def predict_single_sentence(text):
# 测试句子
word_labels, seq_types = PreProcessInputData([text])
# 模型预测
predicted = model.predict([word_labels, seq_types])
y = np.argmax(predicted[0], axis=1)
predict_tag = [id_label_dict[_] for _ in y]
return predict_tag[1:-1]
def get_geo():
return_result = {"code": 200, "message": "success", "data": []}
try:
text = request.get_json()["text"].replace(" ", "")
word_labels, seq_types = PreProcessInputData([text])
# 模型预测
predicted = ner_model.predict([word_labels, seq_types])
y = np.argmax(predicted[0], axis=1)
tag = [id_label_dict[_] for _ in y]
# 输出预测结果
result = bio_to_json(text, tag[1:-1])
return_result["data"] = result
except Exception:
return_result["code"] = 400
return_result["message"] = traceback.format_exc()
return json.dumps(return_result, ensure_ascii=False, indent=2)
def get_text_predict(text):
new_text = []
for word in text.split():
new_text.extend(bert_encode(word))
word_labels, seq_types = PreProcessInputData([new_text])
# 模型预测
predicted = model.predict([word_labels, seq_types])
y = np.argmax(predicted[0], axis=1)
tags = [id_label_dict[_] for _ in y]
# 输出预测结果
real_tag = []
i = 1
for word in text.split():
new_word = bert_encode(word)
if i < len(tags):
real_tag.append(tags[i])
i += len(new_word)
return real_tag
if tag[2:] != tags[c_idx-1][2:] or tags[c_idx-1][2:] == 'O':
tags[c_idx] = 'O'
pass
else:
entity_name = entity_name + c
if tag_next[2:] != entity_tag:
item["entities"].append({"word": entity_name, "start": entity_start, "end": iCount + 1, "type": entity_tag})
entity_name = ''
iCount += 1
return item
# 加载训练好的模型
custom_objects = get_custom_objects()
for key, value in {'CRF': CRF, 'crf_loss': crf_loss, 'crf_accuracy': crf_accuracy}.items():
custom_objects[key] = value
model = load_model("%s_ner.h5" % event_type, custom_objects=custom_objects)
# 测试句子
text = "经过工作人员两天的反复验证、严密测算，记者昨天从上海中心大厦得到确认：被誉为上海中心大厦“定楼神器”的阻尼器，在8月10日出现自2016年正式启用以来的最大摆幅。"
word_labels, seq_types = PreProcessInputData([text])
# 模型预测
predicted = model.predict([word_labels, seq_types])
y = np.argmax(predicted[0], axis=1)
tag = [id_label_dict[_] for _ in y]
# 输出预测结果
result = bio_to_json(text, tag[1:-1])
pprint(result)