merge

n_words_match.py

@@ -1,130 +0,0 @@
-# Copyright 2020 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.
-"""Exact Match metric."""
-import re
-import string
-
-import numpy as np
-
-import datasets
-
-
-_DESCRIPTION = """
-Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
-"""
-
-_KWARGS_DESCRIPTION = """
-Args:
-    predictions: List of predicted texts.
-    references: List of reference texts.
-    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
-        ignore when calculating the exact matches. Note: these regexes are removed
-        from the input data before the changes based on the options below (e.g. ignore_case,
-        ignore_punctuation, ignore_numbers) are applied.
-    ignore_case: Boolean, defaults to False. If true, turns everything
-        to lowercase so that capitalization differences are ignored.
-    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
-        comparing predictions and references.
-    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
-        comparing predictions and references.
-Returns:
-    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
-Examples:
-    >>> exact_match = datasets.load_metric("exact_match")
-    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
-    >>> preds = ["cat?", "theater", "yelling", "agent"]
-    >>> results = exact_match.compute(references=refs, predictions=preds)
-    >>> print(round(results["exact_match"], 1))
-    25.0
-    >>> exact_match = datasets.load_metric("exact_match")
-    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
-    >>> preds = ["cat?", "theater", "yelling", "agent"]
-    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
-    >>> print(round(results["exact_match"], 1))
-    50.0
-    >>> exact_match = datasets.load_metric("exact_match")
-    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
-    >>> preds = ["cat?", "theater", "yelling", "agent"]
-    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
-    >>> print(round(results["exact_match"], 1))
-    75.0
-    >>> exact_match = datasets.load_metric("exact_match")
-    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
-    >>> preds = ["cat?", "theater", "yelling", "agent"]
-    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
-    >>> print(round(results["exact_match"], 1))
-    100.0
-    >>> exact_match = datasets.load_metric("exact_match")
-    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It's like comparing oranges and apples."]
-    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It's like comparing apples and oranges."]
-    >>> results = exact_match.compute(references=refs, predictions=preds)
-    >>> print(round(results["exact_match"], 1))
-    33.3
-"""
-
-_CITATION = """
-"""
-
-
-@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class ExactMatch(datasets.Metric):
-    def _info(self):
-        return datasets.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "predictions": datasets.Value("string", id="sequence"),
-                    "references": datasets.Value("string", id="sequence"),
-                }
-            ),
-            reference_urls=[],
-        )
-
-    def _compute(
-        self,
-        predictions,
-        references,
-        regexes_to_ignore=None,
-        ignore_case=False,
-        ignore_punctuation=False,
-        ignore_numbers=False,
-    ):
-
-        if regexes_to_ignore is not None:
-            for s in regexes_to_ignore:
-                predictions = np.array([re.sub(s, "", x) for x in predictions])
-                references = np.array([re.sub(s, "", x) for x in references])
-        else:
-            predictions = np.asarray(predictions)
-            references = np.asarray(references)
-
-        if ignore_case:
-            predictions = np.char.lower(predictions)
-            references = np.char.lower(references)
-
-        if ignore_punctuation:
-            repl_table = string.punctuation.maketrans("", "", string.punctuation)
-            predictions = np.char.translate(predictions, table=repl_table)
-            references = np.char.translate(references, table=repl_table)
-
-        if ignore_numbers:
-            repl_table = string.digits.maketrans("", "", string.digits)
-            predictions = np.char.translate(predictions, table=repl_table)
-            references = np.char.translate(references, table=repl_table)
-
-        score_list = predictions == references
-
-        return {"exact_match": np.mean(score_list) * 100}