| This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using SQUAD | Muhammad Harris | | | |
| | Train T5 on TPU | How to train T5 on SQUAD with Transformers and Nlp | Suraj Patil | | | |
| | Fine-tune T5 for Classification and Multiple Choice | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning | Suraj Patil | | | |
| | Fine-tune DialoGPT on New Datasets and Languages | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots | Nathan Cooper | | | |
| | Long Sequence Modeling with Reformer | How to train on sequences as long as 500,000 tokens with Reformer | Patrick von Platen | | | |
| | Fine-tune BART for Summarization | How to fine-tune BART for summarization with fastai using blurr | Wayde Gilliam | | | |
| | Fine-tune a pre-trained Transformer on anyone's tweets | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model | Boris Dayma | | | |
| | Optimize 🤗 Hugging Face models with Weights & Biases | A complete tutorial showcasing W&B integration with Hugging Face | Boris Dayma | | | |
| | Pretrain Longformer | How to build a "long" version of existing pretrained models | Iz Beltagy | | | |
| | Fine-tune Longformer for QA | How to fine-tune longformer model for QA task | Suraj Patil | | | |
| | Evaluate Model with 🤗nlp | How to evaluate longformer on TriviaQA with nlp | Patrick von Platen | | | |
| | Fine-tune T5 for Sentiment Span Extraction | How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning | Lorenzo Ampil | | | |
| | Fine-tune DistilBert for Multiclass Classification | How to fine-tune DistilBert for multiclass classification with PyTorch | Abhishek Kumar Mishra | | | |
| |Fine-tune BERT for Multi-label Classification|How to fine-tune BERT for multi-label classification using PyTorch|Abhishek Kumar Mishra || | |
| |Fine-tune T5 for Summarization|How to fine-tune T5 for summarization in PyTorch and track experiments with WandB|Abhishek Kumar Mishra || | |
| |Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing|How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing|Michael Benesty || | |
| |Pretrain Reformer for Masked Language Modeling| How to train a Reformer model with bi-directional self-attention layers | Patrick von Platen | | | |
| |Expand and Fine Tune Sci-BERT| How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. |