This pandect (πανδέκτης is Ancient Greek for encyclopedia) was created to help you find almost anything related to Natural Language Processing that is available online.
- The NLP Index by Quantum Stat / NLP Cypher
- Awesome NLP by keon [GitHub, 12197 stars]
- Speech and Natural Language Processing Awesome List by elaboshira [GitHub, 2028 stars]
- Awesome Deep Learning for Natural Language Processing (NLP) [GitHub, 945 stars]
- Text Mining and Natural Language Processing Resources by stepthom [GitHub, 415 stars]
- Made with ML List by madewithml.com
- Brainsources for #NLP enthusiasts by Philip Vollet
- Awesome AI/ML/DL - NLP Section [GitHub, 928 stars]
- Resources on various machine learning topics by Backprop
- 100 Must-Read NLP Papers 100 Must-Read NLP Papers [GitHub, 3155 stars]
- NLP Paper Summaries by dair-ai [GitHub, 1359 stars]
- Curated collection of papers for the NLP practitioner [GitHub, 1051 stars]
- Papers on Textual Adversarial Attack and Defense [GitHub, 894 stars]
- The Most Influential NLP Research of 2019
- Recent Deep Learning papers in NLU and RL by Valentin Malykh [GitHub, 287 stars]
- Some Notable Recent ML Papers and Future Trends by Aran Komatsuzaki [Blog, Oct. 2020]
- A Survey of Surveys (NLP & ML): Collection of NLP Survey Papers [GitHub, 1317 stars]
- A Paper List for Style Transfer in Text [GitHub, 1177 stars]
- Video recordings index for papers
- NLP top 10 conferences Compendium by soulbliss [GitHub, 375 stars]
- NLP Conferences Calendar
- ICLR 2020 Trends
- SpacyIRL 2019 Conference in Overview
- Paper Digest - Conferences and Papers in Overview
- Video Recordings from Conferences
- NLP Progress by sebastianruder [GitHub, 18873 stars]
- NLP Tasks by Kyubyong [GitHub, 2923 stars]
- Reading list for Awesome Sentiment Analysis papers by declare-lab [GitHub, 369 stars]
- Awesome Sentiment Analysis by xiamx [GitHub, 842 stars]
- NLP Datasets by niderhoff [GitHub, 4744 stars]
- Datasets by Huggingface [GitHub, 8713 stars]
- Big Bad NLP Database
- 25 Best Parallel Text Datasets for Machine Translation Training
- UWA Unambiguous Word Annotations - Word Sense Disambiguation Dataset
- 20 Best German Language Datasets for Machine Learning
- Awesome Embedding Models by Hironsan [GitHub, 1458 stars]
- Awesome list of Sentence Embeddings by Separius [GitHub, 1909 stars]
- Awesome BERT by Jiakui [GitHub, 1692 stars]
- The Super Duper NLP Repo [Website, 2020]
- NLP Resources for Bahasa Indonesian [GitHub, 195 stars]
- Indic NLP Catalog [GitHub, 274 stars]
- Pre-trained language models for Vietnamese [GitHub, 385 stars]
- Natural Language Toolkit for Indic Languages (iNLTK) [GitHub, 724 stars]
- Indic NLP Library [GitHub, 373 stars]
- AI4Bharat-IndicNLP Portal
- ARBML - Implementation of many Arabic NLP and ML projects [GitHub, 182 stars]
- zemberek-nlp - NLP tools for Turkish [GitHub, 941 stars]
- KLUE - Korean Language Understanding Evaluation [GitHub, 330 stars]
- List of pre-trained NLP models [GitHub, 134 stars]
- History of Natural Language Processing
- A Review of the Neural History of Natural Language Processing [Blog, October 2018]
- Natural Language Processing in 2020: The Year In Review [Blog, December 2020]
- ML and NLP Research Highlights of 2020 [Blog, January 2021]
- NLP Highlights [Years: 2017 - now, Status: active]
- The NLP Zone Episodes [Years: 2021 - now, Status: active]
- TWIML AI [Years: 2016 - now, Status: active]
- Practical AI [Years: 2018 - now, Status: active]
