Articles, tutorials and research in modern NLP
Top Down Introduction to BERT with HuggingFace and PyTorch
I will also provide some intuition into how BERT works with a top down approach (applications to algorithm).
PyTorch Transformers Tutorials
A set of annotated Jupyter notebooks, that give user a template to fine-tune transformers model to downstream NLP tasks such as classification, NER etc.
The Future of (Transfer Learning in) Natural Language Processing
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions.
NLP Model Selection
NLP model selection guide to make it easier to select models. This is prescriptive in nature and has to be used with caution.
The Transformer … “Explained”?
An intuitive explanation of the Transformer by motivating it through the lens of CNNs, RNNs, etc.
NLP for Developers: Shrinking Transformers | Rasa
In this video, Rasa Senior Developer Advocate Rachael will talk about different approaches to make transformer models smaller.
Generate Boolean (Yes/No) Questions From Any Content
Question generation algorithm trained on the BoolQ dataset using T5 text-to-text transformer model.
A colab notebook to showcase how to fine-tune T5 model on various NLP tasks (especially non text-2-text tasks with text-2-text approach)
Self Supervised Representation Learning in NLP
An overview of self-supervised pretext tasks in Natural Language Processing
Simple Transformers: Transformers Made Easy
Simple Transformers removes complexity and lets you get down to what matters – model training and experimenting with the Transformer model architectures.
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