lvwerra HF staff commited on
Commit
bffe9e5
1 Parent(s): ec0f188

Update Space (evaluate main: c447fc8e)

Browse files
Files changed (2) hide show
  1. bleu.py +3 -28
  2. requirements.txt +1 -1
bleu.py CHANGED
@@ -13,9 +13,6 @@
13
  # limitations under the License.
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  """ BLEU metric. """
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16
- from dataclasses import dataclass
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- from typing import Callable, Optional
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-
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  import datasets
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  import evaluate
@@ -87,27 +84,13 @@ Examples:
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  """
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89
 
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- @dataclass
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- class BleuConfig(evaluate.info.Config):
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-
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- name: str = "default"
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-
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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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-
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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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- CONFIG_CLASS = BleuConfig
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- ALLOWED_CONFIG_NAMES = ["default"]
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-
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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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  {
@@ -129,12 +112,7 @@ 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 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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-
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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]
@@ -142,10 +120,7 @@ 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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- 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 {
13
  # limitations under the License.
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  """ BLEU metric. """
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  import datasets
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  import evaluate
84
  """
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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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+ def _info(self):
 
 
 
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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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  ],
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  )
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+ def _compute(self, predictions, references, tokenizer=Tokenizer13a(), max_order=4, smooth=False):
 
 
 
 
 
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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, translation_corpus=predictions, max_order=max_order, smooth=smooth
 
 
 
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  )
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  (bleu, precisions, bp, ratio, translation_length, reference_length) = score
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  return {
requirements.txt CHANGED
@@ -1 +1 @@
1
- git+https://github.com/huggingface/evaluate@e4a2724377909fe2aeb4357e3971e5a569673b39
1
+ git+https://github.com/huggingface/evaluate@c447fc8eda9c62af501bfdc6988919571050d950