Comments (2)
This represents the output of the docformer encoder. Weights are randomly initialized so if you wanted to train it, you'd have to put a head on top of it.
from docformer.
@mayankpathaklumiq This implementation has been done, with the variables name, similar to that of the paper, so in order to fully grasp it, maybe you just need to keep the code and the paper, side by side, and everything would be clear, since this is what we did
from docformer.
Related Issues (20)
- Reference code of pre-training tasks HOT 4
- [Errno 2] No such file or directory: 'rvl_cdip_dataset.csv'
- NER task HOT 4
- Predictions are wrong. HOT 1
- can you provide visual question answering task code HOT 1
- Unable to convert model to onnx HOT 3
- DocFormer for Token Classification. HOT 3
- Shape mismatch during sanity check HOT 1
- finetune the Docformer HOT 8
- Pre-trained models HOT 5
- Error in Example: Please provide the bounding box and words or pass the argument "use_ocr" = True HOT 1
- DocFormer for key-value pairs extraction HOT 1
- Using pre-trained models HOT 17
- Inference for token classification. HOT 1
- We can not find DocFormer_For_IR in modeling.py and modeling_l.py HOT 1
- Permission Denied Pretraining Weights HOT 2
- NotImplementedError: Support for `validation_epoch_end` has been removed in v2.0.0. `DocFormer` implements this method HOT 2
- pre-training code tutorial HOT 3
- The pre-trained weight link is invalid HOT 1
- DocFormerv2 HOT 1
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from docformer.