README.md

metadata

title: SacreBLEU
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
  - evaluate
  - metric
description: >-
  SacreBLEU provides hassle-free computation of shareable, comparable, and
  reproducible BLEU scores. Inspired by Rico Sennrich's `multi-bleu-detok.perl`,
  it produces the official WMT scores but works with plain text. It also knows
  all the standard test sets and handles downloading, processing, and
  tokenization for you.

  See the [README.md] file at https://github.com/mjpost/sacreBLEU for more
  information.

Metric Card for SacreBLEU

Metric Description

SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich's multi-bleu-detok.perl, it produces the official Workshop on Machine Translation (WMT) scores but works with plain text. It also knows all the standard test sets and handles downloading, processing, and tokenization.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.

How to Use

This metric takes a set of predictions and a set of references as input, along with various optional parameters.

>>> predictions = ["hello there general kenobi", "foo bar foobar"]
>>> references = [["hello there general kenobi", "hello there !"],
...                 ["foo bar foobar", "foo bar foobar"]]
>>> sacrebleu = evaluate.load("sacrebleu")
>>> results = sacrebleu.compute(predictions=predictions, 
...                             references=references)
>>> print(list(results.keys()))
['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
>>> print(round(results["score"], 1))
100.0

Inputs

• predictions (list of str): list of translations to score. Each translation should be tokenized into a list of tokens.
• references (list of list of str): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
• smooth_method (str): The smoothing method to use, defaults to 'exp'. Possible values are:
  • 'none': no smoothing
  • 'floor': increment zero counts
  • 'add-k': increment num/denom by k for n>1
  • 'exp': exponential decay
• smooth_value (float): The smoothing value. Only valid when smooth_method='floor' (in which case smooth_value defaults to 0.1) or smooth_method='add-k' (in which case smooth_value defaults to 1).
• tokenize (str): Tokenization method to use for BLEU. If not provided, defaults to 'zh' for Chinese, 'ja-mecab' for Japanese and '13a' (mteval) otherwise. Possible values are:
  • 'none': No tokenization.
  • 'zh': Chinese tokenization.
  • '13a': mimics the mteval-v13a script from Moses.
  • 'intl': International tokenization, mimics the mteval-v14 script from Moses
  • 'char': Language-agnostic character-level tokenization.
  • 'ja-mecab': Japanese tokenization. Uses the MeCab tokenizer.
• lowercase (bool): If True, lowercases the input, enabling case-insensitivity. Defaults to False.
• force (bool): If True, insists that your tokenized input is actually detokenized. Defaults to False.
• use_effective_order (bool): If True, stops including n-gram orders for which precision is 0. This should be True, if sentence-level BLEU will be computed. Defaults to False.

Output Values

• score: BLEU score
• counts: Counts
• totals: Totals
• precisions: Precisions
• bp: Brevity penalty
• sys_len: predictions length
• ref_len: reference length

The output is in the following format:

{'score': 39.76353643835252, 'counts': [6, 4, 2, 1], 'totals': [10, 8, 6, 4], 'precisions': [60.0, 50.0, 33.333333333333336, 25.0], 'bp': 1.0, 'sys_len': 10, 'ref_len': 7}

The score can take any value between 0.0 and 100.0, inclusive.

Values from Popular Papers

Examples

>>> predictions = ["hello there general kenobi", 
...                 "on our way to ankh morpork"]
>>> references = [["hello there general kenobi", "hello there !"],
...                 ["goodbye ankh morpork", "ankh morpork"]]
>>> sacrebleu = evaluate.load("sacrebleu")
>>> results = sacrebleu.compute(predictions=predictions, 
...                             references=references)
>>> print(list(results.keys()))
['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
>>> print(round(results["score"], 1))
39.8

Limitations and Bias

Because what this metric calculates is BLEU scores, it has the same limitations as that metric, except that sacreBLEU is more easily reproducible.

Citation

@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}

Further References

• See the sacreBLEU README.md file for more information.