The best tutorials for learning and applying transformers.
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.
VirTex: Learning Visual Representations from Textual Annotations
We train CNN+Transformer from scratch from COCO, transfer the CNN to 6 downstream vision tasks, and exceed ImageNet features despite using 10x fewer ...
The Transformer Family
This post presents how the vanilla Transformer can be improved for longer-term attention span, less memory and computation consumption, RL task solving, ...
Multi-task Training with Hugging Face Transformers and NLP
A recipe for multi-task training with Transformers' Trainer and NLP datasets.
Transformers from Scratch
Attempt to explain directly how modern transformers work, and why, without some of the historical baggage.
The Illustrated Transformer
In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained.
RoBERTa meets TPUs
Understanding and applying the RoBERTa model to the current challenge.
The Annotated Transformer
In this post I present an “annotated” version of the paper in the form of a line-by-line implementation.
Linformer: Self-Attention with Linear Complexity
We demonstrate that the self-attention mechanism can be approximated by a low-rank matrix.
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