Ulipenitz

Maybe usefull for people working on a **multi-label** problem: You can wrap _**LogisticRegression**_ inside _**OneVsRestClassifier**_ like this: ``` model = SetFitModel( model_body=SentenceTransformer('all-MiniLM-L6-v2'), model_head=OneVsRestClassifier(LogisticRegression(class_weight="balanced")), multi_target_strategy="one-vs-rest" ) ```

You could try overwritting the class method `on_train_end`. I had a similar issue when using `NeptuneCallback`, because the `TrainingArguments` class in `SetFit` is missing `overwrite_output_dir` as an argument: ``` File...

Thanks for the answer @SiddhantSadangi! This is indeed useful to log metadata like test metrics after training. My problem though is that I need to set up the python logger...

I had the same issue with the NeptuneCallback. Worked locally, but got the error in a Docker container on the cloud. It seems pretty weird to me, because there is...

This is my current workaround to avoid calling the `run` method (I use `NeptuneCallbackSetFit`, because I had to overwrite some class methods which I mentioned here [setfit498](https://github.com/huggingface/setfit/issues/498) and here [setfit464](https://github.com/huggingface/setfit/issues/464)...

I could not extract this from my original code, but I put together the example script from the setfit github start page and my steps for reproduction. Unfortunately, this issue...

Sorry for not coming back earlier. Yes, this solves the issue of initializing multiple runs in parallel! Thank you!

I am facing the same issue. Any updates on this?

Thank you for the quick reply! I already tried this, but unfortunately I get an error like this: `FileNotFoundError: [Errno 2] No such file or directory: 'ABSOLUTEPATH\\.neptune\\async\\project__9701b6a4-d310-4f5f-a6e0-7827a05c1e78\\exec-1708349077.259059-2024-02-19_14.24.37.259059-5884\\upload_path\\data_dummy_data-1708349077.32419-2024-02-19_14.24.37.324190.pkl'` I used this...

Sorry, I did not realize that I was not running on the newest version. It works now! Also, your proposed solution works! Thanks for the help! :-)