53 lines
1.2 KiB
Python
53 lines
1.2 KiB
Python
import pandas as pd
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import fasttext
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X_train = pd.read_csv('train/in.tsv', sep='\t', header=None)
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X_train = X_train[2]
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y_train = pd.read_csv('train/expected.tsv', sep='\t', header=None)
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X_dev = pd.read_csv('dev-0/in.tsv', sep='\t', header=None)
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X_dev = X_dev[2]
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y_dev = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None)
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X_test_A = pd.read_csv('test-A/in.tsv', sep='\t', header=None)
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X_test_A = X_test_A[2]
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X_test_B = pd.read_csv('test-B/in.tsv', sep='\t', header=None)
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X_test_B = X_test_B[2]
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with open('train.txt', 'w', encoding='utf-8') as f:
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for i in range(len(X_train)):
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f.write(f'__label__{y_train[0][i]} {X_train[i]}\n')
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f.close()
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with open('dev.txt', 'w', encoding='utf-8') as f:
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for i in range(len(X_dev)):
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f.write(f'__label__{y_dev[0][i]} {X_dev[i]}\n')
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f.close()
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model = fasttext.train_supervised('train.txt')
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with open('dev-0/out.tsv', 'w') as f:
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for sentence in X_dev:
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f.write(f'{model.predict(sentence)[0][0][9:]}\n')
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f.close()
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with open('test-A/tsv.txt', 'w') as f:
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for sentence in X_test_A:
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f.write(f'{model.predict(sentence)[0][0][9:]}\n')
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f.close()
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with open('test-B/tsv.txt', 'w') as f:
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for sentence in X_test_B:
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f.write(f'{model.predict(sentence)[0][0][9:]}\n')
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f.close()
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