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The NLP Cypher | 12.06.20
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The NLP Cypher | 12.06.20

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July 19, 2023
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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Landscape with a Marsh (Bril)

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 12.06.20

Orion

Hey, welcome back! Plenty of NLP to discuss this week as NeurIPS takes off today. Over the last couple of days, the usual suspects opened the research paper firehose. Have a look U+1F447

Carnegie Mellon University at NeurIPS 2020

Carnegie Mellon University is proud to present 88 papers at the 34th Conference on Neural Information Processing…

blog.ml.cmu.edu

OpenAI at NeurIPS 2020

Live demos and discussions at our virtual booth.

openai.com

Microsoft at NeurIPS 2020 – Microsoft Research

Microsoft is delighted to sponsor and attend the 34th Annual Conference on Neural Information Processing System…

www.microsoft.com

Salesforce Research at NeurIPS 2020

This year marks the 34th annual conference on Neural Information Processing Systems (NeurIPS) reimagined for the first…

blog.einstein.ai

Super Duper NLP Repo U+270C

We recently made an awesome contribution to the Super Duper NLP Repo, adding 47 notebooks bringing us to 313 total! Added a decent selection of notebooks relating to adapters, the NEMO library, GEDI GPT-2, and PERIN for semantic parsing. Want to thank Abhilash Majumder & Eyal Gruss for their awesome contribution! U+1F60E

Oh, and EMNLP has yet to go away, Eric Wallace et al. released his slides from the conference on the interpretability of NLP models predictions.

ToC

(1) Overview of Interpretability

(2) What Parts of An Input Led to a Prediction?

(3) What Decision Rules Led to a Prediction?

(4) Which Training Examples Caused a Prediction?

(5) Implementing Interpretations

(6) Open Problems

declassified

Jraph U+007C DeepMind’s GNN Lib

While DeepMind isn’t solving age-old problems in protein folding, they just released a GNN library (in jax). It probably flew under everyone’s radar…

Here’s a basic script for working with graph tuples:

deepmind/jraph

Permalink GitHub is home to over 50 million developers working together to host and review code, manage projects, and…

github.com

El GitHub

deepmind/jraph

Jraph (pronounced giraffe) is a lightweight library for working with graph neural networks in jax. It provides a data…

github.com

Kaggle Data Science and ML 2020 Survey

Everyone’s favorite data science survey was released:

TL;DR

Coursera most popular learning resource.

A lot data scientists working in small companies (less than 50 employees).

Wow, Jupyter is the go-to IDE in data science(U+1F62C).

Only 15% say transformers are the most commonly used model architecture.

AWS leads cloud, but Google comes in 2nd, (that was a surprise, I would’ve guessed Azure).

Tensorboard more popular than I thought.

Survey

State of Data Science and Machine Learning 2020

Download our executive summary for a profile of today's working data scientist and their tools

www.kaggle.com

Data Flow

A blog from Google Cloud (with code snippets) discussing how to create data pipelines for your ML models. It focuses on batching, the singleton model pattern, and dealing with threading/processing. A helpful read for those deploying in the enterprise.

ML inference in Dataflow pipelines U+007C Google Cloud Blog

In this blog, we covered some of the patterns for running remote/local inference calls, including; batching, the…

cloud.google.com

MSFP U+007C Data Type for Efficient Inference

Microsoft invented a new data type used in data representation with a focus on improved latency during model inference called… MSFP.

[MSFP] enables dot product operations — the core of the matrix-matrix and matrix-vector multiplication operators critical to DNN inference — to be performed nearly as efficiently as with integer data types, but with accuracy comparable to floating point.

Apparently MS uses MSFP in Project Brainwave, their real-time production-scale DNN inference in the cloud. As models get bigger, big tech is getting smarter on how to deal with scale and inference in production.

A Microsoft custom data type for efficient inference – Microsoft Research

AI is taking on an increasingly important role in many Microsoft products, such as Bing and Office 365. In some cases…

www.microsoft.com

Recommenders Update

When we first spoke about TensorFlow’s Recommenders library several newsletters ago, I was really excited but TF has upped the ante by building deep learning recommender models “that can retrieve the best candidates out of millions in milliseconds.” U+1F440

It uses Google’s ScaNN library released this past summer, you can check out the repo here: https://github.com/google-research/google-research/tree/master/scann

The second part of their update is their leveraging of DCN (Deep cross networks) models.

TensorFlow Recommenders: Scalable retrieval and feature interaction modelling

November 30, 2020 – Posted by Ruoxi Wang, Phil Sun, Rakesh Shivanna and Maciej Kula (Google) In September, we…

blog.tensorflow.org

Repo Cypher U+1F468‍U+1F4BB

A collection of repos/papers that caught our U+1F441

DframCy

DframCy provides clean APIs to convert spaCy’s linguistic annotations, Matcher and PhraseMatcher information to Pandas dataframe.

yash1994/dframcy

DframCy is a light-weight utility module to integrate Pandas Dataframe to spaCy's linguistic annotation and training…

github.com

Wolfram’s Model Stash

Wolfram has his own Deep Learning model hub. Just stumbled upon this one when I saw one of Wolfram’s tweets earlier this week. U+1F648

Wolfram Neural Net Repository

The Wolfram Neural Net Repository is a public resource that hosts an expanding collection of trained and untrained…

resources.wolframcloud.com

Novel2Graph

The algorithm receives a book and it discovers main characters and main relations between characters.

Oldie but goodie.

IDSIA/novel2graph

The algorithm receives a book and it discovers main characters, main relations between characters and more powerful…

github.com

EDGEBert

New research paper on the improvement of memory and latency w/r/t BERT inference that utilizes several techniques in compression and model architecture. The authors boast of “achieving up to 2.4× and 13.4× inference latency and memory savings, respectively, with less than 1%-pt. drop in accuracy.” U+1F440

Paper: https://arxiv.org/pdf/2011.14203.pdf

OCR and Deep Learning

Couple of weeks ago on LinkedIn I posted a question regarding current OCR techniques that led to a great discussion with my connections. This week, I found this U+1F447. WINNING!

Paper: https://arxiv.org/pdf/2011.13534.pdf

Long Text Classification with BERT

Looking to classify text documents with more than 250 words per doc?

Notebook (U+1F525)

ArmandDS/bert_for_long_text

Permalink GitHub is home to over 50 million developers working together to host and review code, manage projects, and…

github.com

Blog

Using BERT For Classifying Documents with Long Texts

How to fine-tuning Bert for inputs longer than a few words or sentences

medium.com

Dataset of the Week: XED

What is it?

A multi-lingual dataset consisting of emotion annotated movie subtitles from OPUS used for sentiment analysis. The task is formulated as multi-label classification.

Where is it?

Helsinki-NLP/XED

This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core…

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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} 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(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${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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