54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
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import pickle
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from datasets import load_dataset
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from transformers import AutoTokenizer
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from tqdm import tqdm
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from sklearn.preprocessing import MinMaxScaler
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import numpy as np
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dataset = load_dataset('csv', sep='\t', data_files={'train': ['./train_huggingface_format.csv'], 'test': ['./dev-0_huggingface_format.csv']})
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test_dataset_A = load_dataset('csv', sep='\t', data_files='test-A_huggingface_format.csv')
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tokenizer = AutoTokenizer.from_pretrained('roberta-base')
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def tokenize_function(examples):
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t = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
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return t
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test_tokenized_datasets_A = test_dataset_A.map(tokenize_function, batched=True)
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tokenized_datasets = dataset.map(tokenize_function, batched=True)
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#train_dataset = tokenized_datasets["train"].shuffle(seed=42)
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eval_dataset_full = tokenized_datasets["test"]
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#eval_dataset_small = tokenized_datasets["test"].select(range(2000))
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#test_dataset_A = test_tokenized_datasets_A["train"]
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#
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#
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#scalers = dict()
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#scalers['year'] = MinMaxScaler().fit(np.array(train_dataset['year']).reshape(-1,1))
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#
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#def add_scaled(example):
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# for factor in ('year',):
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# example[factor + '_scaled'] = scalers[factor].transform(np.array(example[factor]).reshape(-1,1)).reshape(1,-1)[0].item()
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# return example
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#
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#train_dataset = train_dataset.map(add_scaled)
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#eval_dataset_full = eval_dataset_full.map(add_scaled)
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#eval_dataset_small = eval_dataset_small.map(add_scaled)
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##test_dataset_A = test_dataset_A.map(add_scaled)
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#
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#
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#with open('train_dataset.pickle','wb') as f_p:
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# pickle.dump(train_dataset, f_p)
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#
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#with open('eval_dataset_small.pickle','wb') as f_p:
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# pickle.dump(eval_dataset_small, f_p)
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#
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#with open('eval_dataset_full.pickle','wb') as f_p:
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# pickle.dump(eval_dataset_full, f_p)
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#
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#with open('test_dataset_A.pickle','wb') as f_p:
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# pickle.dump(test_dataset_A, f_p)
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#
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#with open('scalers.pickle','wb') as f_p:
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# pickle.dump(scalers, f_p)
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