- The Data Exchange [Years: 2019 - now, Status: active]
- Gradient Dissent [Years: 2020 - now, Status: active]
- Machine Learning Street Talk [Years: 2020 - now, Status: active]
- The Super Data Science Podcast [Years: 2016 - now, Status: active]
- Data Hack Radio [Years: 2018 - now, Status: active]
- AI Game Changers [Years: 2020 - now, Status: active]
- The Analytics Show [Years: 2019 - now, Status: active]
- NLP News by Sebastian Ruder
- dair.ai Newsletter by dair.ai
- This Week in NLP by Robert Dale
- Papers with Code
- The Batch by deeplearning.ai
- Paper Digest by PaperDigest
- NLP Cypher by QuantumStat
- NLP Zurich [YouTube Recordings]
- NY-NLP (New York)
- Online NLP Meetup
- Hacking-Machine-Learning [YouTube Recordings]
- Yannic Kilcher
- HuggingFace
- Kaggle Reading Group
- Rasa Paper Reading
- Stanford CS224N: NLP with Deep Learning
- NLPxing
- ML Explained - A.I. Socratic Circles - AISC
- Deeplearning.ai
- Machine Learning Street Talk
- GLUE - General Language Understanding Evaluation (GLUE) benchmark
- SuperGLUE - benchmark styled after GLUE with a new set of more difficult language understanding tasks
- decaNLP - The Natural Language Decathlon (decaNLP) for studying general NLP models
- RACE - ReAding Comprehension dataset collected from English Examinations
- dialoglue - DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue
- DynaBench - Dynabench is a research platform for dynamic data collection and benchmarking
- WikiAsp - WikiAsp: Multi-document aspect-based summarization Dataset
- SQuAD - Stanford Question Answering Dataset (SQuAD)
- XQuad - XQuAD (Cross-lingual Question Answering Dataset) for cross-lingual question answering
- GrailQA - Strongly Generalizable Question Answering (GrailQA)
- CSQA - Complex Sequential Question Answering
- XTREME - Massively Multilingual Multi-task Benchmark
- GLUECoS - A benchmark for code-switched NLP
- IndoNLU Benchmark - collection of resources for training, evaluating, and analyzing NLP for Bahasa Indonesia
- IndicGLUE - Natural Language Understanding Benchmark for Indic Languages
- LinCE - Linguistic Code-Switching Evaluation Benchmark
- Russian SuperGlue - Russian SuperGlue Benchmark
- BLURB - Biomedical Language Understanding and Reasoning Benchmark
- BLUE - Biomedical Language Understanding Evaluation benchmark
- Long-Range Arena - Long Range Arena for Benchmarking Efficient Transformers (Pre-print) [GitHub, 315 stars]
- SUPERB - Speech processing Universal PERformance Benchmark
- CodeXGLUE - A benchmark dataset for code intelligence
- CrossNER - CrossNER: Evaluating Cross-Domain Named Entity Recognition
- MultiNLI - Multi-Genre Natural Language Inference corpus
- A Recipe for Training Neural Networks by Andrej Karpathy [Keywords: research, training, 2019]
- Pre-trained ELMo Representations for Many Languages [GitHub, 1342 stars]
- sense2vec - Contextually-keyed word vectors [GitHub, 1274 stars]
- wikipedia2vec [GitHub, 712 stars]
- StarSpace [GitHub, 3645 stars]
- fastText [GitHub, 22815 stars]
- Language Models and Contextualised Word Embeddings by David S. Batista [Blog, 2018]
- An Essential Guide to Pretrained Word Embeddings for NLP Practitioners by AnalyticsVidhya [Blog, 2020]
- Polyglot Word Embeddings Discover Language Clusters [Blog, 2020]
- The Illustrated Word2vec by Jay Alammar [Blog, 2019]
- vecmap - VecMap (cross-lingual word embedding mappings) [GitHub, 559 stars]
- sentence-transformers - Multilingual Sentence & Image Embeddings with BERT [GitHub, 5733 stars]
