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datasets.py
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datasets.py
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#!/usr/bin/env python3
'''
This module contains our Dataset classes and functions to load the 3 datasets we're using.
You should only need to call load_multitask_data to get the training and dev examples
to train your model.
'''
import csv
import torch
from torch.utils.data import Dataset
from tokenizer import BertTokenizer
def preprocess_string(s):
return ' '.join(s.lower()
.replace('.', ' .')
.replace('?', ' ?')
.replace(',', ' ,')
.replace('\'', ' \'')
.split())
class SentenceClassificationDataset(Dataset):
def __init__(self, dataset, args):
self.dataset = dataset
self.p = args
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
self.task_id = 0
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx]
def get_task_id(self):
return self.task_id
def pad_data(self, data):
sents = [x[0] for x in data]
labels = [x[1] for x in data]
sent_ids = [x[2] for x in data]
encoding = self.tokenizer(sents, return_tensors='pt', padding=True, truncation=True)
token_ids = torch.LongTensor(encoding['input_ids'])
attention_mask = torch.LongTensor(encoding['attention_mask'])
token_type_ids = torch.LongTensor(encoding['token_type_ids'])
labels = torch.LongTensor(labels)
return token_ids, attention_mask, labels, sents, sent_ids, token_type_ids
def collate_fn(self, all_data):
token_ids, attention_mask, labels, sents, sent_ids, token_type_ids= self.pad_data(all_data)
batched_data = {
'task_id': self.get_task_id(),
'token_ids': token_ids,
'attention_mask': attention_mask,
'token_type_ids':token_type_ids,
'labels': labels,
'sents': sents,
'sent_ids': sent_ids
}
return batched_data
class SentenceClassificationTestDataset(Dataset):
def __init__(self, dataset, args):
self.dataset = dataset
self.p = args
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
self.task_id = 0
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx]
def get_task_id(self):
return self.task_id
def pad_data(self, data):
sents = [x[0] for x in data]
sent_ids = [x[1] for x in data]
encoding = self.tokenizer(sents, return_tensors='pt', padding=True, truncation=True)
token_ids = torch.LongTensor(encoding['input_ids'])
attention_mask = torch.LongTensor(encoding['attention_mask'])
token_type_ids = torch.LongTensor(encoding['token_type_ids'])
return token_ids, attention_mask, sents, sent_ids, token_type_ids
def collate_fn(self, all_data):
token_ids, attention_mask, sents, sent_ids, token_type_ids= self.pad_data(all_data)
batched_data = {
'task_id': self.get_task_id(),
'token_ids': token_ids,
'attention_mask': attention_mask,
'token_type_ids':token_type_ids,
'sents': sents,
'sent_ids': sent_ids
}
return batched_data
class SentencePairDataset(Dataset):
def __init__(self, dataset, args, isRegression =False):
self.dataset = dataset
self.p = args
self.isRegression = isRegression
self.task_id = 2 if isRegression else 1
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx]
def get_task_id(self):
return self.task_id
def pad_data(self, data):
sent1 = [x[0] for x in data]
sent2 = [x[1] for x in data]
labels = [x[2] for x in data]
sent_ids = [x[3] for x in data]
encoding = self.tokenizer(text=sent1,text_pair=sent2, return_tensors='pt', padding=True, truncation=True)
token_ids = torch.LongTensor(encoding['input_ids'])
attention_mask = torch.LongTensor(encoding['attention_mask'])
token_type_ids = torch.LongTensor(encoding['token_type_ids'])
if self.isRegression:
labels = torch.DoubleTensor(labels)
else:
labels = torch.LongTensor(labels)
return (token_ids, token_type_ids, attention_mask,
labels,sent_ids)
def collate_fn(self, all_data):
(token_ids, token_type_ids, attention_mask,
labels, sent_ids) = self.pad_data(all_data)
batched_data = {
'task_id': self.get_task_id(),
'token_ids': token_ids,
'token_type_ids': token_type_ids,
'attention_mask': attention_mask,
'labels': labels,
'sent_ids': sent_ids
}
return batched_data
class SentencePairTestDataset(Dataset):
def __init__(self, dataset, args, isRegression =False):
self.dataset = dataset
self.p = args
