Slightly different output from trainer.predict and pipeline(..., function_to_apply="none")
Hello,I have a trained multi-label text classifier that I want to use for inference. I loaded it into apipeline("text-classification", "./model"). When calculating performance in the downstream application I noticed how the performance metrics were noticably worse than those I obtained during training (both calculated on the same held-out test set).Here is a histogram or the difference in outputs between the models:Only about 1/4th of the measurements in the 0-bar are exactly equal to 0.I have been scratching my head about this thing for the whole day and any help would be greatly appreciatedEdit: I tested running the pipeline on the GPU and I get the same results. I also checked the parameters of the models in both the trainer and pipeline and they are identical.
2023-06-21T16:15:09Z
Had the same problem with token classification pipeline.  Did you find the reason?