121 lines
5.1 KiB
Python
121 lines
5.1 KiB
Python
'''
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该功能是为了将关键词加入到embedding模型中，以便于在embedding模型中进行关键词的embedding
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该功能的实现是通过修改embedding模型的tokenizer来实现的
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该功能仅仅对EMBEDDING_MODEL参数对应的的模型有效，输出后的模型保存在原本模型
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感谢@CharlesJu1和@charlesyju的贡献提出了想法和最基础的PR
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保存的模型的位置位于原本嵌入模型的目录下，模型的名称为原模型名称+Merge_Keywords_时间戳
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'''
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import sys
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sys.path.append("..")
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from datetime import datetime
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from configs import (
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MODEL_PATH,
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EMBEDDING_MODEL,
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EMBEDDING_KEYWORD_FILE,
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)
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import os
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import torch
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from safetensors.torch import save_model
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from sentence_transformers import SentenceTransformer
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def get_keyword_embedding(bert_model, tokenizer, key_words):
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tokenizer_output = tokenizer(key_words, return_tensors="pt", padding=True, truncation=True)
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# No need to manually convert to tensor as we've set return_tensors="pt"
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input_ids = tokenizer_output['input_ids']
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# Remove the first and last token for each sequence in the batch
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input_ids = input_ids[:, 1:-1]
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keyword_embedding = bert_model.embeddings.word_embeddings(input_ids)
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keyword_embedding = torch.mean(keyword_embedding, 1)
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return keyword_embedding
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def add_keyword_to_model(model_name=EMBEDDING_MODEL, keyword_file: str = "", output_model_path: str = None):
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key_words = []
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with open(keyword_file, "r") as f:
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for line in f:
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key_words.append(line.strip())
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st_model = SentenceTransformer(model_name)
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key_words_len = len(key_words)
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word_embedding_model = st_model._first_module()
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bert_model = word_embedding_model.auto_model
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tokenizer = word_embedding_model.tokenizer
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key_words_embedding = get_keyword_embedding(bert_model, tokenizer, key_words)
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# key_words_embedding = st_model.encode(key_words)
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embedding_weight = bert_model.embeddings.word_embeddings.weight
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embedding_weight_len = len(embedding_weight)
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tokenizer.add_tokens(key_words)
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bert_model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=32)
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# key_words_embedding_tensor = torch.from_numpy(key_words_embedding)
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embedding_weight = bert_model.embeddings.word_embeddings.weight
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with torch.no_grad():
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embedding_weight[embedding_weight_len:embedding_weight_len + key_words_len, :] = key_words_embedding
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if output_model_path:
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os.makedirs(output_model_path, exist_ok=True)
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word_embedding_model.save(output_model_path)
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safetensors_file = os.path.join(output_model_path, "model.safetensors")
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metadata = {'format': 'pt'}
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save_model(bert_model, safetensors_file, metadata)
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print("save model to {}".format(output_model_path))
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def add_keyword_to_embedding_model(path: str = EMBEDDING_KEYWORD_FILE):
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keyword_file = os.path.join(path)
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model_name = MODEL_PATH["embed_model"][EMBEDDING_MODEL]
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model_parent_directory = os.path.dirname(model_name)
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current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
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output_model_name = "{}_Merge_Keywords_{}".format(EMBEDDING_MODEL, current_time)
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output_model_path = os.path.join(model_parent_directory, output_model_name)
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add_keyword_to_model(model_name, keyword_file, output_model_path)
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if __name__ == '__main__':
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add_keyword_to_embedding_model(EMBEDDING_KEYWORD_FILE)
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# input_model_name = ""
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# output_model_path = ""
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# # 以下为加入关键字前后tokenizer的测试用例对比
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# def print_token_ids(output, tokenizer, sentences):
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# for idx, ids in enumerate(output['input_ids']):
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# print(f'sentence={sentences[idx]}')
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# print(f'ids={ids}')
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# for id in ids:
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# decoded_id = tokenizer.decode(id)
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# print(f' {decoded_id}->{id}')
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#
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# sentences = [
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# '数据科学与大数据技术',
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# 'Langchain-Chatchat'
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# ]
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#
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# st_no_keywords = SentenceTransformer(input_model_name)
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# tokenizer_without_keywords = st_no_keywords.tokenizer
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# print("===== tokenizer with no keywords added =====")
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# output = tokenizer_without_keywords(sentences)
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# print_token_ids(output, tokenizer_without_keywords, sentences)
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# print(f'-------- embedding with no keywords added -----')
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# embeddings = st_no_keywords.encode(sentences)
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# print(embeddings)
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#
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# print("--------------------------------------------")
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# print("--------------------------------------------")
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# print("--------------------------------------------")
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#
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# st_with_keywords = SentenceTransformer(output_model_path)
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# tokenizer_with_keywords = st_with_keywords.tokenizer
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# print("===== tokenizer with keyword added =====")
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# output = tokenizer_with_keywords(sentences)
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# print_token_ids(output, tokenizer_with_keywords, sentences)
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#
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# print(f'-------- embedding with keywords added -----')
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# embeddings = st_with_keywords.encode(sentences)
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# print(embeddings) |