#! -*- coding: utf-8 -*- import os # os.environ["TF_KERAS"] = "1" os.environ["CUDA_VISIBLE_DEVICES"] = "0" import glob import random from tqdm import tqdm import numpy as np import pandas as pd from bert4keras.backend import keras, K from bert4keras.layers import Loss from bert4keras.models import build_transformer_model from bert4keras.tokenizers import Tokenizer, load_vocab from bert4keras.optimizers import Adam from bert4keras.snippets import sequence_padding, open from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder from keras.models import Model import tensorflow as tf from keras.backend import set_session config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config)) # 此处不同 global graph graph = tf.get_default_graph() sess = tf.Session(graph=graph) set_session(sess) # global graph,model # graph = tf.get_default_graph() # sess = tf.Session(graph=graph) # K.set_session(sess) # 基本参数 class TotalLoss(Loss): """loss分两部分，一是seq2seq的交叉熵，二是相似度的交叉熵。 """ def compute_loss(self, inputs, mask=None): loss1 = self.compute_loss_of_seq2seq(inputs, mask) loss2 = self.compute_loss_of_similarity(inputs, mask) self.add_metric(loss1, name='seq2seq_loss') self.add_metric(loss2, name='similarity_loss') return loss1 + loss2 def compute_loss_of_seq2seq(self, inputs, mask=None): y_true, y_mask, _, y_pred = inputs y_true = y_true[:, 1:] # 目标token_ids y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分 y_pred = y_pred[:, :-1] # 预测序列，错开一位 loss = K.sparse_categorical_crossentropy(y_true, y_pred) loss = K.sum(loss * y_mask) / K.sum(y_mask) return loss def compute_loss_of_similarity(self, inputs, mask=None): _, _, y_pred, _ = inputs y_true = self.get_labels_of_similarity(y_pred) # 构建标签 y_pred = K.l2_normalize(y_pred, axis=1) # 句向量归一化 similarities = K.dot(y_pred, K.transpose(y_pred)) # 相似度矩阵 similarities = similarities - K.eye(K.shape(y_pred)[0]) * 1e12 # 排除对角线 similarities = similarities * 30 # scale loss = K.categorical_crossentropy( y_true, similarities, from_logits=True ) return loss def get_labels_of_similarity(self, y_pred): idxs = K.arange(0, K.shape(y_pred)[0]) idxs_1 = idxs[None, :] idxs_2 = (idxs + 1 - idxs % 2 * 2)[:, None] labels = K.equal(idxs_1, idxs_2) labels = K.cast(labels, K.floatx()) return labels class GenerateModel(object): def __init__(self): self.epoch_acc_vel = 0 self.config_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_config.json' self.checkpoint_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_model.ckpt' self.dict_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/vocab_drop.txt' self.maxlen = 120 self.novel_maxlen = 60 def device_setup(self): token_dict, keep_tokens = load_vocab( dict_path=self.dict_path, simplified=True, startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], ) tokenizer = Tokenizer(token_dict, do_lower_case=True) # model = build_transformer_model( # self.config_path, # self.checkpoint_path, # application='unilm', # keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表 # ) bert = build_transformer_model( self.config_path, self.checkpoint_path, with_pool='linear', application='unilm', keep_tokens=keep_tokens, return_keras_model=False, ) encoder = keras.models.Model(bert.model.inputs, bert.model.outputs[0]) seq2seq = keras.models.Model(bert.model.inputs, bert.model.outputs[1]) # output = CrossEntropy(2)(model.inputs + model.outputs) # # model = Model(model.inputs, output) # model = Model(model.inputs, model.outputs) outputs = TotalLoss([2, 3])(bert.model.inputs + bert.model.outputs) model = keras.models.Model(bert.model.inputs, outputs) path_model = './output_simbert_yy/best_simbertmodel_datasim_yinhao.weights' model.load_weights(path_model) return encoder,seq2seq, tokenizer class CrossEntropy(Loss): """交叉熵作为loss，并mask掉输入部分 """ def compute_loss(self, inputs, mask=None): y_true, y_mask, y_pred = inputs y_true = y_true[:, 1:] # 目标token_ids y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分 y_pred = y_pred[:, :-1] # 预测序列，错开一位 loss = K.sparse_categorical_crossentropy(y_true, y_pred) loss = K.sum(loss * y_mask) / K.sum(y_mask) return loss class AutoTitle(AutoRegressiveDecoder): """seq2seq解码器 """ def __init__(self, model, tokenizer, start_id, end_id, maxlen, minlen=1): super(AutoTitle, self).