将gensim doc2vec训练模型保存在google colab上

import gensim
from gensim.models.doc2vec import Doc2Vec

# 假设你有一个包含文档的列表，每个文档都是一个单词列表
documents = [["这", "是", "文档", "1"], ["这", "是", "文档", "2"], ...]

# 创建TaggedDocument对象，将文档列表转换为doc2vec所需的格式
tagged_documents = [gensim.models.doc2vec.TaggedDocument(doc, [i]) for i, doc in enumerate(documents)]

# 初始化doc2vec模型
model = Doc2Vec(vector_size=100, min_count=2, epochs=40)

# 构建词汇表
model.build_vocab(tagged_documents)

# 训练模型
model.train(tagged_documents, total_examples=model.corpus_count, epochs=model.epochs)

# 保存模型
model.save("doc2vec_model.bin")

# 安装和导入Google Drive库
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

# 授权并创建Google Drive客户端
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

# 上传模型到Google Drive
model_file = drive.CreateFile({'title': 'doc2vec_model.bin'})
model_file.SetContentFile('doc2vec_model.bin')
model_file.Upload()

model_file_url = model_file['alternateLink']