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title: BERT Score
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
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 sentence-level and
system-level evaluation. Moreover, BERTScore computes precision, recall, and
F1 measure, which can be useful for evaluating different language generation
tasks.
See the project's README at https://github.com/Tiiiger/bert_score#readme for
more information.
Metric Card for BERT Score
Metric description
BERTScore is an automatic evaluation metric for text generation that computes a similarity score for each token in the candidate sentence with each token in the reference sentence. It leverages the pre-trained contextual embeddings from BERT models and matches words in candidate and reference sentences by cosine similarity.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks.
How to use
BERTScore takes 3 mandatory arguments : predictions (a list of string of candidate sentences), references (a list of strings or list of list of strings of reference sentences) and either lang (a string of two letters indicating the language of the sentences, in ISO 639-1 format) or model_type (a string specififying which model to use, according to the BERT specification). The default behavior of the metric is to use the suggested model for the target language when one is specified, otherwise to use the model_type indicated.
from evaluate import load
bertscore = load("bertscore")
predictions = ["hello there", "general kenobi"]
references = ["hello there", "general kenobi"]
results = bertscore.compute(predictions=predictions, references=references, lang="en")
BERTScore also accepts multiple optional arguments:
num_layers (int): The layer of representation to use. The default is the number of layers tuned on WMT16 correlation data, which depends on the model_type used.
verbose (bool): Turn on intermediate status update. The default value is False.
idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
device (str): On which the contextual embedding model will be allocated on. If this argument is None, the model lives on cuda:0 if cuda is available.
nthreads (int): Number of threads used for computation. The default value is 4.
rescale_with_baseline (bool): Rescale BERTScore with the pre-computed baseline. The default value is False.
batch_size (int): BERTScore processing batch size, at least one of model_type or lang. lang needs to be specified when rescale_with_baseline is True.
baseline_path (str): Customized baseline file.
use_fast_tokenizer (bool): use_fast parameter passed to HF tokenizer. The default value is False.
Output values
BERTScore outputs a dictionary with the following values:
precision: The precision for each sentence from the predictions + references lists, which ranges from 0.0 to 1.0.
recall: The recall for each sentence from the predictions + references lists, which ranges from 0.0 to 1.0.
f1: The F1 score for each sentence from the predictions + references lists, which ranges from 0.0 to 1.0.
hashcode: The hashcode of the library.
Values from popular papers
The original BERTScore paper reported average model selection accuracies (Hits@1) on WMT18 hybrid systems for different language pairs, which ranged from 0.004 for en<->tr to 0.824 for en<->de.
For more recent model performance, see the metric leaderboard.
Examples
Maximal values with the distilbert-base-uncased model:
from evaluate import load
bertscore = load("bertscore")
predictions = ["hello world", "general kenobi"]
references = ["hello world", "general kenobi"]
results = bertscore.compute(predictions=predictions, references=references, model_type="distilbert-base-uncased")
print(results)
{'precision': [1.0, 1.0], 'recall': [1.0, 1.0], 'f1': [1.0, 1.0], 'hashcode': 'distilbert-base-uncased_L5_no-idf_version=0.3.10(hug_trans=4.10.3)'}
Partial match with the distilbert-base-uncased model:
from evaluate import load
bertscore = load("bertscore")
predictions = ["hello world", "general kenobi"]
references = ["goodnight moon", "the sun is shining"]
results = bertscore.compute(predictions=predictions, references=references, model_type="distilbert-base-uncased")
print(results)
{'precision': [0.7380737066268921, 0.5584042072296143], 'recall': [0.7380737066268921, 0.5889028906822205], 'f1': [0.7380737066268921, 0.5732481479644775], 'hashcode': 'bert-base-uncased_L5_no-idf_version=0.3.10(hug_trans=4.10.3)'}
Limitations and bias
The original BERTScore paper showed that BERTScore correlates well with human judgment on sentence-level and system-level evaluation, but this depends on the model and language pair selected.
Furthermore, not all languages are supported by the metric -- see the BERTScore supported language list for more information.
Finally, calculating the BERTScore metric involves downloading the BERT model that is used to compute the score-- the default model for en, roberta-large, takes over 1.4GB of storage space and downloading it can take a significant amount of time depending on the speed of your internet connection. If this is an issue, choose a smaller model; for instance distilbert-base-uncased is 268MB. A full list of compatible models can be found here.
Citation
@inproceedings{bert-score,
title={BERTScore: Evaluating Text Generation with BERT},
author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SkeHuCVFDr}
}