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"""
This file contains functions for loading various needed data
"""
import json
import torch
import random
import logging
import os
from random import random as rand
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
logger = logging.getLogger(__name__)
local_file = os.path.split(__file__)[-1]
logging.basicConfig(
format='%(asctime)s : %(filename)s : %(funcName)s : %(levelname)s : %(message)s',
level=logging.INFO)
def load_acronym_kb(kb_path='acronym_kb.json'):
f = open(kb_path, encoding='utf8')
acronym_kb = json.load(f)
for key, values in acronym_kb.items():
values = [v for v, s in values]
acronym_kb[key] = values
logger.info('loaded acronym dictionary successfully, in total there are [{a}] acronyms'.format(a=len(acronym_kb)))
return acronym_kb
def get_candidate(acronym_kb, short_term, can_num=10):
return acronym_kb[short_term][:can_num]
def load_data(path):
data = list()
for line in open(path, encoding='utf8'):
row = json.loads(line)
data.append(row)
return data
def load_dataset(data_path):
all_short_term, all_long_term, all_context = list(), list(), list()
for line in open(data_path, encoding='utf8'):
obj = json.loads(line)
short_term, long_term, context = obj['short_term'], obj['long_term'], ' '.join(obj['tokens'])
all_short_term.append(short_term)
all_long_term.append(long_term)
all_context.append(context)
return {'short_term': all_short_term, 'long_term': all_long_term, 'context':all_context}
def load_pretrain(data_path):
all_short_term, all_long_term, all_context = list(), list(), list()
cnt = 0
for line in open(data_path, encoding='utf8'):
cnt += 1
# row = line.strip().split('\t')
# if len(row) != 3:continue
if cnt>200:continue
obj = json.loads(line)
short_term, long_term, context = obj['short_term'], obj['long_term'], ' '.join(obj['tokens'])
all_short_term.append(short_term)
all_long_term.append(long_term)
all_context.append(context)
return {'short_term': all_short_term, 'long_term': all_long_term, 'context': all_context}
class TextData(Dataset):
def __init__(self, data):
self.all_short_term = data['short_term']
self.all_long_term = data['long_term']
self.all_context = data['context']
def __len__(self):
return len(self.all_short_term)
def __getitem__(self, idx):
return self.all_short_term[idx], self.all_long_term[idx], self.all_context[idx]
def random_negative(target, elements):
flag, result = True, ''
while flag:
temp = random.choice(elements)
if temp != target:
result = temp
flag = False
return result
class SimpleLoader():
def __init__(self, batch_size, tokenizer, kb, shuffle=True):
self.batch_size = batch_size
self.shuffle = shuffle
self.tokenizer = tokenizer
self.kb = kb
def collate_fn(self, batch_data):
pos_tag, neg_tag = 0, 1
batch_short_term, batch_long_term, batch_context = list(zip(*batch_data))
batch_short_term, batch_long_term, batch_context = list(batch_short_term), list(batch_long_term), list(batch_context)
batch_negative, batch_label, batch_label_neg = list(), list(), list()
for index in range(len(batch_short_term)):
short_term, long_term, context = batch_short_term[index], batch_long_term[index], batch_context[index]
batch_label.append(pos_tag)
candidates = [v[0] for v in self.kb[short_term]]
if len(candidates) == 1:
batch_negative.append(long_term)
batch_label_neg.append(pos_tag)
continue
negative = random_negative(long_term, candidates)
batch_negative.append(negative)
batch_label_neg.append(neg_tag)
prompt = batch_context + batch_context
long_terms = batch_long_term + batch_negative
label = batch_label + batch_label_neg
encoding = self.tokenizer(prompt, long_terms, return_tensors="pt", padding=True, truncation=True)
label = torch.LongTensor(label)
return encoding, label
def __call__(self, data_path):
dataset = load_dataset(data_path=data_path)
dataset = TextData(dataset)
train_iterator = DataLoader(dataset=dataset, batch_size=self.batch_size // 2, shuffle=self.shuffle,
collate_fn=self.collate_fn)
return train_iterator
def mask_subword(subword_sequences, prob=0.15, masked_prob=0.8, VOCAB_SIZE=30522):
PAD, CLS, SEP, MASK, BLANK = 0, 101, 102, 103, -100
masked_labels = list()
for sentence in subword_sequences:
labels = [BLANK for _ in range(len(sentence))]
original = sentence[:]
end = len(sentence)
if PAD in sentence:
end = sentence.index(PAD)
for pos in range(end):
if sentence[pos] in (CLS, SEP): continue
if rand() > prob: continue
if rand() < masked_prob: # 80%
sentence[pos] = MASK
elif rand() < 0.5: # 10%
sentence[pos] = random.randint(0, VOCAB_SIZE-1)
labels[pos] = original[pos]
masked_labels.append(labels)
return subword_sequences, masked_labels
class AcroBERTLoader():
def __init__(self, batch_size, tokenizer, kb, shuffle=True, masked_prob=0.15, hard_num=2):
self.batch_size = batch_size
self.shuffle = shuffle
self.tokenizer = tokenizer
self.masked_prob = masked_prob
self.hard_num = hard_num
self.kb = kb
self.all_long_terms = list()
for vs in self.kb.values():
self.all_long_terms.extend(list(vs))
def select_negative(self, target):
selected, flag, max_time = None, True, 10
if target in self.kb:
long_term_candidates = self.kb[target]
if len(long_term_candidates) == 1:
long_term_candidates = self.all_long_terms
else:
long_term_candidates = self.all_long_terms
attempt = 0
while flag and attempt < max_time:
attempt += 1
selected = random.choice(long_term_candidates)
if selected != target:
flag = False
if attempt == max_time:
selected = random.choice(self.all_long_terms)
return selected
def collate_fn(self, batch_data):
batch_short_term, batch_long_term, batch_context = list(zip(*batch_data))
pos_samples, neg_samples, masked_pos_samples = list(), list(), list()
for _ in range(self.hard_num):
temp_pos_samples = [batch_long_term[index] + ' [SEP] ' + batch_context[index] for index in range(len(batch_long_term))]
neg_long_terms = [self.select_negative(st) for st in batch_short_term]
temp_neg_samples = [neg_long_terms[index] + ' [SEP] ' + batch_context[index] for index in range(len(batch_long_term))]
temp_masked_pos_samples = [batch_long_term[index] + ' [SEP] ' + batch_context[index] for index in range(len(batch_long_term))]
pos_samples.extend(temp_pos_samples)
neg_samples.extend(temp_neg_samples)
masked_pos_samples.extend(temp_masked_pos_samples)
return pos_samples, masked_pos_samples, neg_samples
def __call__(self, data_path):
dataset = load_pretrain(data_path=data_path)
logger.info('loaded dataset, sample = {a}'.format(a=len(dataset['short_term'])))
dataset = TextData(dataset)
train_iterator = DataLoader(dataset=dataset, batch_size=self.batch_size // (2 * self.hard_num), shuffle=self.shuffle,
collate_fn=self.collate_fn)
return train_iterator
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