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load_data.py
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import pickle as pickle
import os
import pandas as pd
import torch
import tqdm
from utils import make_entity_ids
import numpy as np
class RE_Dataset(torch.utils.data.Dataset):
""" Dataset 구성을 위한 class."""
def __init__(self, pair_dataset, labels):
self.pair_dataset = pair_dataset
self.labels = labels
def __getitem__(self, idx):
item = {key: val[idx].clone().detach() for key, val in self.pair_dataset.items()}
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
class RBERT_Dataset(torch.utils.data.Dataset):
""" Dataset 구성을 위한 class."""
def __init__(self, pair_dataset, labels,sub_ids,obj_ids):
self.pair_dataset = pair_dataset
self.labels = labels
self.sub_ids = sub_ids
self.obj_ids = obj_ids
def __getitem__(self, idx):
item = {key: val[idx].clone().detach() for key, val in self.pair_dataset.items()}
item['labels'] = torch.tensor(self.labels[idx])
item['sub_ids'] = torch.tensor(self.sub_ids[idx])
item['obj_ids'] = torch.tensor(self.obj_ids[idx])
return item
def __len__(self):
return len(self.labels)
class Preprocess:
def __init__(self, path):
self.data = self.load_data(path)
def load_data(self, path):
data = pd.read_csv(path)
return data
def label_to_num(self, label):
num_label = []
with open('./NLP_dataset/dict_label_to_num.pkl', 'rb') as f:
dict_label_to_num= pickle.load(f)
for val in label:
num_label.append(dict_label_to_num[val])
return num_label
def tokenized_dataset(self, dataset, tokenizer,type=False,test=False):
if type == 'rbert':
sub_list = []
obj_list = []
for sent in dataset['sentence']:
sub_id,obj_id = make_entity_ids.make_ent_ids(tokenizer,sent)
sub_list.append(sub_id)
obj_list.append(obj_id)
if test:
tmp = []
for e01,e02,e03,e04 in zip(dataset['subject_entity'],dataset['object_entity'],dataset['subject_type'],dataset['object_type']):
ex = f"@*{e03}*{e01}@ 와(과) #^{e04}^{e02}# 의 관계"
tmp.append(ex)
tokenized_sentences = tokenizer(
tmp,
list(dataset['sentence']),
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=256,
add_special_tokens=True,
)
else:
tokenized_sentences = tokenizer(
list(dataset['sentence']),
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=256,#160
add_special_tokens=True,
)
return tokenized_sentences,sub_list,obj_list
elif type == 'entity':
print(dataset['sentence'].iloc[0:10])
entity_loc_ids = []
entity_type_ids = []
for sent, sub_type, obj_type in zip(dataset['sentence'], dataset['subject_type'], dataset['object_type']):
current_entity_loc_ids, current_entity_type_ids = make_entity_ids.make_entity_ids(sentence=sent, tokenizer=tokenizer)
entity_loc_ids.append(current_entity_loc_ids)
entity_type_ids.append(current_entity_type_ids)
tokenized_sentences = tokenizer(
list(dataset['sentence']),
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=256,
add_special_tokens=True,
)
tokenized_sentences['entity_loc_ids'] = torch.LongTensor(entity_loc_ids)
tokenized_sentences['entity_type_ids'] = torch.LongTensor(entity_type_ids)
return tokenized_sentences
elif type == 'xlm':
concat_entity = []
for e01, e02 in zip(dataset['subject_entity'], dataset['object_entity']):
temp = ''
temp = e01 + '[SEP]' + e02
concat_entity.append(temp)
tokenized_sentences = tokenizer(
concat_entity,
list(dataset['sentence']),
return_tensors="pt",
padding=True,
truncation=True,
max_length=256,
add_special_tokens=True,
)
return tokenized_sentences
else:
if test:
tmp = []
for e01,e02,e03,e04 in zip(dataset['subject_entity'],dataset['object_entity'],dataset['subject_type'],dataset['object_type']):
ex = f"@*{e03}*{e01}@ 와(과) #^{e04}^{e02}# 의 관계"
tmp.append(ex)
tokenized_sentences = tokenizer(
tmp,
list(dataset['sentence']),
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=256,
add_special_tokens=True,
)
else:
tokenized_sentences = tokenizer(
list(dataset['sentence']),
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=256,
add_special_tokens=True,
)
return tokenized_sentences