Model Description
Refer to https://github.com/qiyuw/WSPAlign and https://github.com/qiyuw/WSPAlign.InferEval for details.
Qucik Usage
First clone inference repository:
git clone https://github.com/qiyuw/WSPAlign.InferEval.git
Then install the requirements following https://github.com/qiyuw/WSPAlign.InferEval. For inference only transformers
, SpaCy
and torch
are required.
Finally, run the following example:
python inference.py --model_name_or_path qiyuw/WSPAlign-ft-kftt --src_lang ja --src_text="私は猫が好きです。" --tgt_lang en --tgt_text="I like cats."
Check inference.py
for details usage.
Citation
Cite our paper if WSPAlign helps your work:
@inproceedings{wu-etal-2023-wspalign,
title = "{WSPA}lign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction",
author = "Wu, Qiyu and Nagata, Masaaki and Tsuruoka, Yoshimasa",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.621",
pages = "11084--11099",
}
- Downloads last month
- 632
This model does not have enough activity to be deployed to Inference API (serverless) yet.
Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.