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
T5 (paper):
- fine-tuning T5ForConditionalGeneration on a Dutch summarization dataset on TPU using HuggingFace Accelerate

... 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:

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

jnishi/Transformers-Tutorials