MLFlow exception when logging BERT model to Azure ML

Question

I am trying to fine-tune a BERT model on Azure ML. I am using Azure ML Jobs to log the metrics, hyperparameters and models using the Python MLFlow API.

But the train method from transformers.Trainer class is causing the following exception with status code 400:

mlflow.exceptions.RestException: INVALID_PARAMETER_VALUE: 
Response: {'Error': {'Code': 'ValidationError', 'Severity': None, 
'Message': 'No more than 500 characters per params Value. Request contains 2 of greater length.' 
...
}

This code worked without issues when it was run on another compute which logged to a self-hosted MLFlow Tracking Server which leads me to believe that this is an issue related to experiment tracking on AML Jobs.

Can someone please help me fix this?

These are my package versions: azure-core==1.29, azureml-core==1.55, azureml-mlflow==1.55, mlflow==2.11, transformers==4.33.0
I am on Python 3.10