【關係抽取-R-BERT】載入資料集
阿新 • • 發佈：2021-03-14
# 認識資料集
```
Component-Whole(e2,e1) The system as described above has its greatest application in an arrayed configuration of antenna elements .
Other The child was carefully wrapped and bound into the cradle by means of a cord.
Instrument-Agency(e2,e1) The author of a keygen uses a disassembler to look at the raw assembly code.
Other A misty ridge uprises from the surge .
Member-Collection(e1,e2) The student association is the voice of the undergraduate student population of the State University of New York at Buffalo.
Other This is the sprawling complex that is Peru's largest producer of silver.
Cause-Effect(e2,e1) The current view is that the chronic inflammation in the distal part of the stomach caused by Helicobacter pylori infection People have been moving back into downtown .
Content-Container(e1,e2) The lawsonite was contained in a platinum crucible 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 solvent flask for each trial.
Member-Collection(e1,e2) The fifty essays collected in this volume testify to most of the prominent themes from Professor Quispel's scholarly career.
Other Their composer has sunk into oblivion .
```
該資料是SemEval2010 Task8資料集，資料，具體介紹可以參考：https://blog.csdn.net/qq_29883591/article/details/88567561
# 處理資料相關程式碼：data_loader.py
```python
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("") # the start position of entity1
e12_p = tokens_a.index(" ") # the end position of entity1
e21_p = tokens_a.index("") # the start position of entity2
e22_p = tokens_a.index(" ") # 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
```
這裡面用到了utils.py中的get_label函式：
```
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中的內容如下：
```
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)
```
最後是這麼使用的：
```
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 = ["", " ", "", " "]
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
```
其中使用的相關引數的定義如下：
```python
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)
```
# 分步解析資料處理程式碼
- 使用的時候是呼叫load_and_cache_examples(args, tokenizer, mode)函式，其中args引數用於傳入初始化的一些引數設定，tokenizer用於將字或符號轉換為相應的數字,mode用於標識是訓練資料還是驗證或者測試資料。
- 在load_and_cache_examples函式中首先呼叫processors[args.task](args)，這個processors是一個字典，字典的鍵是資料集名稱，值是處理該資料集的函式名，當我們使用其它的資料集的時候，我們也要在這裡面新增相關的鍵值對錶示。
- 隨後將args引數傳入到SemEvalProcessor()函式中。該函式的作用就是生成每一個樣本，每一個樣本用InputExample類表示，包括樣本的唯一標識，文字，標籤，最後返回的是包含多個InputExample的列表。
- 隨後通過
convert_examples_to_features(
examples, args.max_seq_len, tokenizer, add_sep_token=args.add_sep_token
)
針對於每一個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,
然後封裝成一個InputFeatures，最後返回一個包含多個InputFeatures的列表。
其中還有一些細節我們要清楚的：
- 需要將實體\、\ 用$表示，實體\、\ 用#表示
- 由於加入了[cls]，因此其對應的索引位置要+1
- 是否需要加入[sep]時要考慮
- 句子不夠長要進行填補，句子太長了要進行截斷
- 最後我們得到相關的列表：
dataset = TensorDataset(
all_input_ids,
all_attention_mask,
all_token_type_ids,
all_label_ids,
all_e1_mask,
all_e2_mask,
)
將其轉換成TensorDataset並返回。
# 最後的結果
```python
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 configuration of antenna elements .']
03/14/2021 08:37:37 - INFO - data_loader - ['Entity-Destination(e1,e2)', 'A basic data entry has been added to the database .']
03/14/2021 08:37:37 - INFO - data_loader - ['Cause-Effect(e2,e1)', "It's hell in the hospitals where the amputees's screaming after the lapse of morphine is heard all the time."]
03/14/2021 08:37:37 - INFO - data_loader - ['Cause-Effect(e2,e1)', 'Ironically, the damage caused by the floods , 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 dispute over sick pay .']
03/14/2021 08:37:37 - INFO - data_loader - ['Product-Producer(e2,e1)', 'The factory produced several versions of the RAF-2203 minibuses based on GAZ-24.']
03/14/2021 08:37:37 - INFO - data_loader - ['Other', 'A solar calendar is a calendar whose dates indicate the position 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 pleasure from the work 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)
03/14/2021 08:37:37 - INFO - data_loader - e1_mask: 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 0 0 0
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
```
並生成一個：cached_train_semeval_bert-base-uncased_384檔案
程式碼來源：https://github.com/monolog