Precision_negative /= samples_with_negative_judgement
Precision_positivie /= samples_with_positive_judgement
msg += "samples_with_negative_judgement: {}\n".format(
samples_with_negative_judgement
)
msg += "samples_with_positive_judgement: {}\n".format(
samples_with_positive_judgement
)
msg += "MRR_negative: {}\n".format(MRR_negative)
msg += "MRR_positive: {}\n".format(MRR_positive)
msg += "Precision_negative: {}\n".format(Precision_negative)
msg += "Precision_positivie: {}\n".format(Precision_positivie)
logger.info("\n" + msg + "\n")
print("\n" + msg + "\n")
# dump pickle with the result of the experiment
all_results = dict(
list_of_results=list_of_results, global_MRR=MRR, global_P_at_10=Precision
)
with open("{}/result.pkl".format(log_directory), "wb") as f:
pickle.dump(all_results, f)
return Precision1
if __name__ == "__main__":
parser = options.get_eval_KB_completion_parser()
args = options.parse_args(parser)
main(args)
sentences = [[json_test_official[index]['claim']]]
print("\n{}:".format(model_name))
contextual_embeddings, sentence_lengths, tokenized_text_list = model.get_contextual_embeddings(
sentences)
x_test_official[index] = contextual_embeddings[11][0][0]
print(tokenized_text_list)
return (x_train, json_train, x_test, json_test, x_test_official,
json_test_official)
if __name__ == '__main__':
parser = options.get_general_parser()
args = options.parse_args(parser) #We pass the command --lm bert
x_train, json_train, x_test, json_test, x_test_official, json_test_official = main(
args) #Save the datasets
#We create a numpy array with the labels of the datasets
y_train = np.zeros((len(json_train), 1))
for index in range(len(json_train)):
if json_train[index]['label'] == 'SUPPORTS':
y_train[index] = 1
else:
y_train[index] = 0
y_train = np.ravel(y_train) #Reshaping the array
y_train = y_train.astype(int) #Convert the array into int64
#We do the same for the test dataset
