Use fastai v2 with HuggingFace's pretrained transformers, see the notebooks below depending on your task:

• Text classification: fasthugs_seq_classification.ipynb
• Language model pre-training or fine-tuning (RoBERTa only for now): fasthugs_language_model.ipynb

• Added fasthugs_language_model.ipynb which shows you how to pre-train or fine-tune a Masked Language Model (MLM), RoBERTa in this case, from scratch

• Added new get_vocab functionality from HuggingFace, unified api to extract a tokenizer's vocab
• Added new AutoModelForSequenceClassification, AutoConfig, AutoModelForSequenceClassification HuggingFace functionality to make things tider
• Tidied up and refactored FastHugsTokenizer and FastHugsModel
• OLD demo and vocab files to be deleted soon

FastHugsTokenizer: A tokenizer wrapper than can be used with fastai-v2’s tokenizer.

FastHugsModel: A model wrapper over the HF models, more or less the same to the wrapper’s from HF fastai-v1 articles mentioned below

Padding: Padding settings for the padding token index and on whether the transformer prefers left or right padding

Model Splitters: Functions to split the classification head from the model backbone in line with fastai-v2’s new definition of Learner (splitters)

This notebook heavily borrows from this notebook , which in turn is based off of this tutorial and accompanying article. Huge thanks to Melissa Rajaram and Maximilien Roberti for these great resources, if you're not familiar with the HuggingFace library please given them a read first as they are quite comprehensive.

This paper introduces the v2 version of the fastai library and you can follow and contribute to v2's progress on the forums. This notebook uses the small IMDB dataset and is based off the fastai-v2 ULMFiT tutorial. Huge thanks to Jeremy, Sylvain, Rachel and the fastai community for making this library what it is. I'm super excited about the additinal flexibility v2 brings. 🎉