Avoiding the time consuming for downloading the pre-trained models

[ForawardStar]

📚 Migration

Very great project! I can successfully run the code:

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model_Bert = AutoModel.from_pretrained("bert-base-chinese")

But, every time I run my code, the downloading processing are repeated, which is time consuming. So I wonder how to load the pre-trained models from my local directory instead of downloading the pre-trained models everytime?

[zartdinov]

Hi! It should work (examples for Linux systems)

# Save model to directory:
model.save_pretrained("./my_model_directory/") 

# Load model from directory:
model = AutoModel.from_pretrained("./my_model_directory/")

####################### OR ###########################

# Cache model to directory:
model = AutoModel.from_pretrained("bert-base-chinese", cache_dir="./my_model_directory/")

[ForawardStar]

Thanks! It is useful for me. One more question: I found that the files saved in "./my_model_directory/" contain config.json, pytorch_model.bin, and other .json files, but do not contain 'vocab.txt', is it reasonable?

[zartdinov]

I think vocab.txt is only needed for the tokenizer. The tokenizer can also be saved in the same directory.

[LysandreJik]

When you do

tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model_Bert = AutoModel.from_pretrained("bert-base-chinese")

this should be caching the files in your local cache, it shouldn't redownload the files every time. You shouldn't need to specify a local folder in which to save them.

How are you identifying that the files are redownloaded every time?

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[earonesty]

this should be caching the files in your local cache, it shouldn't redownload the files every time. You shouldn't need to specify a local folder in which to save them.

model cards are updated frequently. so people re-download them a lot!