68 lines
1.8 KiB
Python
Executable File
68 lines
1.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import gzip
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import logging
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import lzma
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from typing import List
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from simpletransformers.classification import ClassificationModel
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from tqdm import tqdm
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MAPPER_LABERL2ID = {
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'F': 0,
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'M': 1,
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}
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MAPPER_ID2LABEL = dict([(v, k) for k, v in MAPPER_LABERL2ID.items()])
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logger = logging.getLogger(__name__)
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def open_file(path, *args):
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if path.endswith('gz'):
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fopen = gzip.open
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elif path.endswith('xz'):
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fopen = lzma.open
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else:
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fopen = open
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return fopen(path, *args)
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def load_test(path: str) -> List[str]:
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data = []
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logger.info(f'Loading {path}')
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with open_file(path, 'rt') as f:
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for line in tqdm(f):
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line = line.strip()
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data.append(line)
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return data
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if __name__ == '__main__':
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logging.basicConfig(level=logging.INFO)
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transformers_logger = logging.getLogger('transformers')
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#transformers_logger.setLevel(logging.WARNING)
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args = {
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'train_batch_size': 200,
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'num_train_epochs': 2,
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'evaluate_during_training': True,
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'save_steps': 15000,
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'evaluate_during_training_steps': 15000,
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}
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model = ClassificationModel('roberta', 'outputs/best_model',
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num_labels=2, args=args)
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output_name = 'model=base'
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for test_name in ['dev-0', 'dev-1', 'test-A']:
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logger.info(f'START TESTING {test_name}')
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test_data = load_test(f'data/{test_name}/in.tsv.xz')
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predictions, raw_outputs = model.predict(test_data)
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logger.info(f'Saving predictions')
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with open_file(f'data/{test_name}/out-{output_name}.tsv', 'wt') as w:
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for prediction in predictions:
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label_name = MAPPER_ID2LABEL[prediction]
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w.write(f'{label_name}\n')
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