https://github.com/NielsRogge/Transformers-Tutorials

This repository contains demos I made with the Transformers library by HuggingFace.

Transformers-Tutorials

Hi there!

This repository contains demos I made with the Transformers library by 🤗 HuggingFace.

Currently, it contains the following demos:

• BERT (paper):
  • fine-tuning BertForTokenClassification on a named entity recognition (NER) dataset.
• LayoutLM (paper):
  • fine-tuning LayoutLMForTokenClassification on the FUNSD dataset
  • fine-tuning LayoutLMForSequenceClassification on the RVL-CDIP dataset
  • adding image embeddings to LayoutLM during fine-tuning on the FUNSD dataset
• TAPAS (paper):
  • fine-tuning TapasForQuestionAnswering on the Microsoft Sequential Question Answering (SQA) dataset
  • evaluating TapasForSequenceClassification on the Table Fact Checking (TabFact) dataset
• Vision Transformer (paper):
  • fine-tuning ViTForImageClassification on CIFAR-10 using PyTorch Lightning
  • fine-tuning ViTForImageClassification on CIFAR-10 using the 🤗 Trainer
• LUKE (paper):
  • fine-tuning LukeForEntityPairClassification on a custom relation extraction dataset using PyTorch Lightning

If you have any questions regarding these demos, feel free to open an issue on this repository.

Btw, I was also the main contributor to add the Vision Transformer (ViT) by Google AI, Data-efficient Image Transformers (DeiT) by Facebook AI, TAbular Parsing (TAPAS) by Google AI and LUKE by Studio Ousia to the library, so all of them were an incredible learning experience. I can recommend anyone to contribute an AI algorithm to the library!