【关系抽取-R-BERT】加载数据集

西西嘛呦

发布于 2021-03-16 06:54:20

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代码可运行

文章被收录于专栏：数据分析与挖掘数据分析与挖掘

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代码可运行

认识数据集

Component-Whole(e2,e1)	The system as described above has its greatest application in an arrayed <e1> configuration </e1> of antenna <e2> elements </e2>.
Other	The <e1> child </e1> was carefully wrapped and bound into the <e2> cradle </e2> by means of a cord.
Instrument-Agency(e2,e1)	The <e1> author </e1> of a keygen uses a <e2> disassembler </e2> to look at the raw assembly code.
Other	A misty <e1> ridge </e1> uprises from the <e2> surge </e2>.
Member-Collection(e1,e2)	The <e1> student </e1> <e2> association </e2> is the voice of the undergraduate student population of the State University of New York at Buffalo.
Other	This is the sprawling <e1> complex </e1> that is Peru's largest <e2> producer </e2> of silver.
Cause-Effect(e2,e1)	The current view is that the chronic <e1> inflammation </e1> in the distal part of the stomach caused by Helicobacter pylori <e2> infection </e2> results in an increased acid production from the non-infected upper corpus region of the stomach.
Entity-Destination(e1,e2)	<e1> People </e1> have been moving back into <e2> downtown </e2>.
Content-Container(e1,e2)	The <e1> lawsonite </e1> was contained in a <e2> platinum crucible </e2> and the counter-weight was a plastic crucible with metal pieces.
Entity-Destination(e1,e2)	The solute was placed inside a beaker and 5 mL of the <e1> solvent </e1> was pipetted into a 25 mL glass <e2> flask </e2> for each trial.
Member-Collection(e1,e2)	The fifty <e1> essays </e1> collected in this <e2> volume </e2> testify to most of the prominent themes from Professor Quispel's scholarly career.
Other	Their <e1> composer </e1> has sunk into <e2> oblivion </e2>.

该数据是SemEval2010 Task8数据集，数据，具体介绍可以参考：https://blog.csdn.net/qq_29883591/article/details/88567561

处理数据相关代码：data_loader.py

import copy
import csv
import json
import logging
import os

import torch
from torch.utils.data import TensorDataset

from utils import get_label

logger = logging.getLogger(__name__)


class InputExample(object):
    """
    A single training/test example for simple sequence classification.

    Args:
        guid: Unique id for the example.
        text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
        label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """

    def __init__(self, guid, text_a, label):
        self.guid = guid
        self.text_a = text_a
        self.label = label

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class InputFeatures(object):
    """
    A single set of features of data.

    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
    """

    def __init__(self, input_ids, attention_mask, token_type_ids, label_id, e1_mask, e2_mask):
        self.input_ids = input_ids
        self.attention_mask = attention_mask
        self.token_type_ids = token_type_ids
        self.label_id = label_id
        self.e1_mask = e1_mask
        self.e2_mask = e2_mask

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class SemEvalProcessor(object):
    """Processor for the Semeval data set """

    def __init__(self, args):
        self.args = args
        self.relation_labels = get_label(args)

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text_a = line[1]
            label = self.relation_labels.index(line[0])
            if i % 1000 == 0:
                logger.info(line)
            examples.append(InputExample(guid=guid, text_a=text_a, label=label))
        return examples

    def get_examples(self, mode):
        """
        Args:
            mode: train, dev, test
        """
        file_to_read = None
        if mode == "train":
            file_to_read = self.args.train_file
        elif mode == "dev":
            file_to_read = self.args.dev_file
        elif mode == "test":
            file_to_read = self.args.test_file

        logger.info("LOOKING AT {}".format(os.path.join(self.args.data_dir, file_to_read)))
        return self._create_examples(self._read_tsv(os.path.join(self.args.data_dir, file_to_read)), mode)


processors = {"semeval": SemEvalProcessor}


def convert_examples_to_features(
    examples,
    max_seq_len,
    tokenizer,
    cls_token="[CLS]",
    cls_token_segment_id=0,
    sep_token="[SEP]",
    pad_token=0,
    pad_token_segment_id=0,
    sequence_a_segment_id=0,
    add_sep_token=False,
    mask_padding_with_zero=True,
):
    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 5000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))

        tokens_a = tokenizer.tokenize(example.text_a)

        e11_p = tokens_a.index("<e1>")  # the start position of entity1
        e12_p = tokens_a.index("</e1>")  # the end position of entity1
        e21_p = tokens_a.index("<e2>")  # the start position of entity2
        e22_p = tokens_a.index("</e2>")  # the end position of entity2