self.isRegression = isRegression #Added parameter although is not used in the test set for its original purpose
#But just to use the get_task_id method like in the other class
self.task_id = 2 if isRegression else 1
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx]
def get_task_id(self):
return self.task_id
def pad_data(self, data):
sent1 = [x[0] for x in data]
sent2 = [x[1] for x in data]
sent_ids = [x[2] for x in data]
encoding = self.tokenizer(text=sent1,text_pair=sent2, return_tensors='pt', padding=True, truncation=True)
token_ids = torch.LongTensor(encoding['input_ids'])
attention_mask = torch.LongTensor(encoding['attention_mask'])
token_type_ids = torch.LongTensor(encoding['token_type_ids'])
return (token_ids, token_type_ids, attention_mask,
sent_ids)
def collate_fn(self, all_data):
(token_ids, token_type_ids, attention_mask,
sent_ids) = self.pad_data(all_data)
batched_data = {
'task_id': self.get_task_id(),
'token_ids': token_ids,
'token_type_ids': token_type_ids,
'attention_mask': attention_mask,
'sent_ids': sent_ids
}
return batched_data
def load_multitask_test_data():
paraphrase_filename = f'data/quora-test.csv'
sentiment_filename = f'data/ids-sst-test.txt'
similarity_filename = f'data/sts-test.csv'
sentiment_data = []
with open(sentiment_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent = record['sentence'].lower().strip()
sentiment_data.append(sent)
print(f"Loaded {len(sentiment_data)} test examples from {sentiment_filename}")
paraphrase_data = []
with open(paraphrase_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
#if record['split'] != split:
# continue
paraphrase_data.append((preprocess_string(record['sentence1']),
preprocess_string(record['sentence2']),
))
print(f"Loaded {len(paraphrase_data)} test examples from {paraphrase_filename}")
similarity_data = []
with open(similarity_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
similarity_data.append((preprocess_string(record['sentence1']),
preprocess_string(record['sentence2']),
))
print(f"Loaded {len(similarity_data)} test examples from {similarity_filename}")
return sentiment_data, paraphrase_data, similarity_data
def load_multitask_data(sentiment_filename,paraphrase_filename,similarity_filename,split='train'):
sentiment_data = []
num_labels = {}
if split == 'test':
with open(sentiment_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent = record['sentence'].lower().strip()
sent_id = record['id'].lower().strip()
sentiment_data.append((sent,sent_id))
else:
with open(sentiment_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent = record['sentence'].lower().strip()
sent_id = record['id'].lower().strip()
label = int(record['sentiment'].strip())
if label not in num_labels:
num_labels[label] = len(num_labels)
sentiment_data.append((sent, label,sent_id))
print(f"Loaded {len(sentiment_data)} {split} examples from {sentiment_filename}")
paraphrase_data = []
if split == 'test':
with open(paraphrase_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent_id = record['id'].lower().strip()
paraphrase_data.append((preprocess_string(record['sentence1']),
preprocess_string(record['sentence2']),
sent_id))
else:
with open(paraphrase_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
try:
sent_id = record['id'].lower().strip()
paraphrase_data.append((preprocess_string(record['sentence1']),
preprocess_string(record['sentence2']),
int(float(record['is_duplicate'])),sent_id))
except:
pass
print(f"Loaded {len(paraphrase_data)} {split} examples from {paraphrase_filename}")
similarity_data = []
if split == 'test':
with open(similarity_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent_id = record['id'].lower().strip()
similarity_data.append((preprocess_string(record['sentence1']),
preprocess_string(record['sentence2'])
,sent_id))
else:
with open(similarity_filename, 'r', encoding='utf-8') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent_id = record['id'].lower().strip()
similarity_data.append((preprocess_string(record['sentence1']),
preprocess_string(record['sentence2']),
float(record['similarity']),sent_id))
print(f"Loaded {len(similarity_data)} {split} examples from {similarity_filename}")
return sentiment_data, num_labels, paraphrase_data, similarity_data