Metadata-Version: 2.1
Name: bert-score
Version: 0.1.2
Summary: PyTorch implementation of BERT score
Home-page: https://github.com/Tiiiger/bert_score
Author: Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi
Author-email: tzhang@asapp.com
License: MIT
Description: # BERTScore
        Automatic Evaluation Metric described in the paper [BERTScore: Evaluating Text Generation with BERT](https://arxiv.org/abs/1904.09675).
        
        #### Authors:
        * [Tianyi Zhang](https://scholar.google.com/citations?user=OI0HSa0AAAAJ&hl=en)*
        * Varsha Kishore
        * [Felix Wu](https://scholar.google.com.tw/citations?user=sNL8SSoAAAAJ&hl=en)*
        * [Kilian Q. Weinberger](http://kilian.cs.cornell.edu/index.html)
        * [Yoav Artzi](https://yoavartzi.com/)
        
        *: Equal Contribution
        
        ### Overview
        BERTScore leverages the pre-trained contextual embeddings from BERT and matches
        words in candidate and reference sentences by cosine similarity.
        It has been shown to correlate with human judgment on setence-level and
        system-level evaluation.
        Moreover, BERTScore computes precision, recall, and F1 measure, which can be
        useful for evaluating different language generation tasks.
        
        For an illustration, BERTScore precision can be computed as
        ![](https://github.com/Tiiiger/bert_score/blob/master/bert_score.png "BERTScore")
        
        If you find this repo useful, please cite:
        ```
        @article{bert-score,
          title={BERTScore: Evaluating Text Generation with BERT},
          author={Zhang, Tianyi and Kishore, Varsha and Wu, Felix and Weinberger, Kilian Q. and Artzi, Yoav.},
          journal={arXiv preprint arXiv:1904.09675},
          year={2019}
        }
        ```
        
        ### Installation
        Install from pip by 
        
        ```sh
        pip install bert-score
        ```
        
        Install it from the source by:
        ```sh
        git clone https://github.com/Tiiiger/bert_score
        cd bert_score
        pip install -r requiremnts.txt
        pip install .
        ```
        
        ### Usage
        
        #### Metric
        We provide a command line interface(CLI) of BERTScore as well as a python module. 
        For the CLI, you can use it as follows:
        1. To evaluate English text files:
        
        We provide example inputs under `./example`.
        
        ```sh
        bert-score -r example/refs.txt -c example/hyps.txt --bert bert-base-uncased 
        ```
        2. To evaluate Chinese text files:
        
        Please format your input files similar to the ones in `./example`.
        
        ```sh
        bert-score -r [references] -c [candidates] --bert bert-base-chinese
        ```
        3. To evaluate text files in other languages:
        
        Please format your input files similar to the ones in `./example`.
        
        ```sh
        bert-score -r [references] -c [candidates]
        ```
        See more options by `bert-score -h`.
        
        For the python module, we provide a [demo](https://github.com/Tiiiger/bert_score/blob/master/example/Demo.ipynb). 
        Please refer to [`bert_score/score.py`](https://github.com/Tiiiger/bert_score/blob/master/bert_score/score.py) for more details.
        
        ### Acknowledgement
        This repo wouldn't be possible without the awesome [bert](https://github.com/google-research/bert) and [pytorch-pretrained-BERT](https://github.com/huggingface/pytorch-pretrained-BERT).
        
Keywords: BERT NLP deep learning google metric
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