        # Replace the token
        tokens_a[e11_p] = "$"
        tokens_a[e12_p] = "$"
        tokens_a[e21_p] = "#"
        tokens_a[e22_p] = "#"

        # Add 1 because of the [CLS] token
        e11_p += 1
        e12_p += 1
        e21_p += 1
        e22_p += 1

        # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        if add_sep_token:
            special_tokens_count = 2
        else:
            special_tokens_count = 1
        if len(tokens_a) > max_seq_len - special_tokens_count:
            tokens_a = tokens_a[: (max_seq_len - special_tokens_count)]

        tokens = tokens_a
        if add_sep_token:
            tokens += [sep_token]

        token_type_ids = [sequence_a_segment_id] * len(tokens)

        tokens = [cls_token] + tokens
        token_type_ids = [cls_token_segment_id] + token_type_ids

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
        attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_len - len(input_ids)
        input_ids = input_ids + ([pad_token] * padding_length)
        attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
        token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)

        # e1 mask, e2 mask
        e1_mask = [0] * len(attention_mask)
        e2_mask = [0] * len(attention_mask)

        for i in range(e11_p, e12_p + 1):
            e1_mask[i] = 1
        for i in range(e21_p, e22_p + 1):
            e2_mask[i] = 1

        assert len(input_ids) == max_seq_len, "Error with input length {} vs {}".format(len(input_ids), max_seq_len)
        assert len(attention_mask) == max_seq_len, "Error with attention mask length {} vs {}".format(
            len(attention_mask), max_seq_len
        )
        assert len(token_type_ids) == max_seq_len, "Error with token type length {} vs {}".format(
            len(token_type_ids), max_seq_len
        )

        label_id = int(example.label)

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % example.guid)
            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
            logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label_id))
            logger.info("e1_mask: %s" % " ".join([str(x) for x in e1_mask]))
            logger.info("e2_mask: %s" % " ".join([str(x) for x in e2_mask]))

        features.append(
            InputFeatures(
                input_ids=input_ids,
                attention_mask=attention_mask,
                token_type_ids=token_type_ids,
                label_id=label_id,
                e1_mask=e1_mask,
                e2_mask=e2_mask,
            )
        )

    return features


def load_and_cache_examples(args, tokenizer, mode):
    processor = processors[args.task](args)

    # Load data features from cache or dataset file
    cached_features_file = os.path.join(
        args.data_dir,
        "cached_{}_{}_{}_{}".format(
            mode,
            args.task,
            list(filter(None, args.model_name_or_path.split("/"))).pop(),
            args.max_seq_len,
        ),
    )

    if os.path.exists(cached_features_file):
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        if mode == "train":
            examples = processor.get_examples("train")
        elif mode == "dev":
            examples = processor.get_examples("dev")
        elif mode == "test":
            examples = processor.get_examples("test")
        else:
            raise Exception("For mode, Only train, dev, test is available")

        features = convert_examples_to_features(
            examples, args.max_seq_len, tokenizer, add_sep_token=args.add_sep_token
        )
        logger.info("Saving features into cached file %s", cached_features_file)
        torch.save(features, cached_features_file)

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
    all_e1_mask = torch.tensor([f.e1_mask for f in features], dtype=torch.long)  # add e1 mask
    all_e2_mask = torch.tensor([f.e2_mask for f in features], dtype=torch.long)  # add e2 mask

    all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)

    dataset = TensorDataset(
        all_input_ids,
        all_attention_mask,
        all_token_type_ids,
        all_label_ids,
        all_e1_mask,
        all_e2_mask,
    )
    return dataset