__init__(start_id, end_id, maxlen, minlen) self.model = model self.tokenizer = tokenizer self.start_id = start_id self.end_id = end_id self.minlen = minlen self.models = {} if start_id is None: self.first_output_ids = np.empty((1, 0), dtype=int) else: self.first_output_ids = np.array([[self.start_id]]) def data_generator(self, inputs, output_ids): batch_token_ids, batch_segment_ids = [], [] if output_ids == []: for txt in inputs: token_ids, segment_ids = self.tokenizer.encode(txt, maxlen=120) batch_token_ids.append(token_ids) batch_segment_ids.append(segment_ids) else: for txt,output_id in zip(inputs, output_ids): token_ids, segment_ids = self.tokenizer.encode(txt, output_id) batch_token_ids.append(token_ids[:-1]) batch_segment_ids.append(segment_ids[:-1]) batch_token_ids = sequence_padding(batch_token_ids) batch_segment_ids = sequence_padding(batch_segment_ids) return batch_token_ids, batch_segment_ids def beam_search_batch(self, inputs, topk, states=None, temperature=1, min_ends=1): """beam search解码 说明：这里的topk即beam size； 返回：最优解码序列。 """ inputs = [np.array([i]) for i in inputs] output_ids, output_scores = self.first_output_ids, np.zeros(1) for step in range(self.maxlen): scores, states = self.predict( inputs, output_ids, states, temperature, 'logits' ) # 计算当前得分 if step == 0: # 第1步预测后将输入重复topk次 inputs = [np.repeat(i, topk, axis=0) for i in inputs] scores = output_scores.reshape((-1, 1)) + scores # 综合累积得分 indices = scores.argpartition(-topk, axis=None)[-topk:] # 仅保留topk indices_1 = indices // scores.shape[1] # 行索引 indices_2 = (indices % scores.shape[1]).reshape((-1, 1)) # 列索引 output_ids = np.concatenate([output_ids[indices_1], indices_2], 1) # 更新输出 output_scores = np.take_along_axis( scores, indices, axis=None ) # 更新得分 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 best = output_scores.argmax() # 得分最大的那个 if is_end[best] and end_counts[best] >= min_ends: # 如果已经终止 return output_ids[best] # 直接输出 else: # 否则，只保留未完成部分 flag = ~is_end | (end_counts < min_ends) # 标记未完成序列 if not flag.all(): # 如果有已完成的 inputs = [i[flag] for i in inputs] # 扔掉已完成序列 output_ids = output_ids[flag] # 扔掉已完成序列 output_scores = output_scores[flag] # 扔掉已完成序列 end_counts = end_counts[flag] # 扔掉已完成end计数 topk = flag.sum() # topk相应变化 # 达到长度直接输出 return output_ids[output_scores.argmax()] def random_sample_batch( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ inputs = [np.array([i for j in i]) for i in inputs] output_ids = self.first_output_ids results = [] for step in range(self.maxlen): probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 probas = np.repeat(probas, n, axis=0) inputs = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n, axis=0) if topk is not None: k_indices = probas.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas = np.take_along_axis(probas, k_indices, axis=1) # topk概率 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 if topp is not None: p_indices = probas.argsort(axis=1)[:, ::-1] # 从高到低排序 probas = np.take_along_axis(probas, p_indices, axis=1) # 排序概率 cumsum_probas = np.cumsum(probas, axis=1) # 累积概率 flag = np.roll(cumsum_probas >= topp, 1, axis=1) # 标记超过topp的部分 flag[:, 0] = False # 结合上面的np.roll，实现平移一位的效果 probas[flag] = 0 # 后面的全部置零 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数 sample_ids = np.apply_along_axis(sample_func, 1, probas) # 执行采样 sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topp is not None: sample_ids = np.take_along_axis( p_indices, sample_ids, axis=1 ) # 对齐原id