- bpemb - Pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) [GitHub, 982 stars]
- subword-nmt - Unsupervised Word Segmentation for Neural Machine Translation and Text Generation [GitHub, 1750 stars]
- python-bpe - Byte Pair Encoding for Python [GitHub, 147 stars]
- The Transformer Family by Lilian Weng [Blog, 2020]
- Keeping up with the BERTs: a review of the main NLP benchmarks by Manuel Tonneau [Blog, 2020]
- Playing the lottery with rewards and multiple languages - about the effect of random initialization [ICLR 2020 Paper]
- Attention? Attention! by Lilian Weng [Blog, 2018]
- the transformer … “explained”? [Blog, 2019]
- Attention is all you need; Attentional Neural Network Models by Łukasz Kaiser [Talk, 2017]
- Understanding and Applying Self-Attention for NLP [Talk, 2018]
- The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures [Paper, April 2021]
- Pre-Trained Models: Past, Present and Future [Paper, June 2021]
- A Survey of Transformers [Paper, June 2021]
- The Annotated Transformer by Harvard NLP [Blog, 2018]
- The Illustrated Transformer by Jay Alammar [Blog, 2018]
- Illustrated Guide to Transformers by Hong Jing [Blog, 2020]
- Sequential Transformer with Adaptive Attention Span by Facebook. Blog [Blog, 2019]
- Evolution of Representations in the Transformer by Lena Voita [Blog, 2019]
- Reformer: The Efficient Transformer [Blog, 2020]
- Longformer — The Long-Document Transformer by Viktor Karlsson [Blog, 2020]
- TRANSFORMERS FROM SCRATCH [Blog, 2019]
- Universal Transformers by Mostafa Dehghani [Blog, 2019]
- Transformers in Natural Language Processing — A Brief Survey by George Ho [Blog, May 2020]
- Lite Transformer - Lite Transformer with Long-Short Range Attention [GitHub, 457 stars]
- A Visual Guide to Using BERT for the First Time by Jay Alammar [Blog, 2019]
- The Dark Secrets of BERT by Anna Rogers [Blog, 2020]
- Understanding searches better than ever before [Blog, 2019]
- Demystifying BERT: A Comprehensive Guide to the Groundbreaking NLP Framework [Blog, 2019]
- SemBERT - Semantics-aware BERT for Language Understanding [GitHub, 227 stars]
- BERTweet - BERTweet: A pre-trained language model for English Tweets [GitHub, 343 stars]
- Optimal Subarchitecture Extraction for BERT [GitHub, 435 stars]
- CharacterBERT: Reconciling ELMo and BERT [GitHub, 114 stars]
- When BERT Plays The Lottery, All Tickets Are Winning [Blog, Dec 2020]
- T5 Understanding Transformer-Based Self-Supervised Architectures [Blog, August 2020]
- T5: the Text-To-Text Transfer Transformer [Blog, 2020]
- multilingual-t5 - Multilingual T5 (mT5) is a massively multilingual pretrained text-to-text transformer model [GitHub, 697 stars]
- Big Bird: Transformers for Longer Sequences original paper by Google Research [Paper, July 2020]
- Reformer: The Efficient Transformer - [Paper, February 2020] [Video, October 2020]
- Longformer: The Long-Document Transformer - [Paper, April 2020] [Video, April 2020]
- Linformer: Self-Attention with Linear Complexity - [Paper, June 2020] [Video, June 2020]
- Rethinking Attention with Performers - [Paper, September 2020] [Video, September 2020]
- performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch [GitHub, 674 stars]
- Switch Transformers: Scaling to Trillion Parameter Models original paper by Google Research [Paper, January 2021]
- The Illustrated GPT-2 by Jay Alammar [Blog, 2019]
- The Annotated GPT-2 by Aman Arora
