具体代码： https://github.com/danan0755/Bert_Classifier/blob/master/Bert_Predict.py
def request_from_raw_text(vocab_file, label2id_file, query, model_key): """ :return: """ text_list = [query] data_list = [] label_list = [] if os.path.exists(label2id_file): with open(label2id_file, 'rb') as rf: label2id = pickle.load(rf) id2label = {value: key for key, value in label2id.items()} label_list = [key for key in label2id.keys()] tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=True) predict_examples = _create_examples(text_list, label2id_file) for (ex_index, example) in enumerate(predict_examples): feature = convert_single_example(ex_index, example, label_list, 128, tokenizer) # ex_index, example, label_list, max_seq_length,tokenizer features = {} features["input_ids"] = feature.input_ids features["input_mask"] = feature.input_mask features["segment_ids"] = feature.segment_ids features["label_ids"] = feature.label_id data_list.append(features) data = json.dumps({"signature_name": "serving_default", "instances": data_list}) headers = {"content-type": "application/json"} json_response = requests.post('http://localhost:8501/v1/models/{}:predict'.format(model_key), data=data, headers=headers) predictions = json.loads(json_response.text) p_list = predictions.get('predictions')[0] label_index = np.argmax(p_list) label = id2label.get(label_index) pred_score = max(p_list) return pred_score, label