# -*- coding: utf-8 -*- """ @Time : 2023/1/31 19:02 @Author : @FileName: @Software: @Describe: """ import os # os.environ["TF_KERAS"] = "1" import pandas as pd os.environ["CUDA_VISIBLE_DEVICES"] = "0" import json import numpy as np from bert4keras.backend import keras, set_gelu from bert4keras.tokenizers import Tokenizer, load_vocab from bert4keras.models import build_transformer_model from bert4keras.optimizers import Adam, extend_with_piecewise_linear_lr from bert4keras.snippets import sequence_padding, DataGenerator from bert4keras.snippets import open from keras.layers import Lambda, Dense import tensorflow as tf from keras.backend import set_session from sklearn.metrics.pairwise import cosine_similarity from rouge import Rouge # pip install rouge from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction from tqdm import tqdm import jieba from gensim.models import KeyedVectors, word2vec, Word2Vec import random import difflib config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config)) # 此处不同 class Word2vecModel: def __init__(self): self.path = "E:\pycharm_workspace\查重分析\word2vec_model\\word2vec_add_new_18.model" self.model = Word2Vec.load(self.path) def word2vec_res(self,seg_0_list, seg_1_list): sentence_0_list = [] sentence_1_list = [] for i in seg_0_list: a = self.model.wv[i] sentence_0_list.append(a) for i in seg_1_list: a = self.model.wv[i] sentence_1_list.append(a) return sentence_0_list, sentence_1_list class Evaluator(keras.callbacks.Callback): """评估与保存 """ def __init__(self): self.rouge = Rouge() self.smooth = SmoothingFunction().method1 self.best_bleu = 0. # def on_epoch_end(self, epoch, logs=None): # metrics = self.evaluate(valid_data) # 评测模型 # if metrics['bleu'] > self.best_bleu: # self.best_bleu = metrics['bleu'] # model.save_weights('./best_model.weights') # 保存模型 # metrics['best_bleu'] = self.best_bleu # print('valid_data:', metrics) def evaluate_t(self, data_1, data_2, topk=1): total = 0 rouge_1, rouge_2, rouge_l, bleu = 0, 0, 0, 0 scores = self.rouge.get_scores(hyps=[data_1], refs=[data_2]) rouge_1 += scores[0]['rouge-1']['f'] rouge_2 += scores[0]['rouge-2']['f'] rouge_l += scores[0]['rouge-l']['f'] bleu += sentence_bleu( references=[data_1.split(' ')], hypothesis=data_2.split(' '), smoothing_function=self.smooth ) # rouge_1 /= total # rouge_2 /= total # rouge_l /= total # bleu /= total return [rouge_1, rouge_2, rouge_l, bleu] class bertModel: def __init__(self): self.config_path = '../chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_config.json' self.checkpoint_path = '../chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_model.ckpt' self.dict_path = '../chinese_roberta_wwm_ext_L-12_H-768_A-12/vocab.txt' self.token_dict, self.keep_tokens = load_vocab( dict_path=self.dict_path, simplified=True, startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], ) self.tokenizer = Tokenizer(self.token_dict, do_lower_case=True) self.buildmodel() def buildmodel(self): bert = build_transformer_model( config_path=self.config_path, checkpoint_path=self.checkpoint_path, return_keras_model=False, ) output = Lambda(lambda x: x[:, 0], name='CLS-token')(bert.model.output) self.model = keras.models.Model(bert.model.input, output) self.model.summary() def predict(self,text): batch_token_ids, batch_segment_ids = [], [] token_ids, segment_ids = self.tokenizer.encode(text, maxlen=256) batch_token_ids.append(token_ids) batch_segment_ids.append(segment_ids) return self.model.predict([batch_token_ids, batch_segment_ids]) def simbert(data_1, data_2): pass def word2vec(): pass def bleu(): pass if __name__ == '__main__': file = "../data/train_yy_zong_sim_99.txt" sim_value = [1, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0] model = bertModel() eval_class = Evaluator() # word2vecmodel = Word2vecModel() try: with open(file, 'r', encoding="utf-8") as f: lines = [x.strip() for x in f if x.strip() != ''] except: with open(file, 'r', encoding="gbk") as f: lines = [x.strip() for x in f if x.strip() != ''] bertsim_list = [] bleusim_list = [] word2vecsim_list = [] data_train_text = [] random.shuffle(lines) print(len(lines)) for txt in tqdm(lines): text = txt.split('\t') if len(text) == 3: data_1 = text[0] data_2 = text[2] str_sim_value = difflib.SequenceMatcher(None, data_1, data_2).quick_ratio() # if len(data_2) - len(data_1) < 0 and len(data_2) / len(data_1) > 0.8: # num_yu = 1 - len(data_2) / len(data_1) # str_sim_value = 1 - str_sim_value * num_yu if 1 >= str_sim_value > 0.95: data_train_text.append([data_1, data_2, str(str_sim_value), "1-0.95"]) elif 0.95 >= str_sim_value > 0.9: data_train_text.append([data_1, data_2, str(str_sim_value), "0.95-0.9"]) elif 0.9 >= str_sim_value > 0.85: data_train_text.append([data_1, data_2, str(str_sim_value), "0.9-0.85"]) elif 0.85 >= str_sim_value > 0.8: data_train_text.append([data_1, data_2, str(str_sim_value), "0.85-0.8"]) elif 0.8 >= str_sim_value > 0.75: data_train_text.append([data_1, data_2, str(str_sim_value), "0.8-0.75"]) elif 0.75 >= str_sim_value > 0.7: data_train_text.append([data_1, data_2, str(str_sim_value), "0.75-0.7"]) else: data_train_text.append([data_1, data_2, str(str_sim_value), "0.7 - 0"]) data_train_text = sorted(data_train_text, key=lambda x:x[2], reverse=True) df = pd.DataFrame(data_train_text) print(df) df.to_csv("../data/yy改写相似度_1.csv", index=None) df.to_excel("../data/yy改写相似度_1.xlsx", index=None)