if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 flag = is_end & (end_counts >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids[flag]: # 存好已完成序列 results.append(ids) flag = (flag == False) # 标记未完成序列 inputs = [i[flag] for i in inputs] # 只保留未完成部分输入 output_ids = output_ids[flag] # 只保留未完成部分候选集 end_counts = end_counts[flag] # 只保留未完成部分end计数 if len(output_ids) == 0: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results def random_sample_and_beam_search( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ whether_end_b = False results_r = [] results_b = [] # index_r = [i for i in range(n)] # index_b = [i for i in range(topk)] index_r = np.arange(n) index_b = np.arange(topk) inputs = [np.array([i]) for i in inputs] output_ids, output_scores = self.first_output_ids, np.zeros(1) results = [] for step in range(self.maxlen): beam_n = len(index_b) probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas = probas / probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 probas = np.repeat(probas, n + topk, axis=0) inputs_r = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n + topk, axis=0) inputs_b = [np.repeat(i, topk, axis=0) for i in inputs] else: if whether_end_b == False: inputs_r = [i[:-beam_n, :] for i in inputs] inputs_b = [i[-beam_n:, :] for i in inputs] else: inputs_r = inputs if whether_end_b == False: probas_r = probas[:-beam_n, :] else: probas_r = probas if step == 0: probas_b = probas[0,:] else: probas_b = probas[-beam_n:, :] if whether_end_b == False: output_ids_r = output_ids[:-beam_n, :] output_ids_b = output_ids[-beam_n:, :] else: output_ids_r = output_ids k_indices = probas_r.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas_r = np.take_along_axis(probas_r, k_indices, axis=1) # topk概率 probas_r /= probas_r.sum(axis=1, keepdims=True) # 重新归一化 if whether_end_b == False: scores = output_scores.reshape((-1, 1)) + probas_b # 综合累积得分 indices = scores.argpartition(-topk, axis=None)[-topk:] # 仅保留topk indices_1 = indices // scores.shape[1] # 行索引 indices_2 = (indices % scores.shape[1]).reshape((-1, 1)) # 列索引 try: output_ids_b = np.concatenate([output_ids_b[indices_1], indices_2], 1) # 更新输出 except: print(output_ids_b.shape) print(indices_1) print(indices_2) exit() output_scores = np.take_along_axis( scores, indices, axis=None ) # 更新得分 sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数 try: sample_ids = np.apply_along_axis(sample_func, 1, probas_r) # 执行采样 except: print(probas_r) sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids_r = np.concatenate([output_ids_r, sample_ids], 1) # 更新输出 # output_ids = np.concatenate([output_ids_r, output_ids_b], 0) if whether_end_b == False: is_end_r = output_ids_r[:, -1] == self.end_id # 标记是否以end标记结束 is_end_b = output_ids_b[:, -1] == self.end_id # 标记是否以end标记结束 else: is_end_r = output_ids_r[:, -1] == self.end_id if whether_end_b == False: end_counts_r = (output_ids_r == self.end_id).sum(1) # 统计出现的end标记 end_counts_b = (output_ids_b == self.end_id).sum(1) # 统计出现的end标记 else: end_counts_r = (output_ids_r == self.end_id).sum(1) # random_serach if output_ids_r.shape[1] >= self.minlen: # 最短长度判断 flag = is_end_r & (end_counts_r >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids_r[flag]: # 存好已完成序列 results_r.append(ids) flag = (flag == False) # 标记未完成序列 try: index_r = index_r[flag] except: print("flag",flag) print("index_r",index_r) inputs_r = [i[flag] for i in inputs_r] # 只保留未完成部分输入 output_ids_r = output_ids_r[flag] # 只保留未完成部分候选集 end_counts_r = end_counts_r[flag] # 只保留未完成部分end计数 # beam_serach if whether_end_b == False: if output_ids_b.shape[1] >= self.minlen: # 最短长度判断 best = output_scores.argmax() # 得分最大的那个 if is_end_b[best] and end_counts_b[best] >= min_ends: # 如果已经终止 results_b.append(output_ids_b[best]) # 直接输出 whether_end_b = True else: # 否则，只保留未完成部分 flag_b = ~is_end_b | (end_counts_b < min_ends) # 标记未完成序列 if not flag_b.all(): # 如果有已完成的 