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| # coding=utf-8 | |
| # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. | |
| # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # 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. | |
| """ XNLI utils (dataset loading and evaluation)""" | |
| import os | |
| from ...utils import logging | |
| from .utils import DataProcessor, InputExample | |
| logger = logging.get_logger(__name__) | |
| class XnliProcessor(DataProcessor): | |
| """ | |
| Processor for the XNLI dataset. Adapted from | |
| https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207 | |
| """ | |
| def __init__(self, language, train_language=None): | |
| self.language = language | |
| self.train_language = train_language | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| lg = self.language if self.train_language is None else self.train_language | |
| lines = self._read_tsv(os.path.join(data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv")) | |
| examples = [] | |
| for i, line in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = f"train-{i}" | |
| text_a = line[0] | |
| text_b = line[1] | |
| label = "contradiction" if line[2] == "contradictory" else line[2] | |
| if not isinstance(text_a, str): | |
| raise ValueError(f"Training input {text_a} is not a string") | |
| if not isinstance(text_b, str): | |
| raise ValueError(f"Training input {text_b} is not a string") | |
| if not isinstance(label, str): | |
| raise ValueError(f"Training label {label} is not a string") | |
| examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.test.tsv")) | |
| examples = [] | |
| for i, line in enumerate(lines): | |
| if i == 0: | |
| continue | |
| language = line[0] | |
| if language != self.language: | |
| continue | |
| guid = f"test-{i}" | |
| text_a = line[6] | |
| text_b = line[7] | |
| label = line[1] | |
| if not isinstance(text_a, str): | |
| raise ValueError(f"Training input {text_a} is not a string") | |
| if not isinstance(text_b, str): | |
| raise ValueError(f"Training input {text_b} is not a string") | |
| if not isinstance(label, str): | |
| raise ValueError(f"Training label {label} is not a string") | |
| examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["contradiction", "entailment", "neutral"] | |
| xnli_processors = { | |
| "xnli": XnliProcessor, | |
| } | |
| xnli_output_modes = { | |
| "xnli": "classification", | |
| } | |
| xnli_tasks_num_labels = { | |
| "xnli": 3, | |
| } | |