Fine-tune a Hugginface model with only loss function(without labels)?
For the task I want to do I don’t have any labels, only a loss function to minimize. Is there a way for me to fine-tune a model without target output?More concretely, I want to initialize the fine-tuning by putting the classes I want to predict for token classification in random places in the input text until it learns to output those. When the model learns to output the classifications I have a function that takes the output and gives the loss back. Hopefully, this will let the model learn to classify correctly.The loss is continuous, but can also be binary by using a threshold.Is this possible to do without going under the hood?I’m new to this. Any information at all would be appreciated. Thanks