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59 lines
2.0 KiB
59 lines
2.0 KiB
from tqdm import tqdm
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import os
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import sys
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sys.path.append(os.getcwd())
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from config import shixuns_data_path,shixuns_bert_em_path
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import pandas as pd
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import logging
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from transformers import AutoTokenizer, TFAutoModel
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from config import bert_base_chinese
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from utils import finalcut
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logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
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tqdm.pandas()
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# 加载大规模预训练Bert模型
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bert_model = bert_base_chinese
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tokenizer = AutoTokenizer.from_pretrained(bert_model)
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model = TFAutoModel.from_pretrained(bert_model,output_hidden_states=True) # 模型是否返回所有隐藏状态。
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shixun = pd.read_csv(shixuns_data_path,sep='\t',encoding='utf-8')
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shixun = shixun.drop(['updated_at', 'status', 'publish_time', 'modify_time',
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'reset_time', 'trainee', 'myshixuns_count','disciplines_id',
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'disciplines_name', 'subject_id','created_at_ts'], axis=1)
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#添加bert_em列
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for i in tqdm(range(768)):
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bert_em = "bert_em" + str(i)
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shixun[bert_em] = 0.
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# 准备数据
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shixun_name = shixun["shixun_name"]
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language = shixun["language"]
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subject_name = shixun["subject_name"]
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shixun_name.fillna(value="",inplace=True)
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language.fillna(value="",inplace=True)
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subject_name.fillna(value="",inplace=True)
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shixun_text = shixun_name + language + subject_name
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words = []
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for i in tqdm(range(len(shixun_text))):
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words.append(finalcut((shixun_text[i])))#删除句子中无效字符
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def getbert_vec(word_list):
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inputs = tokenizer(word_list, return_tensors="tf", padding="max_length", truncation=True, max_length=64)
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outputs = model(inputs)
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hidden_states = outputs[1] # 获得句子向量
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return list(hidden_states.numpy()[0])
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words_list = []
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for i in tqdm(range(len(words))):
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words_list.append(getbert_vec(words[i]))#获得句子的bert embedding向量
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for i in tqdm(range(len(words_list))):
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for j in range(len(words_list[i])):
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column = "bert_em" + str(j)
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shixun.loc[i, column] = words_list[i][j]
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shixun.to_csv(shixuns_bert_em_path,sep='\t', index=False, header=True) |