- OpenAI’s GPT-2: the model, the hype, and the controversy by Ryan Lowe [Blog, 2019]
- How to generate text by Patrick von Platen [Blog, 2020]
- Zero Shot Learning for Text Classification by Amit Chaudhary [Blog, 2020]
- GPT-3 A Brief Summary by Leo Gao [Blog, 2020]
- GPT-3, a Giant Step for Deep Learning And NLP by Yoel Zeldes [Blog, June 2020]
- GPT-3 Language Model: A Technical Overview by Chuan Li [Blog, June 2020]
- Is it possible for language models to achieve language understanding? by Christopher Potts
- Aweseome GPT-3 - list of all resources related to GPT-3 [GitHub, 3256 stars]
- GPT-3 Projects - a map of all GPT-3 start-ups and commercial projects
- GPT-3 Demo Showcase - GPT-3 Demo Showcase, 180+ Apps, Examples, & Resources
- OpenAI API - API Demo to use GPT-3 for commercial applications
- GPT-Neo - in-progress GPT-3 open source replication HuggingFace Hub
- GPT-J - A 6 billion parameter, autoregressive text generation model trained on The Pile
- Effectively using GPT-J with few-shot learning [Blog, July 2021]
- What is Two-Stream Self-Attention in XLNet by Xu LIANG [Blog, 2019]
- Visual Paper Summary: ALBERT (A Lite BERT) by Amit Chaudhary [Blog, 2020]
- Turing NLG by Microsoft
- Multi-Label Text Classification with XLNet by Josh Xin Jie Lee [Blog, 2019]
- ELECTRA [GitHub, 1842 stars]
- Performer implementation of Performer, a linear attention-based transformer, in Pytorch [GitHub, 674 stars]
- Distilling knowledge from Neural Networks to build smaller and faster models by FloydHub [Blog, 2019]
- David over Goliath: towards smaller models for cheaper, faster, and greener NLP by Manuel Tonneau [Blog, 2020]
- Compression of Deep Learning Models for Text: A Survey (+Overview of Approaches) [Paper, April 2021]
- PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization by Google AI [Blog, June 2020]
- CTRLsum - CTRLsum: Towards Generic Controllable Text Summarization [GitHub, 67 stars]
- XL-Sum - XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages [GitHub, 97 stars]
- In Search of Best Practices for NLP Projects [Slides, Dec. 2020]
- EMNLP 2020: High Performance Natural Language Processing by Google Research [Slides, Recording, Nov. 2020]
- Practical Natural Language Processing - A Comprehensive Guide to Building Real-World NLP Systems [Book, June 2020]
- How to Structure and Manage NLP Projects [Blog, May 2021]
- Applied NLP Thinking - Applied NLP Thinking: How to Translate Problems into Solutions [Blog, June 2021]
MLOps, especially when applied to NLP, is a set of best practices around automating various parts of the workflow when building and deploying NLP pipelines.
In general, MLOps for NLP includes having the following processes in place:
- Data Versioning - make sure your training, annotation and other types of data are versioned and tracked
- Experiment Tracking - make sure that all of your experiments are automatically tracked and saved where they can be easily replicated or retraced
- Model Registry - make sure any neural models you train are versioned and tracked and it is easy to roll back to any of them
- Automated Testing and Behavioral Testing - besides regular unit and integration tests, you want to have behavioral tests that check for bias or potential adversarial attacks
- Model Deployment and Serving - automate model deployment, ideally also with zero-downtime deploys like Blue/Green, Canary deploys etc.
- Data and Model Observability - track data drift, model accuracy drift etc.
