tldr-transformers

The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

Models: GPT- *, * BERT *, Adapter- *, * T5, etc.

BERT and T5 (art from the original papers)

Each set of notes includes links to the paper, the original code implementation (if available) and the Huggingface 🤗 implementation.

Here is an example: t5.

The transformers papers are presented somewhat chronologically below. Go to the " 👉 Notes 👈 " column below to find the notes for each paper.

This repo also includes a table quantifying the differences across transformer papers all in one table.

Quick Note
Motivation
Papers::Transformer Papers
Papers::1 Table To Rule Them All
Papers::Alignment Papers
Papers::Scaling Law Papers
Papers::LM Memorization Papers
Papers::Limited Label Learning Papers
How To Contribute
How To Point Our Errors
Citation
License

Quick_Note

This is not an intro to deep learning in NLP. If you are looking for that, I recommend one of the following: Fast AI's course, one of the Coursera courses, or maybe this old thing. Come here after that.

Motivation

With the explosion in papers on all things Transformers the past few years, it seems useful to catalog the salient features/results/insights of each paper in a digestible format. Hence this repo.

Models

Model	Year	Institute	Paper	👉 Notes 👈	Original Code	Huggingface 🤗	Other Repo
Transformer	2017	Google	Attention is All You Need	Skipped, too many good write-ups: Harvard NLP Group Jay Alammar Lilian Weng Something old		?
GPT-3	2018	OpenAI	Language Models are Unsupervised Multitask Learners	To-Do	X	X
GPT-J-6B	2021	EleutherAI	GPT-J-6B: 6B Jax-Based Transformer (public GPT-3)	X	here	x	x
BERT	2018	Google	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	BERT notes	here	here
DistilBERT	2019	Huggingface	DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter	DistilBERT notes		here
ALBERT	2019	Google/Toyota	ALBERT: A Lite BERT for Self-supervised Learning of Language Representations	ALBERT notes	here	here
RoBERTa	2019	Facebook	RoBERTa: A Robustly Optimized BERT Pretraining Approach	RoBERTa notes	here	here
BART	2019	Facebook	BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension	BART notes	here	here
T5	2019	Google	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	T5 notes	here	here
Adapter-BERT	2019	Google	Parameter-Efficient Transfer Learning for NLP	Adapter-BERT notes	here	-	here
Megatron-LM	2019	NVIDIA	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism	Megatron notes	here	-	here
Reformer	2020	Google	Reformer: The Efficient Transformer	Reformer notes		here
byT5	2021	Google	ByT5: Towards a token-free future with pre-trained byte-to-byte models	ByT5 notes	here	here
CLIP	2021	OpenAI	Learning Transferable Visual Models From Natural Language Supervision	CLIP notes	here	here
DALL-E	2021	OpenAI	Zero-Shot Text-to-Image Generation	DALL-E notes	here	-
Codex	2021	OpenAI	Evaluating Large Language Models Trained on Code	Codex notes	X	-

BigTable

All of the table summaries found ^ collapsed into one really big table here.

Alignment

Paper	Year	Institute	👉 Notes 👈	Codes
Fine-Tuning Language Models from Human Preferences	2019	OpenAI	To-Do	None

Scaling

Paper	Year	Institute	👉 Notes 👈	Codes
Scaling Laws for Neural Language Models	2020	OpenAI	To-Do	None

Memorization

Paper	Year	Institute	👉 Notes 👈	Codes
Extracting Training Data from Large Language Models	2021	Google et al.	To-Do	None
Deduplicating Training Data Makes Language Models Better	2021	Google et al.	To-Do	None

FewLabels

Paper	Year	Institute	👉 Notes 👈	Codes
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP	2021	GIT/UNC	To-Do	None
Learning with fewer labeled examples	2021	Kevin Murphy & Colin Raffel (Preprint: "Probabilistic Machine Learning", Chapter 19)	Worth a read, won't summarize here.	None

Contribute

If you are interested in contributing to this repo, feel free to do the following:

Fork the repo.
Create a Draft PR with the paper of interest (to prevent "in-flight" issues).
Use the suggested template to write your "tl;dr". If it's an architecture paper, you may also want to add to the larger table here.
Submit your PR.

Errata

Undoubtedly there is information that is incorrect here. Please open an Issue and point it out.

Citation

@misc{cliff-notes-transformers,
  author = {Thompson, Will},
  url = {https://github.com/will-thompson-k/cliff-notes-transformers},
  year = {2021}
}

For the notes above, I've linked the original papers.

License

MIT

The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

Related tags

Overview

tldr-transformers

Contents

Quick_Note

Motivation

Models

BigTable

Alignment

Scaling

Memorization

FewLabels

Contribute

Errata

Citation

License

Owner

Will Thompson

[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Pytorch implementation of NeurIPS 2021 paper: Geometry Processing with Neural Fields.

EMNLP 2021 paper The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers.

DLL: Direct Lidar Localization

Edison AT is software Depression Assistant personal.

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Powerful unsupervised domain adaptation method for dense retrieval.

🥈78th place in Riiid Answer Correctness Prediction competition

Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation

Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

PyTorch implementation for Graph Contrastive Learning with Augmentations

Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

PFLD pytorch Implementation

A Python package for performing pore network modeling of porous media

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

Make a Turtlebot3 follow a figure 8 trajectory and create a robot arm and make it follow a trajectory

All supplementary material used by me while TA-ing CS3244: Machine Learning

SCI-AIDE : High-fidelity Few-shot Histopathology Image Synthesis for Rare Cancer Diagnosis

Space-invaders - Simple Game created using Python & PyGame, as my Beginner Python Project