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The NLP Cypher | 02.28.21
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The NLP Cypher | 02.28.21

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July 21, 2023
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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

The Human Condition U+007C Magritte

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 02.28.21

Zeroshot

Hey welcome back! Plenty to talk about this week. But first some quick housekeeping. We’ll be updating the Super Duper NLP Repo this upcoming week… finally U+1F601. If you have an awesome NLP notebook to add drop us a note on our contact page and we’ll give you a shoutout on our update tweet. … U+1F680

Ok, if you own a KIA automobile please read this…U+1F447

KIA was apparently hacked with ransomware earlier this month, and the actors want to be paid in full. They are asking for a cool $21 million in BTC. KIA has denied the allegations it was ever hacked although they recently suffered network outages.

Read more here.

Consequences of the Hack U+1F440

“Kia’s key connected services remain offline, meaning customers are unable to pay their car loans, remotely start their vehicles, or other functions using Kia’s infrastructure.” — the drive blog

A *PSEUDO* DALL-E Appears From the Ashes

OpenAI opened the week with a pre-emptive strike w/r/t its DALL-E project by releasing *part* of the model. They released the image reconstruction *part* d-VAE. The actual encoder language model remains out of pocket, and without this, we can’t actually achieve what they demo’d in their paper.

openai/DALL-E

PyTorch package for the discrete VAE used for DALL·E. – openai/DALL-E

github.com

OpenAI’s CLIP Implementations:

If you’re still interested in OpenAI’s CLIP, we found a YUUUGE list on Reddit highlighting a ton of Colab/Jupyter notebooks running the model!! U+1F525U+1F525

U+26A0 WARNING length of list may impair your judgement, we recommend you avoid driving or operating heavy machinery while reading. U+26A0

