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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""ANLS - Average Normalized Levenshtein Similarity"""
import datasets
import evaluate
from compute_score import compute_score
_CITATION = """\
@article{,
title = {Binary codes capable of correcting deletions, insertions, and reversals},
journal = {Soviet physics doklady},
volume = {10},
number = {8},
pages = {707--710},
year = {1966},
url = {https://nymity.ch/sybilhunting/pdf/Levenshtein1966a.pdf},
author = {V. I. Levenshtein},
}
"""
_DESCRIPTION = """\
ANLS refer to the average normalized Levenshtein similarity.
"""
_KWARGS_DESCRIPTION = """
Computes Average Normalized Levenshtein Similarity (ANLS).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': the text of the answer
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the SQuAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'anls': The ANLS score of predicted tokens versus the gold answer
Examples:
>>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
>>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
>>> anls_metric = evaluate.load("anls")
>>> results = anls_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'anls_score': 100.0}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Anls(evaluate.Metric):
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": {"id": datasets.Value("string"), "prediction_text": datasets.Value("string")},
"references": {
"id": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
},
}
)
)
def _compute(self, predictions, references):
prediction_dict = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
dataset = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
score = compute_score(dataset=dataset, predictions=prediction_dict)
return score
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