Align measures the correspondence of information between two pieces of text, which is introduced in
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
Yuheng Zha*, Yichi Yang*, Ruichen Li and Zhiting Hu
NeurIPS 2023
For a given text pair I have been in Kentucky, Kirby, text I have been in the US is aligned with I have been in Europe or text Kentucky has the best fried chicken is not aligned with
Text alignment is applicable to a wide range of downstream tasks, e.g., Natural Language Inference, Paraphrase Detection, Fact Verification, Semantic Textual Similarity, Question Answering, Coreference Resolution and Information Retrieval.
We recommend to install Align in a conda environment.
First clone this repo:
git clone https://github.com/yuh-zha/Align.git
cd AlignCreate a virtual conda environment:
conda create -n Align python=3.9
conda activate Align
pip install -e .Install the required spaCy model
python -m spacy download en_core_web_smWe provide two versions of Align checkpoints: Align-base and Align-large. The -base model is based on RoBERTa-base and has 125M parameters. The -large model is based on RoBERTa-large and has 355M parameters.
Align-base: https://huggingface.co/yzha/Align/resolve/main/Align-base.ckpt
Align-large: https://huggingface.co/yzha/Align/resolve/main/Align-large.ckpt
To get the alignment score of the text pairs (text_a and text_b), use the scorer function of Align:
from align import Align
text_a = ["Your text here"]
text_b = ["Your text here"]
scorer = Align(model="roberta-large", batch_size=32, device="cuda", ckpt_path="path/to/ckpt")
score = scorer(contexts=text_a, claims=text_b)model: The backbone model of Align. It can be roberta-base or roberta-large
batch_size: The batch size of inference
ckpt_path: The path to the checkpoint
If you find this work helpful, please consider cite:
@inproceedings{
zha2023text,
title={Text Alignment Is An Efficient Unified Model for Massive {NLP} Tasks},
author={Yuheng Zha and Yichi Yang and Ruichen Li and Zhiting Hu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=xkkBFePoFn}
}