【关系抽取-R-BERT】加载数据集
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：
```python
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
03/14/2021 08:37:37 - INFO - data_loader - token_type_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 - label: 14 (id = 14)
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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 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
03/14/2021 08:37:37 - INFO - data_loader - token_type_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 - label: 15 (id = 15)
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03/14/2021 08:37:37 - INFO - data_loader - e2_mask: 0 0 0 0 0 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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/14/2021 08:37:40 - INFO - data_loader - Writing example 5000 of 8000
03/14/2021 08:37:42 - INFO - data_loader - Saving features into cached file ./data/cached_train_semeval_bert-base-uncased_384
并生成一个：cached_train_semeval_bert-base-uncased_384文件
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