还有什么解决方案可以将现有的Googlenews W2v加载到gensim中，并使用额外的语料库对其进行微调吗？

pip install gensim

import gensim.downloader as api

# 下载并加载预训练的 Google News Word2Vec 模型
model = api.load('word2vec-google-news-300')

from gensim.models import KeyedVectors

# 加载预训练的 Google News Word2Vec 模型
model = KeyedVectors.load_word2vec_format('path/to/GoogleNews-vectors-negative300.bin', binary=True)

from gensim.models.word2vec import LineSentence

# 准备额外的语料库
additional_corpus = LineSentence('path/to/additional_corpus.txt')

from gensim.models import Word2Vec

# 将 KeyedVectors 转换为 Word2Vec 模型
word2vec_model = Word2Vec(vector_size=300, window=5, min_count=1)
word2vec_model.build_vocab([list(model.key_to_index.keys())], update=True)

# 使用额外的语料库进行微调
word2vec_model.build_vocab(additional_corpus, update=True)
word2vec_model.train(additional_corpus, total_examples=word2vec_model.corpus_count, epochs=word2vec_model.epochs)

# 保存微调后的模型
word2vec_model.save('path/to/finetuned_word2vec.model')

import gensim.downloader as api
from gensim.models import Word2Vec, KeyedVectors
from gensim.models.word2vec import LineSentence

# 加载预训练的 Google News Word2Vec 模型
model = api.load('word2vec-google-news-300')

# 将 KeyedVectors 转换为 Word2Vec 模型
word2vec_model = Word2Vec(vector_size=300, window=5, min_count=1)
word2vec_model.build_vocab([list(model.key_to_index.keys())], update=True)

# 准备额外的语料库
additional_corpus = LineSentence('path/to/additional_corpus.txt')

# 使用额外的语料库进行微调
word2vec_model.build_vocab(additional_corpus, update=True)
word2vec_model.train(additional_corpus, total_examples=word2vec_model.corpus_count, epochs=word2vec_model.epochs)

# 保存微调后的模型
word2vec_model.save('path/to/finetuned_word2vec.model')