wehub-resource-sync
149 lines
5.3 KiB
Python
149 lines
5.3 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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import csv
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import os
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from paddle.io import Dataset
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from paddlenlp.transformers import RemBertTokenizer
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tokenization = RemBertTokenizer.from_pretrained("rembert")
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class InputExample(object):
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"""
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Use classes to store each example
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"""
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def __init__(self, guid, text_a, text_b=None, label=None):
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self.guid = guid
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self.text_a = text_a
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self.text_b = text_b
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self.label = label
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class MrpcProcessor(object):
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"""Load the dataset and convert each example text to ids"""
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def get_train_examples(self, data_dir):
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_2k.tsv")), "dev")
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def get_test_examples(self, data_dir):
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_2k.tsv")), "test")
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def get_labels(self):
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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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 = "%s-%s" % (set_type, i)
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text_a = tokenization(line[1])["input_ids"]
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text_b = tokenization(line[2])["input_ids"]
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label = int(line[3])
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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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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with open(input_file, "r", encoding="utf-8") as f:
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reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
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lines = []
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for line in reader:
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lines.append(line)
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return lines
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class XNLIProcessor(object):
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"""Load the dataset and convert each example text to ids"""
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def get_train_examples(self, data_dir):
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "multinli.train.en.tsv")), "train")
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def get_dev_examples(self, data_dir):
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "xnli.test.tsv")), "test")
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def get_labels(self):
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return ["neutral", "entailment", "contradictory"]
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def _create_examples(self, lines, set_type):
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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 = "%s-%s" % (set_type, i)
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if set_type == "train":
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text_a = " ".join(line[0].strip().split(" "))
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text_b = " ".join(line[1].strip().split(" "))
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text_a = tokenization(text_a)["input_ids"]
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text_b = tokenization(text_b)["input_ids"]
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label = self.get_labels().index(line[2].strip())
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examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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else:
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text_a = " ".join(line[6].strip().split(" "))
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text_b = " ".join(line[7].strip().split(" "))
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if line[1] == "contradiction":
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line[1] = "contradictory"
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label = self.get_labels().index(line[1].strip())
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text_a = tokenization(text_a)["input_ids"]
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text_b = tokenization(text_b)["input_ids"]
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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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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with open(input_file, "r", encoding="utf-8") as f:
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reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
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lines = []
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for line in reader:
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lines.append(line)
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return lines
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class DataGenerator(Dataset):
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"""Data generator is used to feed features into dataloader."""
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def __init__(self, features):
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super(DataGenerator, self).__init__()
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self.features = features
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def __getitem__(self, item):
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text_a = self.features[item].text_a
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text_b = self.features[item].text_b
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text_a_token_type_ids = [0] * len(text_a)
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text_b_token_type_ids = [1] * len(text_b)
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label = [self.features[item].label]
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return dict(
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text_a=text_a,
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text_b=text_b,
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text_a_token_type_ids=text_a_token_type_ids,
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text_b_token_type_ids=text_b_token_type_ids,
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label=label,
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)
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def __len__(self):
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return len(self.features)
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