Additionally, there are two more components that are not as prevalent for NLP and are mostly used for Computer Vision and other sub-fields of AI:
- Feature Store - centralized storage of all features developed for ML models than can be easily reused by any other ML project
- Metadata Management - storage for all information related to the usage of ML models, mainly for reproducing behavior of deployed ML models, artifact tracking etc.
- MLOps: What It Is, Why it Matters, and How To Implement It by Neptune AI [Blog, July 2021]
- Best MLOps Tools You Need to Know as a Data Scientist by Neptune AI [Blog, July 2021]
- Robust MLOps - Robust MLOps with Open-Source: ModelDB, Docker, Jenkins and Prometheus [Blog, May 2021]
- State of MLOps 2021 by Valohai [Blog, August 2021]
- The MLOps Stack by Valohai [Blog, October 2020]
- Data Version Control for Machine Learning Applications by Megagon AI [Blog, July 2021]
- The Rapid Evolution of the Canonical Stack for Machine Learning [Blog, July 2021]
- MLOps: Comprehensive Beginner’s Guide [Blog, March 2021]
- What I’ve learned about MLOps from speaking with 100+ ML practitioners [Blog, May 2021]
- DataRobot Challenger Models - MLOps Champion/Challenger Models
- State of MLOps Blog by Dr. Ori Cohen
- MLOps cource by Made With ML
- GitHub MLOps - collection of resources on how to facilitate Machine Learning Ops with GitHub
- The MLOps Community - blogs, slack group, newsletter and more all about MLOps
- DVC - Data Version Control (DVC) tracks ML models and data sets [Free and Open Source] Link to GitHub
- Weights & Biases - tools for experiment tracking and dataset versioning [Paid Service]
- Pachyderm - version control for data with the tools to build scalable end-to-end ML/AI pipelines [Paid Service with Free Tier]
- mlflow - open source platform for the machine learning lifecycle [Free and Open Source] Link to GitHub
- Weights & Biases - tools for experiment tracking and dataset versioning [Paid Service]
- Neptune AI - experiment tracking and model registry built for research and production teams [Paid Service]
- Comet ML - enables data scientists and teams to track, compare, explain and optimize experiments and models [Paid Service]
- SigOpt - automate training & tuning, visualize & compare runs [Paid Service]
- Optuna - hyperparameter optimization framework [GitHub, 4894 stars]
- Clear ML - experiment, orchestrate, deploy, and build data stores, all in one place [Free and Open Source] Link to GitHub
- Metaflow - human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects [GitHub, 4500 stars]
- DVC - Data Version Control (DVC) tracks ML models and data sets [Free and Open Source] Link to GitHub
- mlflow - open source platform for the machine learning lifecycle [Free and Open Source] Link to GitHub
- ModelDB - open-source system for Machine Learning model versioning, metadata, and experiment management [GitHub, 1301 stars]
- Neptune AI - experiment tracking and model registry built for research and production teams [Paid Service]
- Valohai - End-to-end ML pipelines [Paid Service]
- Pachyderm - version control for data with the tools to build scalable end-to-end ML/AI pipelines [Paid Service with Free Tier]
- polyaxon - reproduce, automate, and scale your data science workflows with production-grade MLOps tools [Paid Service]
- Comet ML - enables data scientists and teams to track, compare, explain and optimize experiments and models [Paid Service]
- CheckList - Beyond Accuracy: Behavioral Testing of NLP models [GitHub, 1452 stars]
- TextAttack - framework for adversarial attacks, data augmentation, and model training in NLP [GitHub, 1568 stars]
- WildNLP - Corrupt an input text to test NLP models' robustness [GitHub, 66 stars]
- Great Expectations - Write tests for your data [GitHub, 4768 stars]
- mlflow - open source platform for the machine learning lifecycle [Free and Open Source] Link to GitHub