index_b = index_b[flag_b] inputs_b = [i[flag_b] for i in inputs_b] # 扔掉已完成序列 output_ids_b = output_ids_b[flag_b] # 扔掉已完成序列 output_scores = output_scores[flag_b] # 扔掉已完成序列 end_counts_b = end_counts_b[flag_b] # 扔掉已完成end计数 topk = flag_b.sum() # topk相应变化 if whether_end_b == False and len(output_ids_r) != 0: token_r = inputs_r[0] sample_ids_r = inputs_r[1] token_b = inputs_b[0] sample_ids_b = inputs_b[1] token = np.concatenate([token_r,token_b],0) sample_ids = np.concatenate([sample_ids_r,sample_ids_b],0) inputs = [token,sample_ids] output_ids = np.concatenate([output_ids_r, output_ids_b], 0) elif whether_end_b == True and len(output_ids_r) != 0: inputs = inputs_r output_ids = output_ids_r elif whether_end_b == False and len(output_ids_r) == 0: inputs = inputs_b output_ids = output_ids_b else: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results_r, results_b def top_batch( self, inputs_str, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ output_str = [] # token_ids, segment_ids = self.data_generator(inputs, output_ids) batch_nums = len(inputs_str) output_ids =self.first_output_ids_batch = np.empty((batch_nums, 0), dtype=int) results = [[] for i in range(batch_nums)] index_data = [i for i in range(batch_nums)] for step in range(self.maxlen): token_ids, segment_ids = self.data_generator(inputs_str, output_str) inputs = [token_ids, segment_ids] probas = self.predict_batch( inputs ) # 计算当前概率 # probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 probas_new = [] probas_bool = np.array(token_ids, dtype=bool) # np.array(np.where(probas_bool == True)) for i,sentence in enumerate(probas_bool): lie = np.array(np.where(sentence == True))[0] probas_new.append(probas[i,lie[-1]]) probas = np.array(probas_new) k_indices = np.argmax(probas,axis=1) # 仅保留topk k_indices = k_indices.reshape(-1,1) sample_ids = k_indices output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 flag = is_end & (end_counts >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 index = np.array(np.where(flag == True))[0] pop_index = [] for i in index: results[index_data[i]] = output_ids[i] pop_index.append(index_data[i]) for i in pop_index: index_data.remove(i) # for ids in output_ids[flag]: # 存好已完成序列 # results.append(ids) flag = (flag == False) # 标记未完成序列 inputs_str = [inputs_str[i] for i in index_data] # 只保留未完成部分输入 output_ids = output_ids[flag] # 只保留未完成部分候选集 if len(output_ids) == 0: break else: output_str = [tokenizer.decode(ids) for ids in output_ids] else: output_str = [tokenizer.decode(ids) for ids in output_ids] # 如果还有未完成序列，直接放入结果 # for ids in output_ids: # results.append(ids) # 返回结果 return results @AutoRegressiveDecoder.wraps(default_rtype='probas') def predict(self, inputs, output_ids, states): token_ids, segment_ids = inputs token_ids = np.concatenate([token_ids, output_ids], 1) segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) with graph.as_default(): K.set_session(sess) nodes = self.last_token(self.model).predict([token_ids, segment_ids]) return nodes # return self.last_token(self.model).predict([token_ids, segment_ids]) def predict_batch(self, inputs): token_ids, segment_ids = inputs # token_ids = np.concatenate([token_ids, output_ids], 1) # segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) with graph.as_default(): K.set_session(sess) nodes = self.model.predict([token_ids, segment_ids]) return nodes # return self.last_token(self.model).predict([token_ids, segment_ids]) def generate(self, text, topk=5): token_ids, segment_ids = self.tokenizer.encode(text, maxlen=256) output_ids = self.beam_search([token_ids, segment_ids], topk=topk) # 基于beam search return self.tokenizer.decode(output_ids) def generate_random(self, text, n=20, topk=5): if isinstance(text, list): text = text[0] token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) output_ids = self.random_sample([token_ids, segment_ids], n, topk) # 