wehub-resource-sync
136 lines
5.1 KiB
Python
136 lines
5.1 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. 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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import random
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import numpy as np
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import paddle
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def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
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if trans_fn:
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dataset = dataset.map(trans_fn)
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shuffle = True if mode == "train" else False
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if mode == "train":
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batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
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else:
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batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
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return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True)
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def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False):
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"""
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Builds model inputs from a sequence.
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A BERT sequence has the following format:
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- single sequence: ``[CLS] X [SEP]``
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Args:
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example(obj:`list(str)`): The list of text to be converted to ids.
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tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
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which contains most of the methods. Users should refer to the superclass for more information regarding methods.
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max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
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Sequences longer than this will be truncated, sequences shorter will be padded.
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is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.
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Returns:
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input_ids(obj:`list[int]`): The list of query token ids.
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token_type_ids(obj: `list[int]`): List of query sequence pair mask.
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"""
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result = []
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for key, text in example.items():
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if "label" in key:
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# do_evaluate
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result += [example["label"]]
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else:
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# do_train
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encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length)
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input_ids = encoded_inputs["input_ids"]
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token_type_ids = encoded_inputs["token_type_ids"]
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result += [input_ids, token_type_ids]
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return result
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def read_simcse_text(data_path):
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"""Reads data."""
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with open(data_path, "r", encoding="utf-8") as f:
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for line in f:
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data = line.rstrip()
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yield {"text_a": data, "text_b": data}
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def read_text_pair(data_path, is_test=False):
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"""Reads data."""
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with open(data_path, "r", encoding="utf-8") as f:
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for line in f:
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data = line.rstrip().split("\t")
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if is_test is False:
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if len(data) != 3:
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continue
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yield {"text_a": data[0], "text_b": data[1], "label": data[2]}
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else:
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if len(data) != 2:
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continue
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yield {"text_a": data[0], "text_b": data[1]}
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def word_repetition(input_ids, token_type_ids, dup_rate=0.32):
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"""Word Repetition strategy."""
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input_ids = input_ids.numpy().tolist()
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token_type_ids = token_type_ids.numpy().tolist()
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batch_size, seq_len = len(input_ids), len(input_ids[0])
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repetitied_input_ids = []
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repetitied_token_type_ids = []
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rep_seq_len = seq_len
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for batch_id in range(batch_size):
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cur_input_id = input_ids[batch_id]
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actual_len = np.count_nonzero(cur_input_id)
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dup_word_index = []
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# If sequence length is less than 5, skip it
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if actual_len > 5:
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dup_len = random.randint(a=0, b=max(2, int(dup_rate * actual_len)))
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# Skip cls and sep position
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dup_word_index = random.sample(list(range(1, actual_len - 1)), k=dup_len)
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r_input_id = []
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r_token_type_id = []
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for idx, word_id in enumerate(cur_input_id):
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# Insert duplicate word
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if idx in dup_word_index:
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r_input_id.append(word_id)
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r_token_type_id.append(token_type_ids[batch_id][idx])
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r_input_id.append(word_id)
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r_token_type_id.append(token_type_ids[batch_id][idx])
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after_dup_len = len(r_input_id)
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repetitied_input_ids.append(r_input_id)
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repetitied_token_type_ids.append(r_token_type_id)
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if after_dup_len > rep_seq_len:
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rep_seq_len = after_dup_len
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# Padding the data to the same length
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for batch_id in range(batch_size):
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after_dup_len = len(repetitied_input_ids[batch_id])
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pad_len = rep_seq_len - after_dup_len
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repetitied_input_ids[batch_id] += [0] * pad_len
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repetitied_token_type_ids[batch_id] += [0] * pad_len
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return paddle.to_tensor(repetitied_input_ids), paddle.to_tensor(repetitied_token_type_ids)
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