- Amazon SageMaker [Paid Service]
- Valohai - End-to-end ML pipelines [Paid Service]
- NLP Cloud - Production-ready NLP API [Paid Service]
- Saturn Cloud [Paid Service]
- SELDON - machine learning deployment for enterprise [Paid Service]
- Comet ML - enables data scientists and teams to track, compare, explain and optimize experiments and models [Paid Service]
- polyaxon - reproduce, automate, and scale your data science workflows with production-grade MLOps tools [Paid Service]
- TorchServe - flexible and easy to use tool for serving PyTorch models [GitHub, 1999 stars]
- Kubeflow - The Machine Learning Toolkit for Kubernetes [GitHub, 10600 stars]
- KFServing - Serverless Inferencing on Kubernetes [GitHub, 1013 stars]
- TFX - TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines [Paid Service]
- Pachyderm - version control for data with the tools to build scalable end-to-end ML/AI pipelines [Paid Service with Free Tier]
- Cortex - containers as a service on AWS [Paid Service]
- Azure Machine Learning - end-to-end machine learning lifecycle [Paid Service]
- End2End Serverless Transformers On AWS Lambda [GitHub, 73 stars]
- NLP-Service - sample demo of NLP as a service platform built using FastAPI and Hugging Face [GitHub, 11 stars]
- Dagster - data orchestrator for machine learning [Free and Open Source]
- Verta - AI and machine learning deployment and operations [Paid Service]
- Metaflow - human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects [GitHub, 4500 stars]
- flyte - workflow automation platform for complex, mission-critical data and ML processes at scale [GitHub, 1600 stars]
- MLRun - Machine Learning automation and tracking [GitHub, 420 stars]
- DataRobot MLOps - DataRobot MLOps provides a center of excellence for your production AI
- whylogs - open source standard for data and ML logging [GitHub, 515 stars]
- Rubrix - open-source tool for exploring and iterating on data for artificial intelligence projects [GitHub, 154 stars]
- MLRun - Machine Learning automation and tracking [GitHub, 420 stars]
- DataRobot MLOps - DataRobot MLOps provides a center of excellence for your production AI
- Cortex - containers as a service on AWS [Paid Service]
- Algorithmia - minimize risk with advanced reporting and enterprise-grade security and governance across all data, models, and infrastructure [Paid Service]
- Dataiku - dataiku is for teams who want to deliver advanced analytics using the latest techniques at big data scale [Paid Service]
- Evidently AI - tools to analyze and monitor machine learning models [Free and Open Source] Link to GitHub
- Fiddler - ML Model Performance Management Tool [Paid Service]
- Hydrosphere - open-source platform for managing ML models [Paid Service]
- Verta - AI and machine learning deployment and operations [Paid Service]
- Domino Model Ops - Deploy and Manage Models to Drive Business Impact [Paid Service]
- iguazio - deployment and management of your AI applications with MLOps and end-to-end automation of machine learning pipelines [Paid Service]
- Datafold - data quality through diffs, profiling, and anomaly detection [Paid Service]
- acceldata - improve reliability, accelerate scale, and reduce costs across all data pipelines [Paid Service]
- Bigeye - monitoring and alerting to your datasets in minutes [Paid Service]
- datakin - end-to-end, real-time data lineage solution [Paid Service]
- Monte Carlo - data integrity, drifts, schema, lineage [Paid Service]
- SODA - data monitoring, testing and validation [Paid Service]
- whatify - data quality and action recommendation on it [Paid Service]
- Tecton - enterprise feature store for machine learning [Paid Service]
- FEAST - open source feature store for machine learning Website [GitHub, 2084 stars]
- Hopsworks Feature Store - data management system for managing machine learning features [Paid Service]
