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97dcc01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | from torch.utils.data import Dataset
import torch
import pandas as pd
from sklearn.model_selection import train_test_split
def load_data(args, split):
df = pd.read_csv(f"{args.data_root}/{split}.csv")
texts = df['text'].astype(str).values.tolist()
# 测试集test.csv没有target列
if 'target' in df.columns:
labels = df['target'].values.tolist()
else:
labels = None
return texts, labels
class MyDataset(Dataset):
def __init__(self, data, tokenizer, max_length, is_test):
self.tokenizer = tokenizer
self.max_length = max_length
self.texts = data[0]
self.labels = data[1] if len(data) > 1 else None
self.is_test = is_test
def __len__(self):
"""returns the length of dataframe"""
return len(self.texts)
def __getitem__(self, index):
"""return the input ids, attention masks and target ids"""
text = str(self.texts[index])
source = self.tokenizer.batch_encode_plus(
[text],
max_length=self.max_length,
# pad_to_max_length=True,
truncation=True,
padding="max_length",
return_tensors="pt",
)
source_ids = source["input_ids"].squeeze()
source_mask = source["attention_mask"].squeeze()
data_sample = {
"input_ids": source_ids,
"attention_mask": source_mask,
}
# 仅当不是测试集且 labels 可用时附上 labels
if (not self.is_test) and (self.labels is not None):
label = self.labels[index]
data_sample["labels"] = torch.tensor(label).squeeze()
return data_sample
if __name__ == "__main__":
df = pd.read_csv('data/train_src.csv')
train_df, val_df = train_test_split(df, test_size=0.1, stratify=df['target'], random_state=42)
train_df.to_csv('data/train.csv', index=False)
val_df.to_csv('data/val.csv', index=False)
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