Comments (4)
Most Transformer models (like LayoutLM) are pre-trained with a max sequence length of 512 tokens because of time/memory complexity.
One possibility is to first split up the document in different parts, and then apply LayoutLM on each part.
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Can you say how to extract information from layoutlm in question answer pair format ?
from transformers-tutorials.
@jyothish-mohan I have a tutorial on how to use the new LayoutLMv2 model for DocVQA here.
from transformers-tutorials.
Thanks
from transformers-tutorials.
Related Issues (20)
- UDOP FUNSD , not able to perform Inference HOT 2
- LayoutLM prediction with confidence score? HOT 1
- Table transformer crop issue
- UDOP - How to change UdopToknizer to another tokenizer that supports CJK languages? HOT 1
- How can we Fine Tune Pix2StruCt for DocVQA task?
- UDOP - Is it possible to get several bboxes from one single input ?
- [Bug] Owlv2 Zero-shot object detection
- True Inference with Layoutlmv3 HOT 2
- Huge negative loss while fine-tuning SAM HOT 2
- Grounding Dino - Multiple Class HOT 3
- Fine tune TrOCR on IAM Handwriting Database using Seq2SeqTrainer HOT 13
- [Finetuning OneFormer] How to use multiple GPUs
- can't login to push hub model
- Mask2Former panoptic fine-tune HOT 3
- SAM Instance segmentation Finetuning output mask with instance lablel
- Table transformer demo not handling empty cells
- "Unused or unrecognized kwargs: padding" error when running notebook example
- UDOP - Fine tuning with bad metrics HOT 2
- Adding Inference Pipeline for Table-Transformer HOT 1
- - [x] https://github.com/NielsRogge/Transformers-Tutorials/issues/421#issue-2287902399
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from transformers-tutorials.