I’m currently working on my master thesis and need to implement a text-classification model. Therefore I’m fine-tuning a base BERT model on my data. So far so good. To evaluate my fine-tuned model, I’m passing my test data to it for predictions. For this purpose I’m using the “pipeline” API provided by huggingface with a sigmoid function (since I’m facing a multi-label classification problem). It does work and I get as an output the probs for each class. Now if instead of using the pipeline I tokenize and pass the test data directly to the model (i.e. model(tokenized_data)) I get the logits for each class back. But if I transform these logits to probs with a sigmoid function, then I get probs which are completely different from the one outputted by the pipeline. Is this somehow possible?
By the way I double-checked everything (tokenized inputs, parameters, model loading etc.). I think I am missing something, but I don’t know what. Any help is pretty much appreciated!!
Thanks in advance!