NER model only predicts the outside 'O' tag
HiI’ve managed to produce an NER module followingthis tutorial. Although I am using the ScienceIE dataset (Process, Material, Task entities).The issue is that my model only predicts the ‘O’ tag… I can understand that the model does it because the majority of tags are ‘O’ but obviously this isn’t solving the task.I’ve created a custom compute_metrics function that calculates f1 score for each entity and the macro-average. However, this doesn’t help steer the model. Do I need to create a custom loss function?I’ve tried googling around for this issue but haven’t had much luck. Sorry if this is a repeat issue.Thanks for your help.
2021-04-27T18:27:47Z
hey@CDobbsCDobbs:I’ve created a custom compute_metrics function that calculates f1 score for each entity and the macro-average. However, this doesn’t help steer the model. Do I need to create a custom loss function?if you’re using theTrainerfor fine-tuning, then you shouldn’t need a custom loss function. speaking of losses, does the train / validation loss decrease during fine-tuning?without seeing more details / code on your approach, it’s hard to debug the problem - can you share them?ps. you might find it instructive to see the “official” tutorial on NER which contains many more details than the one you linked to:Google Colab