[TF] Save finetuned-model without huggingface-hub login

Feature request

[TF] Save finetuned-model in local without huggingface-hub login

Motivation

in TF, We need to login for saving finetuned-model.

from transformers.keras_callbacks import PushToHubCallback

push_to_hub_callback = PushToHubCallback(
    output_dir="my_awesome_model",
    tokenizer=tokenizer,
)

But I don’t want to sync in my hub yet. Firstly, I want to save my models in local and test them

I checked that works in PyTorch, But It’s not in Tensorflow

Your contribution

I think we need to add argument whether to login or not

https://github.com/huggingface/transformers/blob/0526a075c567d7508205fe6054310f3d132b3227/src/transformers/keras_callbacks.py#L267

[goreng2]

Hi @Rocketknight1 ! Thanks for your comment.

I want to use Huggingface’s pipeline API for inference. I think pipeline perhaps can receive only .h5 model

When I tried ModelCheckpoint callback, It returns ckpt files. It can’t be used in pipeline. For convert ckpt to .h5, I need to write model architecture (in my case ELECTRA) But It’s so difficult and complex to me 😥 I tried to convert ckpt to pth (PyTorch) But It doesn’t work… Maybe this code only works in converting TF1 to PyTorch

When I tried model.save('my_model.h5'), Error msg raised. Maybe Something format is not match

I don’t test model.save_pretrained() yet, It returns .h5?

[Rocketknight1]

Ah, yes. The .ckpt files from ModelCheckpoint are only useful for saving/resuming training, and you won’t be able to use them in pipelines.

The way TF models on HuggingFace work is that they’re built on top of Keras models. model.save() and ModelCheckpoint are both part of Keras. However, if you want to save the model to load with other HuggingFace tools, you should use save_pretrained(). This is our method and doesn’t exist in base Keras models. It saves the model as .h5, but also adds a config.json that will allow the pipeline API and other methods like from_pretrained to initialize the model correctly.

Try just doing this:

model.save_pretrained("my_model")
pipe = pipeline("text-classification", model="my_model")

Though of course, make sure to change text-classification to the task you want to do!