Text2topic loss for bi-encoder models
Project description
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
text2topicloss-1.0.0.tar.gz
(15.5 kB
view hashes)
Built Distribution
Close
Hashes for text2topicloss-1.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bd70f4c377cde2877dbc633e89512343a139b6d3788392115b7f0328e0c48897 |
|
MD5 | 8df3f6cba2d966c75df21079d24ded5c |
|
BLAKE2b-256 | 9e42cc1bda6de73ecfdd5fdf0ffd91f7d3c86aa4805082515c538820a63f17c7 |