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):
  • performing inference with ViTForImageClassification
  • 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
• DETR (paper):
  • performing inference with DetrForObjectDetection
  • fine-tuning DetrForObjectDetection on a custom object detection dataset
  • evaluating DetrForObjectDetection on the COCO detection 2017 validation set
  • performing inference with DetrForSegmentation
  • fine-tuning DetrForSegmentation on COCO panoptic 2017

… more to come! 🤗

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, LUKE by Studio Ousia and DEtection TRansformers (DETR) by Facebook AI to the library, so all of them were an incredible learning experience. I can recommend anyone to contribute an AI algorithm to the library!