simbert文本相似語義召回；保存及在線服務(wù)https://blog.csdn.net/weixin_42357472/article/details/116205077

SimBERT（基于UniLM思想、融檢索與生成于一體的BERT模型）【主要應(yīng)用場景：相似文本生成、相似文本檢索】
https://blog.csdn.net/u013250861/article/details/123649047

import numpy as np import os from collections import Counter os.environ['TF_KERAS'] = '1' from bert4keras.backend import keras, K from bert4keras.models import build_transformer_model from bert4keras.tokenizers import Tokenizer from bert4keras.snippets import sequence_padding from bert4keras.snippets import uniout from keras.models import Modelmaxlen = 32# bert配置 # bert配置 config_path = r'D:***t\chinese_simbert_L-6_H-384_A-12\bert_config.json' checkpoint_path = r'D:\*****rt\chinese_simbert_L-6_H-384_A-12\bert_model.ckpt' dict_path = r'D:\****rt\chinese_simbert_L-6_H-384_A-12\vocab.txt'# 建立分詞器 tokenizer = Tokenizer(dict_path, do_lower_case=True) # 建立分詞器# 建立加載模型 bert = build_transformer_model(config_path,checkpoint_path,with_pool='linear',application='unilm',return_keras_model=False, )encoder = keras.models.Model(bert.model.inputs, bert.model.outputs[0])import pandas as pd datas1 = pd.read_csv(r'D:****raw_datas150.csv') datas_all = list(datas1["title"])# 測試相似度效果 data = datas_all a_token_ids, b_token_ids, labels = [], [], [] texts = []for d in data:token_ids = tokenizer.encode(d, maxlen=maxlen)[0]a_token_ids.append(token_ids) # token_ids = tokenizer.encode(d[1], maxlen=maxlen)[0] # b_token_ids.append(token_ids) # labels.append(d[2])texts.append(d)a_token_ids = sequence_padding(a_token_ids) # b_token_ids = sequence_padding(b_token_ids) a_vecs = encoder.predict([a_token_ids, np.zeros_like(a_token_ids)],verbose=True) # b_vecs = encoder.predict([b_token_ids, np.zeros_like(b_token_ids)], # verbose=True) # labels = np.array(labels)a_vecs = a_vecs / (a_vecs**2).sum(axis=1, keepdims=True)**0.5print(type(a_vecs)) np.save("sim_all_datas.npy",a_vecs)#import numpy as np #a_vecsss = np.load(r"D:\tcl\simbert\sim_all_datas.npy")def most_similar(text, topn=10):"""檢索最相近的topn個(gè)句子"""token_ids, segment_ids = tokenizer.encode(text, max_length=maxlen)print(token_ids, segment_ids )vec = encoder.predict([[token_ids], [segment_ids]])[0]vec /= (vec**2).sum()**0.5sims = np.dot(a_vecsss, vec)return [(i, datas_all[i], sims[i]) for i in sims.argsort()[::-1][:topn]]kk=["海綿寶寶"] mmm = [] for i in kk:results = most_similar(i, 10)mmm.append([i,results])print(i,results) from paddlenlp import Taskflow similarity = Taskflow("text_similarity") [2022-03-22 15:17:18,306] [ INFO] - Downloading model_state.pdparams from [https://bj.bcebos.com/paddlenlp/taskflow/text_similarity/simbert-base-chinese/model_state.pdparams](https://bj.bcebos.com/paddlenlp/taskflow/text_similarity/simbert-base-chinese/model_state.pdparams) 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 615M/615M [00:29<00:00, 22.1MB/s] [2022-03-22 15:17:51,977] [ INFO] - Downloading model_config.json from [https://bj.bcebos.com/paddlenlp/taskflow/text_similarity/simbert-base-chinese/model_config.json](https://bj.bcebos.com/paddlenlp/taskflow/text_similarity/simbert-base-chinese/model_config.json) 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 334/334 [00:00<00:00, 197kB/s] [2022-03-22 15:17:52,154] [ INFO] - Downloading https://bj.bcebos.com/paddlenlp/models/transformers/simbert/vocab.txt and saved to /root/.paddlenlp/models/simbert-base-chinese [2022-03-22 15:17:52,154] [ INFO] - Downloading vocab.txt from [https://bj.bcebos.com/paddlenlp/models/transformers/simbert/vocab.txt](https://bj.bcebos.com/paddlenlp/models/transformers/simbert/vocab.txt) 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63.4k/63.4k [00:00<00:00, 744kB/s] [2022-03-22 15:18:10,818] [ INFO] - Weights from pretrained model not used in BertModel: ['cls.predictions.decoder_bias', 'cls.predictions.transform.weight', 'cls.predictions.transform.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder_weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias'] [2022-03-22 15:18:12,113] [ INFO] - Converting to the inference model cost a little time. [2022-03-22 15:18:30,093] [ INFO] - The inference model save in the path:/root/.paddlenlp/taskflow/text_similarity/simbert-base-chinese/static/inference

總結(jié)

以上是生活随笔為你收集整理的simbert文本相似度，短文本语义匹配模型的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。