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 !
Installation
pip install text2topicloss
or
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 :
@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
Metadata
Release files for text2topicloss 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| text2topicloss-1.0.0.tar.gz | 15.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| text2topicloss-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.2 kB
Release files / text2topicloss-1.0.0.tar.gz
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| Size | 15.5 kB |
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Release files / text2topicloss-1.0.0-py3-none-any.whl
| Download URL | text2topicloss-1.0.0-py3-none-any.whl |
|---|---|
| Size | 15.7 kB |
| Tags | Python 3 |
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