def main(qa_path, relations_filepath, data_directory, batch_size,
must_choose_answer, device, trained_to_reject, error_path):
runner = QARunner(qa_path, relations_filepath, data_directory, batch_size,
must_choose_answer, device, trained_to_reject)
em, f1, per_relation_metrics = runner.predict()
save_se_list(runner.se_list, error_path)
return {'em': em, 'f1': f1, 'per_relation_metrics': per_relation_metrics}
def main(qa_path, relations_filepath, data_directory, batch_size,
must_choose_answer, device, trained_to_reject):
runner = QARunner(qa_path,
relations_filepath,
data_directory,
batch_size,
must_choose_answer,
device,
trained_to_reject,
calculate_single_error=False)
em, f1, per_relation_metrics = runner.predict()
print(f'Total samples: {runner.total_samples}')
return {'em': em, 'f1': f1, 'per_relation_metrics': per_relation_metrics}
def main(qa_path, relations_filepath, data_directory, batch_size,
must_choose_answer, device, trained_to_reject):
runner = QARunner(qa_path,
relations_filepath,
data_directory,
batch_size,
must_choose_answer,
device,
trained_to_reject,
calculate_single_error=False)
em, f1, per_relation_metrics = runner.predict()
with open('BERTZSREZSRE.pkl', 'wb') as wf:
pickle.dump(per_relation_metrics, wf)
return {'em': em, 'f1': f1, 'per_relation_metrics': per_relation_metrics}
def main(qa_path, relations_filepath, data_directory, batch_size,
must_choose_answer):
runner = QARunner(qa_path, relations_filepath, data_directory, batch_size,
must_choose_answer)
em, f1, per_relation_metrics = runner.predict()
return {'em': em, 'f1': f1, 'per_relation_metrics': per_relation_metrics}