这里面用到了utils.py中的get_label函数：

def get_label(args):
    return [label.strip() for label in open(os.path.join(args.data_dir, args.label_file), "r", encoding="utf-8")]

其中label.txt中的内容如下：

Other
Cause-Effect(e1,e2)
Cause-Effect(e2,e1)
Instrument-Agency(e1,e2)
Instrument-Agency(e2,e1)
Product-Producer(e1,e2)
Product-Producer(e2,e1)
Content-Container(e1,e2)
Content-Container(e2,e1)
Entity-Origin(e1,e2)
Entity-Origin(e2,e1)
Entity-Destination(e1,e2)
Entity-Destination(e2,e1)
Component-Whole(e1,e2)
Component-Whole(e2,e1)
Member-Collection(e1,e2)
Member-Collection(e2,e1)
Message-Topic(e1,e2)
Message-Topic(e2,e1)

最后是这么使用的：

import argparse

from data_loader import load_and_cache_examples
from trainer import Trainer
from utils import init_logger, load_tokenizer, set_seed

def main(args):
    init_logger()
    set_seed(args)
    tokenizer = load_tokenizer(args)

    train_dataset = load_and_cache_examples(args, tokenizer, mode="train")

其中用到了utils.py中的init_logger，load_tokenizer,set_seed：

import logging
import os
import random

import numpy as np
import torch
from transformers import BertTokenizer


ADDITIONAL_SPECIAL_TOKENS = ["<e1>", "</e1>", "<e2>", "</e2>"]


def get_label(args):
    return [label.strip() for label in open(os.path.join(args.data_dir, args.label_file), "r", encoding="utf-8")]


def load_tokenizer(args):
    tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
    tokenizer.add_special_tokens({"additional_special_tokens": ADDITIONAL_SPECIAL_TOKENS})
    return tokenizer

其中使用的相关参数的定义如下：

 parser = argparse.ArgumentParser()

    parser.add_argument("--task", default="semeval", type=str, help="The name of the task to train")
    parser.add_argument(
        "--data_dir",
        default="./data",
        type=str,
        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
    )
    parser.add_argument("--model_dir", default="./model", type=str, help="Path to model")
    parser.add_argument(
        "--eval_dir",
        default="./eval",
        type=str,
        help="Evaluation script, result directory",
    )
    parser.add_argument("--train_file", default="train.tsv", type=str, help="Train file")
    parser.add_argument("--test_file", default="test.tsv", type=str, help="Test file")
    parser.add_argument("--label_file", default="label.txt", type=str, help="Label file")

    parser.add_argument(
        "--model_name_or_path",
        type=str,
        default="bert-base-uncased",
        help="Model Name or Path",
    )

    parser.add_argument("--seed", type=int, default=77, help="random seed for initialization")
    parser.add_argument("--train_batch_size", default=16, type=int, help="Batch size for training.")
    parser.add_argument("--eval_batch_size", default=32, type=int, help="Batch size for evaluation.")
    parser.add_argument(
        "--max_seq_len",
        default=384,
        type=int,
        help="The maximum total input sequence length after tokenization.",
    )
    parser.add_argument(
        "--learning_rate",
        default=2e-5,
        type=float,
        help="The initial learning rate for Adam.",
    )
    parser.add_argument(
        "--num_train_epochs",
        default=10.0,
        type=float,
        help="Total number of training epochs to perform.",
    )
    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument(
        "--max_steps",
        default=-1,
        type=int,
        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
    parser.add_argument(
        "--dropout_rate",
        default=0.1,
        type=float,
        help="Dropout for fully-connected layers",
    )

    parser.add_argument("--logging_steps", type=int, default=250, help="Log every X updates steps.")
    parser.add_argument(
        "--save_steps",
        type=int,
        default=250,
        help="Save checkpoint every X updates steps.",
    )

    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the test set.")
    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
    parser.add_argument(
        "--add_sep_token",
        action="store_true",
        help="Add [SEP] token at the end of the sentence",
    )

    args = parser.parse_args()

    main(args)