  1. The Big Sleep: BigGANxCLIP.ipynb — Colaboratory by advadnoun. Uses BigGAN to generate images. To my knowledge, this is the first CLIP-steered BigGAN app that was released. Instructions and examples. Notebook copy by levindabhi.
  2. (Added Feb. 15, 2021) Drive-Integrated The Big Sleep: BigGANxCLIP.ipynb — Colaboratory by advadnoun. Uses BigGAN to generate images.
  3. Big Sleep — Colaboratory by lucidrains. Uses BigGAN to generate images. The GitHub repo has a local machine version. GitHub.
  4. The Big Sleep Customized NMKD Public.ipynb — Colaboratory by nmkd. Uses BigGAN to generate images. Allows multiple samples to be generated in a run.
  5. Text2Image — Colaboratory by tg_bomze. Uses BigGAN to generate images. GitHub.
  6. Text2Image_v2 — Colaboratory by tg_bomze. Uses BigGAN to generate images. GitHub.
  7. Text2Image_v3 — Colaboratory by tg_bomze. Uses BigGAN (default) or Sigmoid to generate images. GitHub.
  8. (Added Feb. 26, 2021) Image Guided Big Sleep Public.ipynb — Colaboratory by jdude_. Uses BigGAN to generate images. Reddit post.
  9. ClipBigGAN.ipynb — Colaboratory by eyaler. Uses BigGAN to generate images/videos. GitHub. Notebook copy by levindabhi.
  10. WanderCLIP.ipynb — Colaboratory by eyaler. Uses BigGAN (default) or Sigmoid to generate images/videos. GitHub.
  11. Story2Hallucination.ipynb — Colaboratory by bonkerfield. Uses BigGAN to generate images/videos. GitHub.
  12. (Added around Feb. 7, 2021) Story2Hallucination_GIF.ipynb — Colaboratory by bonkerfield. Uses BigGAN to generate images. GitHub.
  13. (Added Feb. 24, 2021) Colab-BigGANxCLIP.ipynb — Colaboratory by styler00dollar. Uses BigGAN to generate images. “Just a more compressed/smaller version of that [advadnoun’s] notebook”. GitHub.
  14. CLIP-GLaSS.ipynb — Colaboratory by Galatolo. Uses BigGAN (default) or StyleGAN to generate images. The GPT2 config is for image-to-text, not text-to-image. GitHub.
  15. (Added Feb. 15, 2021) dank.xyz. Uses BigGAN or StyleGAN to generate images. An easy-to-use website for accessing The Big Sleep and CLIP-GLaSS. To my knowledge this site is not affiliated with the developers of The Big Sleep or CLIP-GLaSS. Reddit reference.
  16. (Added Feb. 25, 2021) Aleph-Image: CLIPxDAll-E.ipynb — Colaboratory by advadnoun. Uses DALL-E’s discrete VAE (variational autoencoder) component to generate images. Twitter reference. Reddit post.
  17. (Added Feb. 26, 2021) Aleph2Image (delta): CLIP+DALL-E decoder.ipynb — Colaboratory by advadnoun. Uses DALL-E’s discrete VAE (variational autoencoder) component to generate images. Twitter reference. Reddit post.
  18. (Added Feb. 27, 2021) Copy of working wow good of gamma aleph2img.ipynb — Colaboratory by advadnoun. Uses DALL-E’s discrete VAE (variational autoencoder) component to generate images. Twitter reference.
  19. (Added Feb. 27, 2021) Aleph-Image: CLIPxDAll-E (with white blotch fix #2) — Colaboratory by thomash. Uses DALL-E’s discrete VAE (variational autoencoder) component to generate images. Applies the white blotch fix mentioned here to advadnoun’s “Aleph-Image: CLIPxDAll-E” notebook.
  20. (Added Feb. 14, 2021) GA StyleGAN2 WikiArt CLIP Experiments — Pytorch — clean — Colaboratory by pbaylies. Uses StyleGAN to generate images. More info.
  21. (Added Feb. 15, 2021) StyleCLIP — Colaboratory by orpatashnik. Uses StyleGAN to generate images. GitHub. Twitter reference. Reddit post.
  22. (Added Feb. 15, 2021) StyleCLIP by vipermu. Uses StyleGAN to generate images.
  23. (Added Feb. 24, 2021) CLIP_StyleGAN.ipynb — Colaboratory by levindabhi. Uses StyleGAN to generate images.
  24. (Added Feb. 23, 2021) TediGAN — Colaboratory by weihaox. Uses StyleGAN to generate images. GitHub. I got error “No pre-trained weights found for perceptual model!” when I used the Colab notebook, which was fixed when I made the change mentioned here. After this change, I still got an error in the cell that displays the images, but the results were in the remote file system. Use the “Files” icon on the left to browse the remote file system.
  25. TADNE and CLIP — Colaboratory by nagolinc. Uses TADNE (“This Anime Does Not Exist”) to generate images. GitHub.
  26. CLIP + TADNE (pytorch) v2 — Colaboratory by nagolinc. Uses TADNE (“This Anime Does Not Exist”) to generate images. Instructions and examples. GitHub. Notebook copy by levindabhi
  27. (Added Feb. 24, 2021) clipping-CLIP-to-GAN by cloneofsimo. Uses FastGAN to generate images.
  28. CLIP & gradient ascent for text-to-image (Deep Daze?).ipynb — Colaboratory by advadnoun. Uses SIREN to generate images. To my knowledge, this is the first app released that uses CLIP for steering image creation. Instructions and examples. Notebook copy by levindabhi.
  29. Deep Daze — Colaboratory by lucidrains. Uses SIREN to generate images. The GitHub repo has a local machine version. GitHub. Notebook copy by levindabhi.
  30. CLIP-SIREN-WithSampleDL.ipynb — Colaboratory by norod78. Uses SIREN to generate images.
  31. (Added Feb. 17, 2021) Text2Image Siren+.ipynb — Colaboratory by eps696. Uses SIREN to generate images. Twitter reference. Example #1. Example #2. Example #3.
  32. (Added Feb. 24, 2021) Colab-deep-daze — Colaboratory by styler00dollar. Uses SIREN to generate images. I did not get this notebook to work, but your results may vary. GitHub.
  33. (Added Feb. 18, 2021) Text2Image FFT.ipynb — Colaboratory by eps696. Uses FFT (Fast Fourier Transform) from Lucent/Lucid to generate images. Twitter reference. Example #1. Example #2.

Recent Advances in Language Model Fine-Tuning

Sebastian Ruder, one of the pioneers of transfer learning in NLP, has an awesome new blog post on the recent advances in fine-tuning. The five categories below are discussed. This is a must read if you are anywhere near the realm of NLP.

declassified

Recent Advances in Language Model Fine-tuning

Fine-tuning a pre-trained language model (LM) has become the de facto standard for doing transfer learning in natural…

ruder.io

Visualizing Community Structure in Graphs

Communities is a Python library for detecting community structure in graphs. It implements the following algorithms:

Louvain method

Girvan-Newman algorithm

Hierarchical clustering

Spectral clustering

Bron-Kerbosch algorithm

shobrook/communities

communities is a Python library for detecting community structure in graphs. It implements the following algorithms…

github.com

The Overflow U+007C Python Stats

Helpful post on the Stack Overflow blog discussing Python libraries such as numpy, pandas, matplotlib, and seaborn. There’s a YouTube Video on the page where they explore a New York City housing dataset. This is on the introductory level.

