Update Space (evaluate main: 828c6327)

README.md

@@ -1,12 +1,113 @@
 ---
-title: Sacrebleu
-emoji: 👁
-colorFrom: indigo
-colorTo: yellow
+title: SacreBLEU
+emoji: 🤗 
+colorFrom: blue
+colorTo: red
 sdk: gradio
 sdk_version: 3.0.2
 app_file: app.py
 pinned: false
+tags:
+- evaluate
+- metric
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+# 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.
+
+
+```python
+>>> 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](https://pypi.org/project/mecab-python3).
+- **`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:
+```python
+{'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
+
+```python
+>>> 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
+```bibtex
+@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](https://github.com/mjpost/sacreBLEU) for more information.

app.py

@@ -0,0 +1,6 @@
+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("sacrebleu")
+launch_gradio_widget(module)

requirements.txt

@@ -0,0 +1,4 @@
+# TODO: fix github to release
+git+https://github.com/huggingface/evaluate.git@b6e6ed7f3e6844b297bff1b43a1b4be0709b9671
+datasets~=2.0
+sacrebleu

sacrebleu.py

@@ -0,0 +1,166 @@
+# Copyright 2020 The HuggingFace Evaluate Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" SACREBLEU metric. """
+
+import datasets
+import sacrebleu as scb
+from packaging import version
+
+import evaluate
+
+
+_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",
+}
+"""
+
+_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.
+"""
+
+_KWARGS_DESCRIPTION = """
+Produces BLEU scores along with its sufficient statistics
+from a source against one or more references.
+
+Args:
+    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](https://pypi.org/project/mecab-python3).
+    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`.
+
+Returns:
+    'score': BLEU score,
+    'counts': Counts,
+    'totals': Totals,
+    'precisions': Precisions,
+    'bp': Brevity penalty,
+    'sys_len': predictions length,
+    'ref_len': reference length,
+
+Examples:
+
+    Example 1:
+        >>> 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
+
+    Example 2:
+        >>> 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
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class Sacrebleu(evaluate.EvaluationModule):
+    def _info(self):
+        if version.parse(scb.__version__) < version.parse("1.4.12"):
+            raise ImportWarning(
+                "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
+                'You can install it with `pip install "sacrebleu>=1.4.12"`.'
+            )
+        return evaluate.EvaluationModuleInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            homepage="https://github.com/mjpost/sacreBLEU",
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Value("string", id="sequence"),
+                    "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
+                }
+            ),
+            codebase_urls=["https://github.com/mjpost/sacreBLEU"],
+            reference_urls=[
+                "https://github.com/mjpost/sacreBLEU",
+                "https://en.wikipedia.org/wiki/BLEU",
+                "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
+            ],
+        )
+
+    def _compute(
+        self,
+        predictions,
+        references,
+        smooth_method="exp",
+        smooth_value=None,
+        force=False,
+        lowercase=False,
+        tokenize=None,
+        use_effective_order=False,
+    ):
+        references_per_prediction = len(references[0])
+        if any(len(refs) != references_per_prediction for refs in references):
+            raise ValueError("Sacrebleu requires the same number of references for each prediction")
+        transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
+        output = scb.corpus_bleu(
+            predictions,
+            transformed_references,
+            smooth_method=smooth_method,
+            smooth_value=smooth_value,
+            force=force,
+            lowercase=lowercase,
+            use_effective_order=use_effective_order,
+            **(dict(tokenize=tokenize) if tokenize else {}),
+        )
+        output_dict = {
+            "score": output.score,
+            "counts": output.counts,
+            "totals": output.totals,
+            "precisions": output.precisions,
+            "bp": output.bp,
+            "sys_len": output.sys_len,
+            "ref_len": output.ref_len,
+        }
+        return output_dict