98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" XNLI utils (dataset loading and evaluation)"""
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import os
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from ...utils import logging
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from .utils import DataProcessor, InputExample
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logger = logging.get_logger(__name__)
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class XnliProcessor(DataProcessor):
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"""
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Processor for the XNLI dataset. Adapted from
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https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207
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"""
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def __init__(self, language, train_language=None):
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self.language = language
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self.train_language = train_language
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def get_train_examples(self, data_dir):
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"""See base class."""
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lg = self.language if self.train_language is None else self.train_language
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lines = self._read_tsv(os.path.join(data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"))
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examples = []
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for i, line in enumerate(lines):
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if i == 0:
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continue
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guid = f"train-{i}"
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text_a = line[0]
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text_b = line[1]
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label = "contradiction" if line[2] == "contradictory" else line[2]
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if not isinstance(text_a, str):
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raise ValueError(f"Training input {text_a} is not a string")
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if not isinstance(text_b, str):
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raise ValueError(f"Training input {text_b} is not a string")
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if not isinstance(label, str):
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raise ValueError(f"Training label {label} is not a string")
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examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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def get_test_examples(self, data_dir):
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"""See base class."""
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lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.test.tsv"))
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examples = []
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for i, line in enumerate(lines):
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if i == 0:
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continue
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language = line[0]
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if language != self.language:
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continue
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guid = f"test-{i}"
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text_a = line[6]
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text_b = line[7]
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label = line[1]
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if not isinstance(text_a, str):
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raise ValueError(f"Training input {text_a} is not a string")
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if not isinstance(text_b, str):
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raise ValueError(f"Training input {text_b} is not a string")
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if not isinstance(label, str):
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raise ValueError(f"Training label {label} is not a string")
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examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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xnli_processors = {
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"xnli": XnliProcessor,
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}
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xnli_output_modes = {
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"xnli": "classification",
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}
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xnli_tasks_num_labels = {
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"xnli": 3,
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}
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