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# BLEURT: a Transfer Learning-Based Metric for Natural Language Generation

BLEURT is an evaluation metric for Natural Language Generation. It takes a pair of sentences as input, a *reference* and a *candidate*, and it returns a score that indicates to what extent the candidate is fluent and conveys the mearning of the reference. It is comparable to [`sentence-BLEU`](https://en.wikipedia.org/wiki/BLEU), [`BERTscore`](https://arxiv.org/abs/1904.09675), and [`COMET`](https://github.com/Unbabel/COMET).
BLEURT is an evaluation metric for Natural Language Generation. It takes a pair of sentences as input, a *reference* and a *candidate*, and it returns a score that indicates to what extent the candidate is fluent and conveys the meaning of the reference. It is comparable to [`sentence-BLEU`](https://en.wikipedia.org/wiki/BLEU), [`BERTscore`](https://arxiv.org/abs/1904.09675), and [`COMET`](https://github.com/Unbabel/COMET).

BLEURT is a *trained metric*, that is, it is a regression model trained on ratings data. The model is based on [`BERT`](https://arxiv.org/abs/1810.04805) and [`RemBERT`](https://arxiv.org/pdf/2010.12821.pdf). This repository contains all the code necessary to use it and/or fine-tune it for your own applications. BLEURT uses Tensorflow, and it benefits greatly from modern GPUs (it runs on CPU too).

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