- ML Metadata - a library for recording and retrieving metadata associated with ML developer and data scientist workflows [GitHub, 358 stars]
- Neptune AI - experiment tracking and model registry built for research and production teams [Paid Service]
- Metaflow - human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects [GitHub, 4500 stars]
- kedro - Python framework for creating reproducible, maintainable and modular data science code [GitHub, 4200 stars]
- Seldon Core - MLOps framework to package, deploy, monitor and manage thousands of production machine learning models [GitHub, 2500 stars]
- ZenML - MLOps framework to create reproducible ML pipelines for production machine learning [GitHub, 1200 stars]
- Google Vertex AI - build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified AI platform [Paid Service]
- Why BERT Fails in Commercial Environments by Intel AI [Blog, 2020]
- Fine Tuning BERT for Text Classification with FARM by Sebastian Guggisberg [Blog, 2020]
- Pretrain Transformers Models in PyTorch using Hugging Face Transformers [GitHub, 106 stars]
- Practical NLP for the Real World [Presentation, 2019]
- From Paper to Product – How we implemented BERT by Christoph Henkelmann [Talk, 2020]
- Parallelformers: An Efficient Model Parallelization Toolkit for Deployment [GitHub, 333 stars]
- embedding-as-service [GitHub, 157 stars]
- Bert-as-service [GitHub, 9449 stars]
- NLP Recipes by microsoft [GitHub, 5618 stars]
- NLP with Python by susanli2016 [GitHub, 2079 stars]
- Basic Utilities for PyTorch NLP by PetrochukM [GitHub, 1943 stars]
- Blackstone - A spaCy pipeline and model for NLP on unstructured legal text [GitHub, 496 stars]
- Sci spaCy - spaCy pipeline and models for scientific/biomedical documents [GitHub, 975 stars]
- FinBERT: Pre-Trained on SEC Filings for Financial NLP Tasks [GitHub, 147 stars]
- LexNLP - Information retrieval and extraction for real, unstructured legal text [GitHub, 464 stars]
- NerDL and NerCRF - Tutorial on Named Entity Recognition for Healthcare with SparkNLP
- Legal Text Analytics - A list of selected resources dedicated to Legal Text Analytics [GitHub, 296 stars]
- BioIE - A curated list of resources relevant to doing Biomedical Information Extraction [GitHub, 159 stars]
- wav2letter - Automatic Speech Recognition Toolkit [GitHub, 5811 stars]
- DeepSpeech - Baidu's DeepSpeech architecture [GitHub, 17826 stars]
- Acoustic Word Embeddings by Maria Obedkova [Blog, 2020]
- kaldi - Kaldi is a toolkit for speech recognition [GitHub, 10711 stars]
- awesome-kaldi - resources for using Kaldi [GitHub, 434 stars]
- ESPnet - End-to-End Speech Processing Toolkit [GitHub, 4015 stars]
- HuBERT - Self-supervised representation learning for speech recognition, generation, and compression [Blog, June 2021]
- FastSpeech - The Implementation of FastSpeech based on pytorch [GitHub, 637 stars]
- TTS - a deep learning toolkit for Text-to-Speech [GitHub, 1954 stars]
- VoxPopuli - large-scale multilingual speech corpus for representation learning [GitHub, 199 stars]
- Topic Modelling with PySpark and Spark NLP by Maria Obedkova [Spark, Blog, 2020]
- Top2Vec [GitHub, 1217 stars]
- Anchored Correlation Explanation Topic Modeling [GitHub, 273 stars]
- Topic Modeling in Embedding Spaces [GitHub, 359 stars] Paper
- TopicNet - A high-level interface for BigARTM library [GitHub, 108 stars]
- BERTopic - Leveraging BERT and a class-based TF-IDF to create easily interpretable topics [GitHub, 1294 stars]
- OCTIS - A python package to optimize and evaluate topic models [GitHub, 206 stars]
- Contextualized Topic Models [GitHub, 578 stars]
- PyTextRank - PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension [GitHub, 1584 stars]
- textrank - TextRank implementation for Python 3 [GitHub, 1045 stars]
- rake-nltk - Rapid Automatic Keyword Extraction algorithm using NLTK [GitHub, 835 stars]
