Transformers-Tutorials

Hi there!

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

NOTE: if you are not familiar with HuggingFace and/or Transformers, I highly recommend to check out our free course, which introduces you to several Transformer architectures (such as BERT, GPT-2, T5, BART, etc.), as well as an overview of the HuggingFace libraries, including Transformers, Tokenizers, Datasets, Accelerate and the hub.

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
• T5 (paper):
  • fine-tuning T5ForConditionalGeneration on a Dutch summarization dataset on TPU using HuggingFace Accelerate
  • fine-tuning T5ForConditionalGeneration (CodeT5) for Ruby code summarization using PyTorch Lightning
• LayoutLMv2 (paper):
  • fine-tuning LayoutLMv2ForSequenceClassification on RVL-CDIP
  • fine-tuning LayoutLMv2ForTokenClassification on FUNSD
  • fine-tuning LayoutLMv2ForTokenClassification on FUNSD using the 🤗 Trainer
  • performing inference with LayoutLMv2ForTokenClassification on FUNSD
  • true inference with LayoutLMv2ForTokenClassification (when no labels are available) + Gradio demo
  • fine-tuning LayoutLMv2ForTokenClassification on CORD
  • fine-tuning LayoutLMv2ForQuestionAnswering on DOCVQA
• CANINE (paper):
  • fine-tuning CanineForSequenceClassification on IMDb
• GPT-J-6B (repository):
  • performing inference with GPTJForCausalLM to illustrate few-shot learning and code generation

... 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 following algorithms to the library:

• TAbular PArSing (TAPAS) by Google AI
• Vision Transformer (ViT) by Google AI
• Data-efficient Image Transformers (DeiT) by Facebook AI
• LUKE by Studio Ousia
• DEtection TRansformers (DETR) by Facebook AI
• CANINE by Google AI
• BEiT by Microsoft Research
• LayoutLMv2 (and LayoutXLM) by Microsoft Research

All of them were an incredible learning experience. I can recommend anyone to contribute an AI algorithm to the library!