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import json
import re
import torch
import numpy as np
from collections import defaultdict
class summarization_data:
def __init__(self, raw_data, tokenizer, domain_adaption=False, wwm_prob=0.1):
data = self.select_data(raw_data)
self.data = self.data_clean(data)
self.len = len(self.data)
self.tokenizer = tokenizer
self.padding_max_len = "max_length"
self.domain_adaption = domain_adaption
self.wwm_prob = wwm_prob
def select_data(self, raw_data):
data = list()
for item in raw_data:
del item['pub_time']
del item['labels']
data.append(item)
return data
def data_clean(self, data):
for item in data:
item["text"] = re.sub(r"[?|!+?|:|(|)]|\\|-|/.*?/|http\S+", "", item["text"].lower())
item["title"] = re.sub(r"[?|!+?|:|(|)]|\\|-|/.*?/|http\S+", "", item["title"].lower())
return data
def __getitem__(self, index):
if self.domain_adaption:
tokenized_text = self.tokenizer(
self.data[index]["text"],
add_special_tokens=True,
padding="max_length",
return_token_type_ids=True,
truncation=True,
)
text_mask = tokenized_text['attention_mask']
input_ids, labels = self._word_masking(self.tokenizer, tokenized_text, self.wwm_prob)
return {
'input_ids': torch.tensor(input_ids),
'attention_mask': torch.tensor(text_mask),
'labels': torch.tensor(labels)
}
else:
tokenized_text = self.tokenizer(
"summarize:"+self.data[index]["text"],
add_special_tokens=True,
padding="max_length",
return_token_type_ids=True,
truncation=True,
)
text_ids = tokenized_text['input_ids']
text_mask = tokenized_text['attention_mask']
tokenized_title = self.tokenizer(
self.data[index]["title"],
padding="max_length",
return_token_type_ids=True,
truncation=True,
)
title_ids = tokenized_title['input_ids']
return {
'input_ids': torch.tensor(text_ids),
'attention_mask': torch.tensor(text_mask),
'labels': torch.tensor(title_ids)
}
def _word_masking(self, tokenizer, tokenized_inputs, wwm_prob):
# randomly mask words
input_ids = tokenized_inputs["input_ids"]
mask = np.random.binomial(1, wwm_prob, (len(input_ids),))
labels = list()
for idx in np.where(mask == 1)[0]:
#add special sentinel tokens
sentinel_token = tokenizer.additional_special_tokens[input_ids[idx] % 100]
labels.append(tokenizer(sentinel_token).input_ids[0])
labels.append(input_ids[idx])
input_ids[idx] = tokenizer(sentinel_token).input_ids[0]
return input_ids, labels
def __len__(self):
return self.len
# from tqdm.auto import tqdm
# from transformers import T5TokenizerFast, T5ForConditionalGeneration
# from transformers import DataCollatorForSeq2Seq
# from torch.utils.data import DataLoader, random_split
# from FTE_NLP.model.summarization_dataset_v1 import *
# from torch.optim import AdamW
# from transformers import get_scheduler
# from FTE_NLP.utils.post_process import *
#
# json_filename = '../data/raw_EDT/Trading_benchmark/evaluate_news_test.json'
# with open(json_filename) as data_file:
# test_data = json.loads(data_file.read())
#
# model_checkpoint = "t5-small"
# tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, model_max_length=512)
# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
#
# all_dataset = summarization_data(test_data, tokenizer, domain_adaption=True, wwm_prob=0.1)
# train_dataset, eval_dataset = random_split(all_dataset, [7, 3], generator=torch.Generator().manual_seed(42))
#
# # data_collator = DataCollatorForSeq2Seq(tokenizer, model,label_pad_token_id=0)
# data_collator = DataCollatorForSeq2Seq(tokenizer,model)
# # pass data to dataloader
# train_params = {'batch_size': 2, 'shuffle': False, 'num_workers': 0}
# train_loader = DataLoader(train_dataset, collate_fn=data_collator, **train_params)
#
# eval_params = {'batch_size': 2, 'shuffle': False, 'num_workers': 0}
# eval_loader = DataLoader(eval_dataset, collate_fn=data_collator, **train_params)
#
#
# for item in train_loader:
# print(item)
# print("input id numbers:",item["input_ids"][0])
# print("input id: ",tokenizer.decode(item["input_ids"][0]))
# print("labels id number:",item["labels"][0])
# print("labels:",tokenizer.decode(item["labels"][0],skip_special_tokens=False))
# break |