Spaces:
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Update Space (evaluate main: e4a27243)
Browse files- bleu.py +28 -3
- requirements.txt +1 -1
bleu.py
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@@ -13,6 +13,9 @@
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# limitations under the License.
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""" BLEU metric. """
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import datasets
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import evaluate
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"""
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class Bleu(evaluate.Metric):
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return evaluate.MetricInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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features=[
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datasets.Features(
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{
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@@ -112,7 +129,12 @@ class Bleu(evaluate.Metric):
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],
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)
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def _compute(self, predictions, references
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# if only one reference is provided make sure we still use list of lists
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if isinstance(references[0], str):
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references = [[ref] for ref in references]
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@@ -120,7 +142,10 @@ class Bleu(evaluate.Metric):
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references = [[tokenizer(r) for r in ref] for ref in references]
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predictions = [tokenizer(p) for p in predictions]
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score = compute_bleu(
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reference_corpus=references,
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)
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(bleu, precisions, bp, ratio, translation_length, reference_length) = score
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return {
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# limitations under the License.
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""" BLEU metric. """
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from dataclasses import dataclass
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from typing import Callable, Optional
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import datasets
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import evaluate
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"""
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@dataclass
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class BleuConfig(evaluate.info.Config):
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name: str = "default"
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tokenizer: Optional[Callable] = None
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max_order: int = 4
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smooth: bool = False
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class Bleu(evaluate.Metric):
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CONFIG_CLASS = BleuConfig
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ALLOWED_CONFIG_NAMES = ["default"]
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def _info(self, config):
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return evaluate.MetricInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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config=config,
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features=[
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datasets.Features(
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{
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],
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)
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def _compute(self, predictions, references):
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if self.config.tokenizer is None:
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tokenizer = Tokenizer13a()
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else:
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tokenizer = self.config.tokenizer
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# if only one reference is provided make sure we still use list of lists
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if isinstance(references[0], str):
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references = [[ref] for ref in references]
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references = [[tokenizer(r) for r in ref] for ref in references]
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predictions = [tokenizer(p) for p in predictions]
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score = compute_bleu(
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reference_corpus=references,
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translation_corpus=predictions,
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max_order=self.config.max_order,
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smooth=self.config.smooth,
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)
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(bleu, precisions, bp, ratio, translation_length, reference_length) = score
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return {
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requirements.txt
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@@ -1 +1 @@
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git+https://github.com/huggingface/evaluate@
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git+https://github.com/huggingface/evaluate@e4a2724377909fe2aeb4357e3971e5a569673b39
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