分步解析数据处理代码

使用的时候是调用load_and_cache_examples(args, tokenizer, mode)函数，其中args参数用于传入初始化的一些参数设置，tokenizer用于将字或符号转换为相应的数字,mode用于标识是训练数据还是验证或者测试数据。
在load_and_cache_examples函数中首先调用processorsargs.task，这个processors是一个字典，字典的键是数据集名称，值是处理该数据集的函数名，当我们使用其它的数据集的时候，我们也要在这里面添加相关的键值对表示。
随后将args参数传入到SemEvalProcessor()函数中。该函数的作用就是生成每一个样本，每一个样本用InputExample类表示，包括样本的唯一标识，文本，标签，最后返回的是包含多个InputExample的列表。
随后通过 convert_examples_to_features( examples, args.max_seq_len, tokenizer, add_sep_token=args.add_sep_token ) 针对于每一个example，都要求得： input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, label_id=label_id, e1_mask=e1_mask, e2_mask=e2_mask, 然后封装成一个InputFeatures，最后返回一个包含多个InputFeatures的列表。其中还有一些细节我们要清楚的：
- 需要将实体<e1>、</e1>用$表示，实体<e2>、</e2>用#表示
- 由于加入了[cls]，因此其对应的索引位置要+1
- 是否需要加入[sep]时要考虑
- 句子不够长要进行填补，句子太长了要进行截断
最后我们得到相关的列表： dataset = TensorDataset( all_input_ids, all_attention_mask, all_token_type_ids, all_label_ids, all_e1_mask, all_e2_mask, ) 将其转换成TensorDataset并返回。

最后的结果

03/14/2021 08:37:37 - INFO - data_loader -   Creating features from dataset file at ./data
03/14/2021 08:37:37 - INFO - data_loader -   LOOKING AT ./data/train.tsv
03/14/2021 08:37:37 - INFO - data_loader -   ['Component-Whole(e2,e1)', 'The system as described above has its greatest application in an arrayed <e1> configuration </e1> of antenna <e2> elements </e2>.']
03/14/2021 08:37:37 - INFO - data_loader -   ['Entity-Destination(e1,e2)', 'A basic <e1> data entry </e1> has been added to the <e2> database </e2>.']
03/14/2021 08:37:37 - INFO - data_loader -   ['Cause-Effect(e2,e1)', "It's hell in the hospitals where the amputees's <e1> screaming </e1> after the <e2> lapse </e2> of morphine is heard all the time."]
03/14/2021 08:37:37 - INFO - data_loader -   ['Cause-Effect(e2,e1)', 'Ironically, the <e1> damage </e1> caused by the <e2> floods </e2>, and the subsequent insurance payout, were what prompted the restoration of the station building.']
03/14/2021 08:37:37 - INFO - data_loader -   ['Other', 'Workers at a Devon hospital are due to strike on 5 January for two days in a <e1> dispute </e1> over sick <e2> pay </e2>.']
03/14/2021 08:37:37 - INFO - data_loader -   ['Product-Producer(e2,e1)', 'The <e1> factory </e1> produced several versions of the RAF-2203 <e2> minibuses </e2> based on GAZ-24.']
03/14/2021 08:37:37 - INFO - data_loader -   ['Other', 'A solar calendar is a calendar whose <e1> dates </e1> indicate the <e2> position </e2> of the earth on its revolution around the sun.']
03/14/2021 08:37:37 - INFO - data_loader -   ['Cause-Effect(e2,e1)', 'The author clearly got a great deal of <e1> pleasure </e1> from the <e2> work </e2> and did not allow his vast amount of material to force him into shallow generalizations.']
03/14/2021 08:37:37 - INFO - data_loader -   Writing example 0 of 8000
03/14/2021 08:37:37 - INFO - data_loader -   *** Example ***
03/14/2021 08:37:37 - INFO - data_loader -   guid: train-0
03/14/2021 08:37:37 - INFO - data_loader -   tokens: [CLS] the system as described above has its greatest application in an array ##ed $ configuration $ of antenna # elements # .
03/14/2021 08:37:37 - INFO - data_loader -   input_ids: 101 1996 2291 2004 2649 2682 2038 2049 4602 4646 1999 2019 9140 2098 1002 9563 1002 1997 13438 1001 3787 1001 1012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/14/2021 08:37:37 - INFO - data_loader -   attention_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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