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The NLP Cypher | 05.23.21
Latest   Machine Learning   Newsletter

The NLP Cypher | 05.23.21

Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Photo by Timothy Eberly on Unsplash


The NLP Cypher U+007C 05.23.21


Hey Welcome back, another week goes by and so much code/research has been released into the wild.

Oh and btw, The NLP Index is on U+1F525U+1F525U+1F525 , and I want to thank all contributors!

Here’s a quick glimpse at the awesome contributions: A collection of Spanish Medical NLP datasets brought to you by Salvador Lima in Barcelona. U+1F64CU+1F64C Will update the NLP Index with these and other assets by tomorrow.

Cantemist (oncology clinical cases for cancer text mining):

PharmaCoNER (Pharmacological Substances, Compounds and proteins in Spanish clinical case reports):

CodiEsp (Abstracts from Lilacs and Ibecs with ICD10 codes):

MEDDOCAN (Medical Document Anonymization):

MESINESP2 (Medical Semantic Indexing):

Wav2vec-U: Unsupervised Speech Recognition U+1F60D

This new FAIR model doesn’t need transcriptions to learn speech. It just needs unsupervised speech recordings and text. They used a GAN to help discriminate phonemes (sounds of language). While Wav2vec-U doesn’t achieve SOTA on the Librispeech benchmark, it still gets a pretty good score given the fact it didn’t require 960 hours of transcribed speech data. U+1F440


wav2vec Unsupervised: Speech recognition without supervision

To enable speech recognition technology for many more languages spoken around the globe, Facebook AI is releasing…



Wav2vec Unsupervised (wav2vec-U) is a framework for building speech recognition systems without any labeled training…

Polars Dataframes U+1F601

If you use dataframes often, you should check out Polars. It’s an awesome dataframe library written in Rust (includes Python bindings). Comes with Arrow support and all of its glory including parquet file and AWS S3 IO support.


Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow as memory model. Lazy U+007C eager…


Polars – User Guide

This book is an introduction to the Polars DataFrame library. Its goal is to explain the inner workings of Polars by…

Universiteit van Amsterdam U+007C Notebooks and Tutorials

The University of Amsterdam has a sweet collection of colab notebooks mixing various domains including GNNs, Transformers and computer vision.

Here’s their TOC:

Tutorial 2: Introduction to PyTorch

Tutorial 3: Activation functions

Tutorial 4: Optimization and Initialization

Tutorial 5: Inception, ResNet and DenseNet

Tutorial 6: Transformers and Multi-Head Attention

Tutorial 7: Graph Neural Networks

Tutorial 8: Deep Energy Models

Tutorial 9: Autoencoders

Tutorial 10: Adversarial Attacks

Tutorial 11: Normalizing Flows

Tutorial 12: Autoregressive Image Modeling

Welcome to the UvA Deep Learning Tutorials! — UvA DL Notebooks v1.0 documentation

For this year’s course edition, we created a series of Jupyter notebooks that are designed to help you understanding…

KELM U+007C Converting WikiData to Natural Language

Google introduces the KELM dataset in a huge win for the factoid nerds. The dataset is a Wikidata knowledge graph converted into natural language with the idea of using the corpus for improving the factual knowledge in pretrained models! A T5 was used for this conversion. The corpus consists of ~18M sentences spanning ~45M triples and ~1500 relations.

KELM: Integrating Knowledge Graphs with Language Model Pre-training Corpora

Large pre-trained natural language processing (NLP) models, such as BERT, RoBERTa, GPT-3, T5 and REALM, leverage…

Talkin’ about knowledge graphs…

An Introduction to Knowledge Graphs

Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge, and as…

No Trash Search!

No Trash Search

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LabML.AI Annotated PyTorch Papers

Learn from academic papers annotated with their corresponding code. Pretty cool if you want to decipher research. Annotated PyTorch Paper Implementations

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations…

Completely Normal (aka not suspect) Task


The goal of this task is to retrieve charity address (but not other addresses), charity number, charity name and its…

Repo Cypher U+1F468‍U+1F4BB

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

Measuring Coding Challenge Competence With APPS

A benchmark for code generation.

Check out the GPT-Neo results when compared to GPT-2/3, very interesting.



This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks*, Steven Basart*, Saurav…

Connected Papers U+1F4C8

wikipiifed — Automated Dataset Creation and Federated Learning

A repo for automating dataset creation from wikipedia biography pages and utilizing the dataset for federated learning of BERT based named entity recognizer.


This repo represent the automated dataset creation from wikipedia biography pages and utilizing the dataset for…

Connected Papers U+1F4C8

OpenMEVA Benchmark

OpenMEVA is a benchmark for evaluating open-ended story generation.


Contributed by Jian Guan, Zhexin Zhang. Thank Jiaxin Wen for DeBugging. OpenMEVA is a benchmark for evaluating…

Connected Papers U+1F4C8

KLUE: Korean Language Understanding Evaluation

KLUE benchmark is composed of 8 tasks:

  • Topic Classification (TC)
  • Sentence Textual Similarity (STS)
  • Natural Language Inference (NLI)
  • Named Entity Recognition (NER)
  • Relation Extraction (RE)
  • (Part-Of-Speech) + Dependency Parsing (DP)
  • Machine Reading Comprehension (MRC)
  • Dialogue State Tracking (DST)


The KLUE is introduced to make advances in Korean NLP. Korean pre-trained language models(PLMs) have appeared to solve…

Connected Papers U+1F4C8

Contextual Machine Translation

Context-aware models for document-level machine translation. Also includes SCAT, an English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation.

Most MT models are on the sentence level, so this is an interesting repo for those looking to go onto the document level.


Implementations of context-aware models for document-level translation tasks, used in Measuring and Incresing Context…

Connected Papers U+1F4C8

Dataset of the Week: Few-NERD

What is it?

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).

Sample (in typical NER format)

Between O
1789 O
and O
1793 O
he O
sat O
on O
a O
committee O
reviewing O
the O
administrative MISC-law
constitution MISC-law
of MISC-law
Galicia MISC-law
to O
little O
effect O
. O

Where is it?


This is the source code of the ACL-IJCNLP 2021 paper: Few-NERD: A Few-shot Named Entity Recognition Dataset . Check out…

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

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