- yake - Single-document unsupervised keyword extraction [GitHub, 735 stars]
- RAKE-tutorial - A python implementation of the Rapid Automatic Keyword Extraction [GitHub, 352 stars]
- rake-nltk - Rapid Automatic Keyword Extraction algorithm using NLTK [GitHub, 835 stars]
- flashtext - Extract Keywords from sentence or Replace keywords in sentences [GitHub, 4891 stars]
- BERT-Keyword-Extractor - Deep Keyphrase Extraction using BERT [GitHub, 191 stars]
- keyBERT - Minimal keyword extraction with BERT [GitHub, 754 stars]
- Adding a custom tokenizer to spaCy and extracting keywords from Chinese texts by Haowen Jiang [Blog, Feb 2021]
- How to Extract Relevant Keywords with KeyBERT [Blog, June 2021]
- Language Interpretability Tool (LIT) [GitHub, 2610 stars]
- WhatLies - Toolkit to help visualise - what lies in word embeddings [GitHub, 288 stars]
- Interpret-Text - Interpretability techniques and visualization dashboards for NLP models [GitHub, 261 stars]
- InterpretML - Fit interpretable models. Explain blackbox machine learning [GitHub, 3987 stars]
- ecco - Tools to visuals and explore NLP language models [GitHub, 854 stars]
- NLP Profiler - A simple NLP library allows profiling datasets with text columns [GitHub, 199 stars]
- transformers-interpret - Model explainability that works seamlessly with transformers [GitHub, 424 stars]
- Awesome-explainable-AI - collection of research materials on explainable AI/ML [GitHub, 333 stars]
- Bias in Natural Language Processing @EMNLP 2020 [Blog, Nov 2020]
- Machine Learning as a Software Engineering Enterprise - NeurIPS 2020 Keynote [Presentation, Dec 2020]
- Computational Ethics for NLP - course resources from the Carnegie Mellon University [Lecture Notes, Spring 2020]
- Ethics in NLP - resources from ACLs Ethics in NLP track
- The Institute for Ethical AI & Machine Learning
- Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models [Paper, Feb 2021]
- Privacy Considerations in Large Language Models [Blog, Dec 2020]
- DeepWordBug - Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers [GitHub, 48 stars]
- Adversarial-Misspellings - Combating Adversarial Misspellings with Robust Word Recognition [GitHub, 46 stars]
- spaCy by Explosion AI [GitHub, 21027 stars]
- flair by Zalando [GitHub, 10638 stars]
- AllenNLP by AI2 [GitHub, 10371 stars]
- stanza (former Stanford NLP) [GitHub, 5596 stars]
- spaCy stanza [GitHub, 552 stars]
- nltk [GitHub, 10030 stars]
- gensim - framework for topic modeling [GitHub, 12305 stars]
- pororo - Platform of neural models for natural language processing [GitHub, 960 stars]
- NLP Architect - A Deep Learning NLP/NLU library by Intel® AI Lab [GitHub, 2705 stars]
- FARM [GitHub, 1270 stars]
- gobbli by RTI International [GitHub, 260 stars]
- headliner - training and deployment of seq2seq models [GitHub, 230 stars]
- SyferText - A privacy preserving NLP framework [GitHub, 178 stars]
- DeText - Text Understanding Framework for Ranking and Classification Tasks [GitHub, 1142 stars]
- TextHero - Text preprocessing, representation and visualization [GitHub, 2302 stars]
- textblob - TextBlob: Simplified Text Processing [GitHub, 7778 stars]
- AdaptNLP - A high level framework and library for NLP [GitHub, 342 stars]
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- Learn NLP the practical way [Blog, Nov. 2019]
- Learn NLP the Stanford way (+Part 2) [Blog, Nov 2020]
- Choosing the right course for a Practical NLP Engineer
- 12 Best Natural Language Processing Courses & Tutorials to Learn Online
- NLP Course | For You - Great and interactive course on NLP
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- Speller100 by Microsoft [Blog, Feb 2021]
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- Controllable Neural Text Generation [Blog, Jan 2021]
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