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The NLP Cypher | 05.02.21
Latest   Machine Learning   Newsletter

The NLP Cypher | 05.02.21

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July 20, 2023
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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Beware the Beautiful Witch U+007C O’Malley

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 05.02.21

The NLP Index

As an applied machine learning engineer (aka hacker U+1F468‍U+1F4BB aka flying ninja U+1F431‍U+1F464), I’m consistently looking for better and faster ways to stay on top of the deep learning and software development circuit. After comparing various sources for research, code, and apps. I’ve discovered that a significant amount of awesome NLP code is not on arXiv and not all NLP research is on GitHub. To obtain a wider scope of current NLP research and code, I’ve created the NLP Index! A search-as-you-type search engine containing over 3,000 NLP repositories (updated weekly) U+1F525. The index contains the research paper, a ConnectedPapers link for a graph of related papers, and its GitHub repo.

The NLP Index

Top NLP Code Repositories – Quantum Stat

index.quantumstat.com

The intent of this platform is for researchers and hackers to obtain information quickly and comprehensively about all things NLP. And not just from research papers, but from awesome apps that are created on top of this research.

We’ve included the option of open search (as opposed to exclusively only serving pre-defined categories) because of inter-dependencies among subject areas. Meaning, sometimes a paper/repo can be both about “knowledge graphs” and “datasets” simultaneously and it’s difficult to discretize topics. We prefer giving the user the option of openly searching the database across all domains/sectors simultaneously. We also included pre-defined queries with dozens of topics in NLP via the sidebar for convenience.

The index has several attributes such as: search as you type, typo tolerance, and synonym detection.

Synonym Detection

For example, if you search for “dataset” the database will also search for “corpus” and “corpora” text simultaneously to make sure every asset is searched. U+1F91F

Typo Tolerance

If you search “gpt2" it will also include “gpt-2"

Search as you type

It will output results on every character as you type in real-time taking only a couple milliseconds. (thank you memory mapping U+1F648)

Also want to mention that the Big Bad NLP Database has already been merged with the NLP Index! For the most up-to-date compendium of NLP datasets, you can go to the “data” section of the sidebar and click dataset or openly search for a specific dataset/task. Eventually, I will sunset the BBND URL and eventually redirect it to the Index.

Want to thank all of the support I’ve received over the past week after taking the NLP Index live. Thank you to Philip Vollet for sharing his dataset with hundred of NLP repos. You can find his posts in the “Uncharted” section.

More features coming soon. Stay tuned. U+1F649

BERT, Explain Yourself!

Discover why BERT makes an inference using SHAP (SHapley Additive exPlanations); a game theoretic approach to explain the output of any machine learning model. It leverages the Transformers pipeline.

ml6team/quick-tips

It has been over two years since transformer models took the NLP throne U+1F3C5, but up until recently they couldn't tell…

github.com

Colab of the Week

Google Colaboratory

Edit description

colab.research.google.com

Explainable AI Cheat Sheet

Includes graphic, YouTube vid, and several links with papers/ books discussing the topic of explainable AI.

Explainable AI Guide

A brief overview of the Explainable AI cheat sheet with examples.

ex.pegg.io

StyleCLIP is Too Much Fun!

Awesome introduction from Max Woolf on using StyleCLIP (via Colab notebooks) to manipulate headshot pics via text prompts. You can even add your own pictures, the quality is pretty good. For example, take a look at the generation after the text prompt: “Face after using the NLP index” U+1F447 U+1F62DU+1F62D

Easily Transform Portraits of People into AI Aberrations Using StyleCLIP U+007C Max Woolf's Blog

GANs, generative adversarial networks, are all the rage nowadays for creating AI-based imagery. You've probably seen…

minimaxir.com

Software Updates

AdapterHub

New version includes BART and GPT-2 models U+1F6A8

Adapters for Generative and Seq2Seq Models in NLP

Adapters are becoming more and more important in machine learning for NLP. For instance, they enable us to efficiently…

adapterhub.ml

BERTopic

(semi-)supervised topic modeling by leveraging supervised options in UMAP

  • model.fit(docs, y=target_classes)

Backends:

  • Added Spacy, Gensim, USE (TFHub)
  • Use a different backend for document embeddings and word embeddings
  • Create your own backends with bertopic.backend.BaseEmbedder
  • Click here for an overview of all new backends

Calculate and visualize topics per class

  • Calculate: topics_per_class = topic_model.topics_per_class(docs, topics, classes)

Visualize: topic_model.visualize_topics_per_class(topics_per_class)

Release Major Release v0.7 · MaartenGr/BERTopic

The two main features are (semi-)supervised topic modeling and several backends to use instead of Flair and…

github.com

Repo Cypher U+1F468‍U+1F4BB

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

Gradient-based Adversarial Attacks against Text Transformers

A general-purpose framework, GBDA (Gradient-based Distributional Attack), for gradient-based adversarial attacks, and apply it against transformer models on text data.

facebookresearch/text-adversarial-attack

Install HuggingFace dependences conda install -c huggingface transformers pip install datasets (Optional) For attacks…

github.com

Connected Papers U+1F4C8

Easy and Efficient Transformer

Pytorch inference plugin for transformers with large model sizes and long sequences. Currently supports GPT-2 and BERT models.

NetEase-FuXi/EET

EET(Easy and Efficient Transformer) is an efficient Pytorch inference plugin focus on Transformer-based models with…

github.com

Connected Papers U+1F4C8

MDETR: Modulated Detection for End-to-End Multi-Modal Understanding

Code and links to pre-trained models for MDETR (Modulated DETR) for pre-training on data having aligned text and images with box annotations, as well as fine-tuning on tasks requiring fine grained understanding of image and text.

ashkamath/mdetr

This repository contains code and links to pre-trained models for MDETR (Modulated DETR) for pre-training on data…

github.com

Connected Papers U+1F4C8

XLM-T — A Multilingual Language Model Toolkit for Twitter

Continues pre-training on a large corpus of Twitter in multiple languages on the XLM-Roberta-Base model. Includes 4 colab notebooks.

cardiffnlp/xlm-t

This is the XLM-T repository, which includes data, code and pre-trained multilingual language models for Twitter. As…

github.com

Connected Papers U+1F4C8

FRANK: Factuality Evaluation Benchmark

A typology of factual errors for fine-grained analysis of factuality in summarization systems.

artidoro/frank

This repository contains the data for the FRANK Benchmark for factuality evaluation metrics (see our NAACL 2021 paper…

github.com

Connected Papers U+1F4C8

Legal Document Similarity

A collection of state-of-the-art document representation methods for the task of retrieving semantically related US case law. Text-based (e.g., fastText, Transformers), citation-based (e.g., DeepWalk, Poincaré), and
hybrid methods were explored.

malteos/legal-document-similarity

Implementation, trained models and result data for the paper Evaluating Document Representations for Content-based…

github.com

Connected Papers U+1F4C8

Dataset of the Week: Shellcode_IA32 U+1F469‍U+1F4BB

What is it?

Shellcode_IA32 is a dataset containing 20 years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date. This dataset consists of 3,200 examples of instructions in assembly language for IA-32 (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. Dataset is used for automatically generating shell code (code generation task). Assembly programs used to generate shellcode from exploit-db and from shell-storm were collected.

paper

Where is it?

dessertlab/Shellcode_IA32

Shellcode_IA32 is a dataset consisting of challenging but common assembly instructions, collected from real shellcodes…

github.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

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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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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',*/ ]; var replaceText = { '': '', '': '', '
': '
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