NielsRogge

Hi, This is definitely on my list! Generally, I do the following: * first, it's important to understand the original model. For this, I typically scan the paper, read the...

See #23

Hi, This is definitely on my roadmap. The LayoutLMv2 authors [defined](https://github.com/microsoft/unilm/blob/53995b4876464146365693396aaaa09e88a4494e/layoutlmft/layoutlmft/models/layoutlmv2/modeling_layoutlmv2.py#L895) another model called `LayoutLMv2ForRelationExtraction`, that does exactly that. However, they did not specify how to use the model at...

> Tails and heads are not given by the model => Tails are questions, and answers are heads (or vice versa). So `LayoutLMForTokenClassification` does provide you that.

Hi, Thanks for your interest in LayoutLMv3. The model indeed leverages segment position embeddings instead of position embeddings per word, and this seems to greatly improve performance. Indeed, at inference...

Thanks for suggesting, it has been added! https://huggingface.co/docs/transformers/main/en/model_doc/lilt

Hi, Sadly Microsoft didn't open-source any pre-training code. Regarding masked image modeling, I recommend checking out the [run_mim.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-pretraining/run_mim.py) script which I added to Transformers, which allows to do masked image...

Hi, What do you mean by link prediction? LayoutLM can be used to process documents like invoices and receipts. It can extract information from them, and classify them.

Oh that's really cool, I didn't know that. Of course, linking the answers to the questions is important. Given that LayoutLM is capable of identifying all questions and answers from...

Awesome :) feel free to create an organization on [hf.co](https://huggingface.co/login?next=%2Fnew). I can then put all YOLOS checkpoints there, such that people will be able to do (for instance): ``` from...