Python client.BertClient方法代碼示例

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def getVectors(passages, filename):
    i = 1
    bc = BertClient()
    passage = 'In June 1942, the United States Army Corps of Engineersbegan the Manhattan Project- The secret name for the 2 atomic bombs.'
    print("Testing bc\nTesting passages:{}\nVector:{}".format(passage, bc.encode([passage])[0]))
    passagePack = []
    passageIDs = []
    packSize = 100
    with open(filename,'w') as w:
        for passageID in passages:
            if i % 100 == 0:
                print('{} vectors retrieved'.format((i-1)*packSize))
            if len(passagePack) == packSize:
                response = bc.encode(passagePack)
                w.write(process(passageIDs, response))
                passageIDs = []
                passagePack = []
                i += 1
            passageIDs.append(passageID)
            passagePack.append(passages[passageID]) 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def __init__(self, architecture_path=None, weights_path=None):
        self.bc = None
        try: 
            self.bc = BertClient() 
        except:
            raise Exception("PunchlineExtractor: Cannot instantiate BertClient. Is it running???")

        # check if we're loading in a pre-trained model
        if architecture_path is not None:
            assert(weights_path is not None)
            
            with open(architecture_path) as model_arch:
                model_arch_str = model_arch.read()
                self.model = model_from_json(model_arch_str)

            self.model.load_weights(weights_path)
        else:
            self.build_model() 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def __init__(self, component_config=None):
        super(BertVectorsFeaturizer, self).__init__(component_config)
        ip = self.component_config['ip']
        port = self.component_config['port']
        port_out = self.component_config['port_out']
        show_server_config = self.component_config['show_server_config']
        output_fmt = self.component_config['output_fmt']
        check_version = self.component_config['check_version']
        timeout = self.component_config['timeout']
        identity = self.component_config['identity']
        self.bc = BertClient(
            ip=ip,
            port=int(port),
            port_out=int(port_out),
            show_server_config=show_server_config,
            output_fmt=output_fmt,
            check_version=check_version,
            timeout=int(timeout),
            identity=identity
        ) 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def __init__(self):
        super(ActionGetFAQAnswer, self).__init__()
        self.bc = BertClient()
        self.faq_data = json.load(open("./data/nlu/faq.json", "rt", encoding="utf-8"))
        self.standard_questions_encoder = np.load("./data/standard_questions.npy")
        self.standard_questions_encoder_len = np.load("./data/standard_questions_len.npy")
        print(self.standard_questions_encoder.shape) 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def encode_standard_question():
    bc = BertClient()
    data = json.load(open("./data/nlu/faq.json", "rt", encoding="utf-8"))
    standard_questions = [each['q'] for each in data]
    print("Standard question size", len(standard_questions))
    print("Start to calculate encoder....")
    standard_questions_encoder = bc.encode(standard_questions)
    np.save("./data/standard_questions", standard_questions_encoder)
    standard_questions_encoder_len = np.sqrt(np.sum(standard_questions_encoder * standard_questions_encoder, axis=1))
    np.save("./data/standard_questions_len", standard_questions_encoder_len) 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def getVectors(queries, filename):
    i = 1
    bc = BertClient()
    query = 'who founded microsoft'
    print("Testing bc\nTesting Query:{}\nVector:{}".format(query, bc.encode([query])[0]))
    with open(filename,'w') as w:
        for j in range(0,len(queries), 100):
            if i % 100 == 0:
                print('{} vectors retrieved'.format(i*100))
            queryPack = queries[j:j+100]
            response = bc.encode(queryPack)
            w.write(process(queryPack, response))
            i += 1 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def client_clone(id, idx):
    bc = BertClient(port=int(sys.argv[1]), port_out=int(sys.argv[2]), identity=id)
    for j in bc.fetch():
        print('clone-client-%d: received %d x %d' % (idx, j.shape[0], j.shape[1])) 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def run(self):
        try:
            from bert_serving.client import BertClient
        except ImportError:
            raise ImportError('BertClient module is not available, it is required for benchmarking.'
                              'Please use "pip install -U bert-serving-client" to install it.')
        with BertClient(port=self.port, port_out=self.port_out,
                        show_server_config=True, check_version=False, check_length=False) as bc:
            time_all = []
            for _ in range(self.num_repeat):
                start_t = time.perf_counter()
                bc.encode(self.batch)
                time_all.append(time.perf_counter() - start_t)
            self.avg_time = mean(time_all[2:])  # first one is often slow due to cold-start/warm-up effect 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def __init__(self):
        """
        This bot tags sequences of words from abstracts as describing
        P,I, or O elements.
        """
        logging.debug("Loading PICO LSTM-CRF")
        config = Config()
        # build model
        self.model = NERModel(config)
        self.model.build()

