| import pickle as pickle
|
| import os
|
| import pandas as pd
|
| import torch
|
|
|
|
|
| class RE_Dataset(torch.utils.data.Dataset):
|
| def __init__(self, tokenized_dataset, labels):
|
| self.tokenized_dataset = tokenized_dataset
|
| self.labels = labels
|
|
|
| def __getitem__(self, idx):
|
| item = {key: val[idx].clone().detach() for key, val in self.tokenized_dataset.items()}
|
| item['labels'] = torch.tensor(self.labels[idx])
|
| return item
|
|
|
| def __len__(self):
|
| return len(self.labels)
|
|
|
|
|
|
|
| def preprocessing_dataset(dataset, label_type):
|
| label = []
|
| for i in dataset[8]:
|
| if i == 'blind':
|
| label.append(100)
|
| else:
|
| label.append(label_type[i])
|
| out_dataset = pd.DataFrame({'sentence':dataset[1],'entity_01':dataset[2],'entity_02':dataset[5],'label':label,})
|
| return out_dataset
|
|
|
|
|
| def load_data(dataset_dir):
|
|
|
| with open('/opt/ml/input/data/label_type.pkl', 'rb') as f:
|
| label_type = pickle.load(f)
|
|
|
| dataset = pd.read_csv(dataset_dir, delimiter='\t', header=None)
|
|
|
| dataset = preprocessing_dataset(dataset, label_type)
|
|
|
| return dataset
|
|
|
|
|
|
|
|
|
| def tokenized_dataset(dataset, tokenizer):
|
| concat_entity = []
|
| for e01, e02 in zip(dataset['entity_01'], dataset['entity_02']):
|
| 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=100,
|
| add_special_tokens=True,
|
| )
|
| return tokenized_sentences
|
|
|
