戒酒的李白
49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
import os
|
|
from transformers.models.bert import BertTokenizer, BertModel
|
|
import torch
|
|
from tqdm import tqdm
|
|
import numpy as np
|
|
import jieba
|
|
from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
|
|
from contextualized_topic_models.models.ctm import CombinedTM
|
|
|
|
class BERT_CTM_Model:
|
|
def __init__(self, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=50):
|
|
# 加载BERT模型和tokenizer
|
|
self.tokenizer = BertTokenizer.from_pretrained(bert_model_path)
|
|
self.model = BertModel.from_pretrained(bert_model_path)
|
|
|
|
# 创建CTM数据预处理对象
|
|
self.tp = TopicModelDataPreparation(ctm_tokenizer_path)
|
|
self.n_components = n_components
|
|
self.num_epochs = num_epochs
|
|
|
|
def get_bert_embeddings(self, texts):
|
|
"""使用BERT模型批量生成文本的嵌入向量"""
|
|
embeddings = []
|
|
for text in tqdm(texts, desc="Processing texts with BERT"):
|
|
inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80)
|
|
with torch.no_grad():
|
|
outputs = self.model(**inputs)
|
|
embeddings.append(outputs.last_hidden_state.cpu().numpy()) # [batch_size, sequence_length, hidden_size]
|
|
return np.vstack(embeddings)
|
|
|
|
def chinese_tokenize(self, text):
|
|
"""使用jieba对中文文本进行分词"""
|
|
return " ".join(jieba.cut(text))
|
|
|
|
def train_ctm(self, texts):
|
|
"""训练CTM模型"""
|
|
bow_texts = [self.chinese_tokenize(text) for text in texts]
|
|
training_dataset = self.tp.fit(text_for_contextual=texts, text_for_bow=bow_texts)
|
|
|
|
# 训练CTM
|
|
ctm = CombinedTM(bow_size=len(self.tp.vocab), contextual_size=768, n_components=self.n_components, num_epochs=self.num_epochs)
|
|
ctm.fit(training_dataset)
|
|
print("CTM模型训练完成")
|
|
|
|
if __name__ == "__main__":
|
|
model = BERT_CTM_Model('./bert_model', './sentence_bert_model')
|
|
texts = ["这是第一个文本", "这是第二个文本"]
|
|
model.train_ctm(texts)
|