如何在Colab中加载已训练保存的Doc2Vec模型

Hey there! Loading your trained Doc2Vec model is pretty straightforward once you’ve sorted out the correct file path. Let’s walk through this step by step:

1. First, pinpoint your model files #

You mentioned you can view the folder where the model is stored. Use your existing directory-listing code (or a simplified version below) to locate all model-related files. Gensim’s Doc2Vec saves multiple files for a complete model (like your_model_name.model, your_model_name.model.trainables.syn1neg.npy, etc.)—you’ll need the path to the main .model file (it’s usually the largest one in the folder).

Here’s a quick, clean version of the code to list and sort your folder’s contents:

from os import listdir
from os.path import isfile, getsize, join

# Replace this with the actual path of your model's folder
target_folder = "/path/to/your/model/folder"
# Get all files in the folder with their sizes
folder_contents = [(f, getsize(join(target_folder, f))) for f in listdir(target_folder) if isfile(join(target_folder, f))]
# Sort files by size (largest first) to easily spot the main model file
sorted_contents = sorted(folder_contents, key=lambda x: x[1], reverse=True)
for file_name, file_size in sorted_contents:
    print(f"{file_name} — {file_size} bytes")

2. Load the model with Gensim’s built-in method #

Once you have the full path to your main .model file, use Gensim’s Doc2Vec.load() method to reload it. Just make sure you have Gensim imported first!

import gensim

# Replace with the full path to your saved .model file
model_file_path = "/path/to/your/model/folder/your_trained_model.model"
loaded_doc2vec_model = gensim.models.Doc2Vec.load(model_file_path)

A quick heads-up about Colab storage #

If your model is saved in Colab’s temporary default folders (like /content/ or its subfolders), remember these files get deleted when your Colab session ends. If you need to keep the model long-term, you can mount your Google Drive and copy the model files there after loading—though that’s totally optional based on your needs.

内容的提问来源于stack exchange，提问作者VDC