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| # Copyright 2022 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. | |
| """Exact match test for model comparison.""" | |
| import datasets | |
| import numpy as np | |
| import evaluate | |
| _DESCRIPTION = """ | |
| Returns the rate at which the predictions of one model exactly match those of another model. | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Args: | |
| predictions1 (`list` of `int`): Predicted labels for model 1. | |
| predictions2 (`list` of `int`): Predicted labels for model 2. | |
| Returns: | |
| exact_match (`float`): Dictionary containing exact_match rate. Possible values are between 0.0 and 1.0, inclusive. | |
| Examples: | |
| >>> exact_match = evaluate.load("exact_match", module_type="comparison") | |
| >>> results = exact_match.compute(predictions1=[1, 1, 1], predictions2=[1, 1, 1]) | |
| >>> print(results) | |
| {'exact_match': 1.0} | |
| """ | |
| _CITATION = """ | |
| """ | |
| class ExactMatch(evaluate.Comparison): | |
| def _info(self): | |
| return evaluate.ComparisonInfo( | |
| module_type="comparison", | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "predictions1": datasets.Value("int64"), | |
| "predictions2": datasets.Value("int64"), | |
| } | |
| ), | |
| ) | |
| def _compute(self, predictions1, predictions2): | |
| score_list = [p1 == p2 for p1, p2 in zip(predictions1, predictions2)] | |
| return {"exact_match": np.mean(score_list)} | |