Level Up: Mastering statistics with Python – part 2 – Stack Overflow Blog

Welcome back! This is the second class in our Level Up series. If you're just tuning in, you can catch up on what we're…

stackoverflow.blog

BertViz

BertViz is a tool for visualizing attention in the Transformer model, supporting all models from the transformers library (BERT, GPT-2, XLNet, RoBERTa, XLM, CTRL, etc.)

Includes a Colab U+1F60E

jessevig/bertviz

BertViz is a tool for visualizing attention in the Transformer model, supporting all models from the transformers…

github.com

Contextualized Topic Models

Check out this awesome library if you are into topic modeling. They include a zeroshot cross-lingual variant and also a bag-of-words approach for various use-cases.

MilaNLProc/contextualized-topic-models

Contextualized Topic Models (CTM) are a family of topic models that use pre-trained representations of language (e.g…

github.com

Software UpdatesU+007C PyTorch Lightning

New Features:

PyTorch Lightning V1.2.0

Including new integrations with DeepSpeed, PyTorch profiler, Pruning, Quantization, SWA, PyTorch Geometric and more.

pytorch-lightning.medium.com

Software UpdatesU+007C Hugging Face

A new library from HF for pruning models resulting in fewer parameters while maintaining accuracy. Their sparsity notebook can be found on the Super Duper NLP Repo.

huggingface/nn_pruning

An interactive version of this site is available here. has been proved as a very efficient method to prune networks in…

github.com

Repo Cypher U+1F468‍U+1F4BB

A collection of recently released repos that caught our U+1F441

Few-Shot-Learning

A codebase to perform few-shot “in-context” learning using language models similar to the GPT-3 paper.

tonyzhaozh/few-shot-learning

This is a codebase to perform few-shot “in-context” learning using language models similar to the GPT-3 paper. In…

github.com

Connected Papers U+1F4C8

Graphs and Electronic Health Records

Model outperforms the existing graph and non-graph based methods in various electronic health records predictive tasks based on both public data and real-world clinical data.

NYUMedML/GNN_for_EHR

This repository contains the code for the paper Variationally Regularized Graph-based Representation Learning for…

github.com

Code Generation with GPT-2

Fine-tuning GPT-2 on CodeSearchNet for Code Generation

kandluis/code-gen

From /cs230/gpt-2-csn: $ pip install -r path/to/requirements.txt $ python download_model.py 117M Note that…

github.com

Connected Papers U+1F4C8

REMOD: Relation Extraction for Modeling Online Discourse

sdfsd

mjsumpter/remod

The following are the instructions to strictly reproduce the results cited in the paper. The necessary packages can be…

github.com

Connected Papers U+1F4C8

Argument Structure Prediction

This code can be used to train a set of neural networks to jointly perform Link Prediction, Relation Classification, and Component Classification on select argument mining corpora.

AGalassi/StructurePrediction18

Use of residual deep networks, ensemble learning, and attention for Argument Structure Prediction. This code can be…

github.com

Connected Papers U+1F4C8

Dataset of the Week: EmotionGIF 2020 Challenge

What is it?

Dataset contains 40,000 tweets and their GIF responses, labeled with the categories of the animated GIFs. The challenge: given unlabeled tweets, predict the category/categories of a GIF response. The GIFs are stored as MP4 files.

Sample

“idx”: 32,

“text”: “Fell right under my trap”,

“reply”: “Ouch!”,

“categories”: [‘awww’,’yes’,’oops’],

“mp4”: “fe6ec1cd04cd009f3a5975e2d288ff82.mp4”

Where is it?

EmotionGIF 2020 – Dataset

The EmotionGIF 2020 Challenge has ended. The big winner of both Rounds, with a total of participating teams, is Team…

sites.google.com

Every Sunday we do a weekly round-up of NLP news and code drops from researchers around the world.

For complete coverage, follow our Twitter: @Quantum_Stat

Quantum Stat

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Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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