基于随机采样 return [tokenizer.decode(ids) for ids in output_ids] def generate_random_topp(self, text, n=20, topp=0.98): if isinstance(text, list): text = text[0] token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) output_ids = self.random_sample([token_ids, segment_ids], n, topp=topp) # 基于随机采样 return [tokenizer.decode(ids) for ids in output_ids] def generate_top(self, text): output_ids = self.top_batch(text) # 基于随机采样 return [tokenizer.decode(ids) for ids in output_ids] def generate_random_sample_and_beam_search(self, text, n=20, topk=5): text = text[0] token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) output_ids_r, output_ids_b = self.random_sample_and_beam_search([token_ids, segment_ids], n=n, topk=topk) # 基于beam search output_str_r = [self.tokenizer.decode(ids) for ids in output_ids_r] output_str_b = [self.tokenizer.decode(ids) for ids in output_ids_b] return output_str_r, output_str_b def gen_synonyms(self, text, n=20): """"含义： 产生sent的n个相似句，然后返回最相似的k个。 做法：用seq2seq生成，并用encoder算相似度并排序。 """ r = self.generate_random_topp(text, n) r = [i for i in set(r) if i != text] r = [text] + r X, S = [], [] for t in r: x, s = tokenizer.encode(t) X.append(x) S.append(s) X = sequence_padding(X) S = sequence_padding(S) Z = encoder.predict([X, S]) Z /= (Z ** 2).sum(axis=1, keepdims=True) ** 0.5 argsort = np.dot(Z[1:], -Z[0]).argsort() return [r[i + 1] for i in argsort] def gen_synonyms_short(self, text, n=20, len_s = 0.9): """"含义： 产生sent的n个相似句，然后返回最相似的k个。 做法：用seq2seq生成，并用encoder算相似度并排序。 """ if isinstance(text, list): text = text[0] new_text_len = int(len(text) * len_s) r = self.generate_random(text, n) r = [i for i in set(r) if i != text] r = [text] + r X, S = [], [] for t in r: x, s = tokenizer.encode(t) X.append(x) S.append(s) X = sequence_padding(X) S = sequence_padding(S) with graph.as_default(): K.set_session(sess) Z = encoder.predict([X, S]) Z /= (Z ** 2).sum(axis=1, keepdims=True) ** 0.5 argsort = np.dot(Z[1:], -Z[0]).argsort() sentence_list = [r[i + 1] for i in argsort] return_list = [] for i in sentence_list: if len(i) < new_text_len: return_list.append(i) break for i in sentence_list: if new_text_len object: """ @param file:list """ text = file[0] pre = autotitle.generate(text) return pre def just_show_sentence_batch(file: list) -> object: text = file pre = autotitle.generate_top(text) return pre def just_show_csv_random(file): data_new = [] data = pd.read_csv(file).values.tolist() for sentence in tqdm(data): sentence = sentence[1] print(sentence) data_new_dan = [] data_new_dan.extend([sentence, len(sentence)]) pre = autotitle.generate_random(sentence) for i in pre: data_new_dan.extend([i, len(i)]) data_new.append(data_new_dan) pd.DataFrame(data_new).to_csv("data/###第3章 非常尴尬_sim_topK_5.csv") # return pre def just_show_chachong_random(file): text = file[0] pre = autotitle.gen_synonyms(text) return pre def just_show_csv_beam(file): data_new = [] data = pd.read_csv(file).values.tolist() for sentence in tqdm(data): sentence = sentence[1] print(sentence) data_new_dan = [] data_new_dan.extend([sentence, len(sentence)]) pre = autotitle.generate([sentence]) print(pre) data_new_dan.extend([pre, len(pre)]) data_new.append(data_new_dan) pd.DataFrame(data_new).to_csv("data/###第3章 非常尴尬_sim_topK_1.csv") if __name__ == '__main__': # text = ["强调轻资产“经营”, 更加重视“营风险”的规避", "历史和当下都证明,创新是民族生存、发展的不竭源泉,是是自身发展的必然选择", "是时代对于青年们的深切呼唤"] # print(just_show_sentence(text)) # # print(just_show_sentence_batch(text)) # print(type(just_show_sentence_batch(text))) path = "./data/700条论文测试.xlsx" df_list = pd.read_excel(path).values.tolist() df_list_new = [] print(len(df_list)) for i in tqdm(df_list): try: pre = just_show_sentence([i[0]]) df_list_new.append([i[0], i[1]] + [pre]) except: print(i[0]) continue df = pd.DataFrame(df_list_new) df.to_excel("./data/700条论文测试_19.xlsx", index=None)