66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
import pickle
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from datasets import load_dataset, 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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#
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tokenizer = AutoTokenizer.from_pretrained('roberta-base')
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#
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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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#
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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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def get_dataset_dict(dataset):
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with open(dataset) as f_in:
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next(f_in)
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d = dict()
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d['year'] = list()
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d['text'] = list()
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for l in f_in:
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y,t = l.rstrip().split('\t')
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d['year'].append(y)
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d['text'].append(t)
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return d
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train_dataset = Dataset.from_dict(get_dataset_dict('train_huggingface_format.csv')).map(tokenize_function, batched=True).shuffle(seed=42)
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eval_dataset_full = Dataset.from_dict(get_dataset_dict('dev-0_huggingface_format.csv')).map(tokenize_function, batched=True).shuffle(seed=42)
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eval_dataset_small = eval_dataset_full.select(range(2000))
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test_dataset_A = Dataset.from_dict(get_dataset_dict('test-A_huggingface_format.csv')).map(tokenize_function, batched=True).shuffle(seed=42)
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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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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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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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with open('train_dataset.pickle','wb') as f_p:
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pickle.dump(train_dataset, f_p)
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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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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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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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with open('scalers.pickle','wb') as f_p:
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pickle.dump(scalers, f_p)
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