'O', 'B-ORG', 'B-MISC', 'B-PER', 'I-PER', 'B-LOC', 'I-ORG', 'I-MISC',
'I-LOC'
}
for i in range(len(return_list)):
if not return_list[i] in check_set:
return_list[i] = 'O'
print("There is a error check: ", tag, " in index of ", i)
return return_list
bert_vote_list = clean_prediction(bert_vote_list)
elmo_vote_list = clean_prediction(elmo_vote_list)
xlnet_vote_list = clean_prediction(xlnet_vote_list)
print("--------------BERT EVALUATION---------------")
print(evaluate(real, bert_vote_list))
print("-------------------END----------------------")
print("--------------ELMO EVALUATION---------------")
print(evaluate(real, elmo_vote_list))
print("-------------------END----------------------")
print("--------------XLNET EVALUATION---------------")
print(evaluate(real, xlnet_vote_list))
print("-------------------END----------------------")
def get_position_tag(tag_str):
dash_index = tag_str.find("-")
if dash_index == -1:
return tag_str
result = np.reshape(scores, (-1, len(labels)))
id_result = np.argmax(result, axis=1)
la_result = [tag_dictionary.get_item_for_index(i) for i in id_result]
return la_result
#################BERT+ELMO MODEL#########################
print(
"============================TWO MODEL:BERT+ELMO================================="
)
print("****** avg prediction ******")
avg_pred = get_mix_preds_by_result([bert_poss, elmo_poss], "avg")
print(evaluate(real, avg_pred))
print("****** confidence prediction ******")
confidence_pred = get_mix_preds_by_result([bert_poss, elmo_poss], "confidence")
print(evaluate(real, confidence_pred))
print(
"============================TWO MODEL:BERT+ELMO================================="
)
#################BERT+ELMO MODEL#########################
#################BERT+XLNET MODEL#########################
print(
"============================TWO MODEL:BERT+XLNET================================="
)
print("****** avg prediction ******")
avg_pred = get_mix_preds_by_result([bert_poss, xlnet_poss], "avg")
print(evaluate(real, avg_pred))
test_file='test.txt',
dev_file='valid.txt')
print(corpus)
tag_type = 'ner'
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
print(tag_dictionary)
model = SequenceTagger.load(ARGS.path + '/best-model.pt')
pred = []
real = []
for sentence in corpus.test:
for token in sentence.tokens:
real.append(token.get_tag("ner").value)
def model_prediction(model):
model_pred = []
for sentence in corpus.test:
model.predict(sentence)
for token in sentence.tokens:
model_pred.append(token.get_tag("ner").value)
return model_pred
print("****** prediction ******")
mdoel_pred = model_prediction(model)
print(evaluate(real, mdoel_pred))
e_b_x_read = pd.read_csv('result/tag/e_b_x.csv')
ebx_vote_list = e_b_x_read['0'].values.tolist()
x_f_e_read = pd.read_csv('result/tag/x_f_e.csv')
xfe_vote_list = x_f_e_read['0'].values.tolist()
b_f_x_read = pd.read_csv('result/tag/b_f_x.csv')
bfx_vote_list = b_f_x_read['0'].values.tolist()
b_e_f_read = pd.read_csv('result/tag/b_e_f.csv')
bef_vote_list = b_e_f_read['0'].values.tolist()
x_f_e_b_read = pd.read_csv('result/tag/x_f_e_b.csv')
xfeb_vote_list = x_f_e_b_read['0'].values.tolist()
print("Already read all required file!")
print("--------------BERT EVALUATION---------------")
print(evaluate(real, bert_vote_list))
print("-------------------END----------------------")
print("--------------ELMO EVALUATION---------------")
print(evaluate(real, elmo_vote_list))
print("-------------------END----------------------")
print("--------------XLNET EVALUATION---------------")
print(evaluate(real, xlnet_vote_list))
print("-------------------END----------------------")
print("--------------POOL-FLAIR EVALUATION---------------")
# print(evaluate(real, flair_f_vote_list))
print(evaluate(real, flair_vote_list))
print("-------------------END----------------------")
models = [bert_tagger, elmo_tagger, flair_tagger, xlnet_tagger]
ensemble_tagger = EnsembleTagger(models=models,
tag_type=tag_type,
mode='loss')
if ARGS.train:
trainer: ModelTrainer = ModelTrainer(ensemble_tagger, corpus)
trainer.train(model_path,
learning_rate=ARGS.lr,
mini_batch_size=ARGS.batch_size,
max_epochs=ARGS.epoch)
real = []
for sentence in corpus.test:
for token in sentence.tokens:
real.append(token.get_tag("ner").value)
def test(model, data):
results = []
for sentence in data:
model.predict(sentence, all_tag_prob=True)
for token in sentence.tokens:
results.append(token.get_tag("ner").value)
return results
ensemble_pred = test(ensemble_tagger, corpus.test)
print(evaluate(real, ensemble_pred))
xlnet512_poss_list = np.reshape(xlnet512_poss, (-1, len(labels)))
#################BERT+ELMO+XLNET MODEL#########################
print(
"============================THREE MODEL:BERT+ELMO+XLNET================================="
)
confidence_vote_lists = []
for i in range(len(bert_poss_list)):
confidence_vote_lists.append(
[bert_poss_list[i], xlnet_poss_list[i], elmo_poss_list[i]])
confidence_predict = []
for confidence_vote_list in confidence_vote_lists:
confidence_predict.append(confidence_vote(confidence_vote_list))
print(evaluate(real, confidence_predict))
print(
"============================THREE MODEL:BERT+ELMO+XLNET================================="
)
#################BERT+ELMO+XLNET MODEL#########################
#################BERT+ELMO+FLAIR MODEL#########################
print(
"============================THREE MODEL:BERT+ELMO+FLAIR================================="
)
confidence_vote_lists = []
for i in range(len(bert_poss_list)):
confidence_vote_lists.append(
[bert_poss_list[i], flair_poss_list[i], elmo_poss_list[i]])
confidence_predict = []
use_crf=True)
# model = SequenceTagger.load('./log/elmo/best-model.pt')
#
# from conlleval import evaluate
#
pred = []
real = []
for sentence in corpus.test:
for token in sentence.tokens:
real.append(token.get_tag("ner").value)
def model_prediction(model):
model_pred = []
for sentence in corpus.test:
model.predict(sentence)
for token in sentence.tokens:
model_pred.append(token.get_tag("ner").value)
return model_pred
from eval.conlleval import evaluate
pool_flair_model = SequenceTagger.load(
'./log/pool_flair_f_20200330002549/best-model.pt')
print("****** pool_flair prediction ******")
pool_flair_pred = model_prediction(pool_flair_model)
print(evaluate(real, pool_flair_pred))