor = "Wang, Mingxuan  and
      Zitouni, Imed",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-industry.10",
    doi = "10.18653/v1/2023.emnlp-industry.10",
    pages = "93--103",
}
```

## License

GNU General Public License v3.0
