Prabhav Singh

@NielsRogge I am facing this issue also. Cannot seem to load any model weights since the config weight names do not match the ones present in the original Table Transformer...

Hey, Just to clarify - - I trained the model using code provided by the official repository (https://github.com/microsoft/table-transformer/blob/main/src/main.py). - I used the config provided by the same repo (https://github.com/microsoft/table-transformer/blob/main/src/structure_config.json) -...

Hi, I am attaching the training parameters I used & the command I used to train (All from table-transformers original repo): ``` { "lr":5e-5, "lr_backbone":1e-5, "batch_size":2, "weight_decay":1e-4, "epochs":20, "lr_drop":1, "lr_gamma":0.9,...

@Ashwani-Dangwal I am not very sure on this. From what I could understand from the main.py (https://github.com/microsoft/table-transformer/blob/main/src/main.py) loads the base model from DETR and then trains on it. I assumed...

> > Hi, > > I am attaching the training parameters I used & the command I used to train (All from table-transformers original repo): > > ``` > >...

@giuqoob Did you fine-tune on the complete FinTabNet dataset using hugging face model as the base? Unfortunately I fine-tuned on the same using the main.py script and not the hugging...

@Ashwani-Dangwal I have tried training it on FinTabNet and it worked for me after changes to the model weights using the following script -> convert_table_transformer_original_pytorch_checkpoint_to_pytorch.py, present is transformers repo.

Hi, Thanks for the quick help. I was trying to look for a way to improve performance with post processing (Due to memory constraints for training) but I think you...