ireland-news-headlines/roberta_temp/02_load_dataset.py
kubapok d6d7a4dbda a
2021-09-24 15:29:02 +02:00

51 lines
2.0 KiB
Python

import pickle
from datasets import load_dataset
from transformers import AutoTokenizer
from config import MODEL
from tqdm import tqdm
dataset = load_dataset('csv', sep='\t', data_files={'train': ['../train/huggingface_format_year.csv'], 'test': ['../dev-0/huggingface_format_year.csv']})
test_dataset = load_dataset('csv', sep='\t', data_files='../test-A/huggingface_format_year.csv')
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def tokenize_function(examples):
t = tokenizer(examples["text"], padding="max_length", truncation=True)
examples['year'] = [x - 1995 for x in examples['year']]
for column in 'date', 'day_of_month', 'day_of_year', 'month', 'year', 'weekday', 'year_cont':
t[column] = [[a] * b.index(1) + [0] *(len(b) - b.index(1)) for a,b in zip(examples[column], t['input_ids'])]
return t
test_tokenized_datasets = test_dataset.map(tokenize_function, batched=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
#for d in ('train', 'test'):
# for i in tqdm(range(len(tokenized_datasets[d]))):
# tokenized_datasets[d][i][column] = [tokenized_datasets[d][i][column] ] * 512 #len(tokenized_datasets[d][i]['input_ids'])
#
#d = 'train'
#for column in tqdm(('date', 'day_of_month', 'day_of_year', 'month', 'year', 'year_cont')):
# for i in tqdm(range(len(test_tokenized_datasets[d]))):
# test_tokenized_datasets[d][i][column] = [test_tokenized_datasets[d][i][column] ] * 512 #len(test_tokenized_datasets[d][i]['input_ids'])
train_dataset = tokenized_datasets["train"].shuffle(seed=42)
eval_dataset_full = tokenized_datasets["test"]
eval_dataset_small = tokenized_datasets["test"].select(range(2000))
test_dataset = test_tokenized_datasets["train"]
with open('train_dataset.pickle','wb') as f_p:
pickle.dump(train_dataset, f_p)
with open('eval_dataset_small.pickle','wb') as f_p:
pickle.dump(eval_dataset_small, f_p)
with open('eval_dataset_full.pickle','wb') as f_p:
pickle.dump(eval_dataset_full, f_p)
with open('test_dataset.pickle','wb') as f_p:
pickle.dump(test_dataset, f_p)