Demo notebook for LayoutLMForSequenceClassification

Hey there,

I’ve recently improved LayoutLM in the HuggingFace Transformers library by adding some more documentation + code examples, a demo notebook that illustrates how to fine-tune LayoutLMForTokenClassification on the FUNSD dataset, some integration tests that verify whether the implementation in HuggingFace Transformers gives the same output tensors on the same input data as the original implementation, and finally LayoutLMForSequenceClassification. My PR was merged yesterday 😃

However, now I’m also preparing a notebook that illustrates how to fine-tune LayoutLMForSequenceClassification on (a small subset of) the RVL-CDIP dataset. However, it doesn’t seem to be able to overfit the tiny subset (I have 16 images per class, so as there are 16 labels I have 256 training examples). You can run it here: https://colab.research.google.com/drive/1DUpTi2aL64AuIJ_9g6dGgKfltEEFqQbt?usp=sharing

Any feedback is greatly appreciated!

[NielsRogge]

Btw, the demo notebook for fine-tuning LayoutLMForTokenClassification on the FUNSD dataset can be found here.

[NielsRogge]

Hi @monuminu @VishnuGopireddy I have a new notebook that adds visual features from a Resnet-101 backbone in addition to the text + layout features. You can find it here: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb

It relies entirely on HuggingFace Transformers, no need for the unilm repo anymore 😃