        self.model.restore_session(os.path.join(robotreviewer.DATA_ROOT, "pico_spans/model.weights/"))
        logging.debug("PICO classifiers loaded")
        self.bert = BertClient() 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def chatbot_sentence_vec_by_bert_bertasserver():
    """bert encode is used bert as server"""
    from conf.path_config import chicken_and_gossip_path
    from bert_serving.client import BertClient
    from utils.text_tools import txtRead
    import numpy as np

    topk = 5
    matrix_ques_save_path = "doc_vecs_chicken_and_gossip"
    questions = txtRead(chicken_and_gossip_path, encodeType='utf-8')
    ques = [ques.split('\t')[0] for ques in questions][0:100]

    bc = BertClient(ip = 'localhost')
    doc_vecs = bc.encode(ques)
    np.savetxt(matrix_ques_save_path, doc_vecs)
    # matrix_ques = np.loadtxt(matrix_ques_save_path)

    while True:
        query = input('你問: ')
        query_vec = bc.encode([query])[0]
        query_bert_vec = np.array(query_bert_vec)
        # compute normalized dot product as score
        score = np.sum(query_vec * doc_vecs, axis=1) / np.linalg.norm(doc_vecs, axis=1)
        topk_idx = np.argsort(score)[::-1][:topk]
        for idx in topk_idx:
            print('小薑機器人回答： %s\t%s' % (score[idx], questions[idx])) 

# 需要導入模塊: from bert_serving import client [as 別名]
# 或者: from bert_serving.client import BertClient [as 別名]
def test():
    print("Loading test data...")
    start_time = time.time()
    x_test = process_file_nolabel(test_dir, word_to_id, config.seq_length)
    x_test = list(x_test)
    bc = BertClient()
    x_test = bc.encode(x_test)
    # (test 2364, 80)
    session = tf.Session()
    session.run(tf.global_variables_initializer())
    saver = tf.train.Saver()
    saver.restore(sess=session, save_path=save_path)  # 讀取保存的模型

    # print('Testing...')
    # loss_test, acc_test = evaluate(session, x_test, y_test)
    # msg = 'Test Loss: {0:>6.2}, Test Acc: {1:>7.2%}'
    # print(msg.format(loss_test, acc_test))

    batch_size = 32
    data_len = len(x_test)
    num_batch = int((data_len - 1) / batch_size) + 1

    # y_test_cls = np.argmax(y_test, 1)
    y_pred_cls = np.zeros(shape=len(x_test), dtype=np.int32)  # 保存預測結果
    for i in range(num_batch):  # 逐批次處理
        start_id = i * batch_size
        end_id = min((i + 1) * batch_size, data_len)
        feed_dict = {
            model.input_x: x_test[start_id:end_id],
            model.keep_prob: 1.0
        }
        y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls, feed_dict=feed_dict)

    # 評估
    print("Precision, Recall and F1-Score...")
    # print(metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories))

    # 混淆矩陣
    print("Confusion Matrix...")
    # cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
    # print(cm)

    time_dif = get_time_dif(start_time)
    print("Time usage:", time_dif)
    return y_pred_cls