Semanlink - Dev AND Data science

> Toolformer, **a model
trained to decide which APIs to call, when to
call them, what arguments to pass, and how to
best incorporate the results into future token
prediction**.

> fulfills the
following desiderata:
> - The use of tools should be learned in a
self-supervised way without requiring large
amounts of human annotations
>- The LM should be able to decide for itself when
and how to use which tool.

> Approach based
on the recent idea of using large LMs with incontext
learning (Brown et al., 2020) to generate
entire datasets from scratch.
>
> Given just a handful of human-written examples
of how an API can be used, 
> - we let a LM annotate
a huge language modeling dataset with potential
API calls. 
> - We then use a self-supervised loss to
determine which of these API calls actually help
the model in predicting future tokens. 
>- Finally, we
finetune the LM itself on the API calls that it considers
useful.

[Jay Hack @mathemagic1an sur twitter](https://twitter.com/mathemagic1an/status/1624870248221663232):

> from a small seed set of human inputs (essentially demonstrating usage of APIs), the training set for this behavior is generated by the LLM itself.
> 
> So what does this mean? We've found a promising way to tightly integrate arbitrary APIs with our best-performing models.

2023-02-13 About

Jay Hack sur Twitter : "My thoughts on Toolformer IMO the most important paper in the past few weeks..."

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Timo Schick sur Twitter : "Introducing the Toolformer, a language model that teaches itself to use various tools in a self-supervised way..."

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explosion/prodigy-openai-recipes: ✨ Bootstrap annotation with zero- & few-shot learning via OpenAI GPT-3

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Towards a Tagalog NLP pipeline

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deepset sur Twitter : " Generative models have taken the world of NLP by storm. But LLMs do not know about your personal data. This makes personal assistants, enterprise knowledge management and many other applications challenging. Retrieval augmented pipelines are the answer"

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gradio-app/gradio: Create UIs for your machine learning model in Python in 3 minutes

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GitHub Copilot · Your AI pair programmer

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Transformers-Tutorials/LayoutLMv3 at master · NielsRogge/Transformers-Tutorials

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[Tutorial] How to Train LayoutLM on a Custom Dataset with Hugging Face

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Where does hugging face's transformers save models? - Stack Overflow

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Dan Shipper 📧 sur Twitter : "I spent Christmas programming with ChatGPT. Some observations:"

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IBM/zshot: Zero and Few shot named entity & relationships recognition

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LayoutLM Explained

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Matthew Honnibal sur Twitter : "We've been working on new prodi.gy workflows that let you use the @OpenAI API to kickstart your annotations, via zero- or few-shot learning. ..."

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Horace He @ neurips sur Twitter : "Eager mode was what made PyTorch successful. So why did we feel the need to depart from eager mode in PyTorch 2.0?..."

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4. Using the Snorkel-Labeled Dataset for Text Classification - Practical Weak Supervision [Book]

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Alex sur Twitter : "Semantic search: how you can leverage both @CohereAI and @pinecone libraries to quickly build a POC..."

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How to Train an mT5 Model for Translation With Simple Transformers | by Thilina Rajapakse | Towards Data Science

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Google AI Blog: TensorStore for High-Performance, Scalable Array Storage

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merve sur Twitter : "@huggingface transformers includes a new pipeline called Document Question Answering. This is a pipeline you can use to extract information from PDFs!...

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Modeling DNA Sequences with PyTorch | by Erin Wilson | Sep, 2022 | Towards Data Science

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Prompt Tuning BERT🎯:CommonLit Readability | Kaggle

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Active Learning with AutoNLP and Prodigy

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A framework for designing document processing solutions

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Philip Vollet sur Twitter : "Extracting information from PDFs or scanned documents is still a challenge! Use the @huggingface LayoutLMv3 model and Prodigy..."

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Karl Higley sur Twitter : "Many ANN search tools (e.g. FAISS, ScaNN) allow you to provide multiple points as part of the same query..."

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Connor Shorten sur Twitter : "Wow, incredible to see this from NASA! 🚀 Weaviate's integration of Vector Search with Graph Data and Symbolic Relations is a very interesting combination of technologies!..."

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PyKEEN, Python package for reproducible, facile knowledge graph embeddings.

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Understanding Semantic Search and Question Answering | deepset

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Max Irwin sur Twitter : "Instant Neural Search for your website! ..."

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DataBorg - Knowledge management simplified

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Sentence Embedding Fine-tuning for the French Language | by La Javaness R&D | Feb, 2022 | Medium

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GuideToTransformersDomainAdaptation.ipynb - Colaboratory

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Retraining roberta-base using the RoBERTa MLM Procedure | Medium

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SPIKE for Knowledge Base Construction

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Yosi Shamay sur Twitter : "a new platform for rapid ad-hoc knowledgebase construction using extractive search...."

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Nils Reimers sur Twitter : "Creating intent classes for chatbots is challenging This tutorial shows how to use sentence-transformers to find potentially overlapping intent classes and how to improve your data annotation work." / Twitter

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How to Build a Semantic Search Engine With Transformers and Faiss | by Kostas Stathoulopoulos | Towards Data Science

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Modern Question Answering Systems Explained

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Haystack

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Haystack Annotation Tool

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Haystack Annotation Tool

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Integrate ORQA and REALM for Open Domain Question Answering · Issue #312 · deepset-ai/haystack

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How to build a chatbot that reads all your data to find the right answer - Xatkit

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Explosion 💥 sur Twitter : "...annotation tool and AutoNLP to train state-of-the-art NLP models!"

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semi-technologies/weaviate: Weaviate is a cloud-native, modular, real-time vector search engine

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Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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How to Fine-Tune Sentence-BERT for Question Answering | Capital One

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James Briggs sur Twitter : *free* course on vector similarity search and Faiss..."

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Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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neuml/txtai: Build AI-powered semantic search applications

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Haystack (deepset)

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Build NLP features into your product | deepset

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How I almost won an NLP competition without knowing any Machine Learning - DEV Community

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jeshraghian/snntorch: Deep learning with spiking neural networks in Python

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Nils Reimers sur Twitter : "How to train state-of-the-art sentence embeddings?"

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Transformer models - Hugging Face Course

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scikit-learn Pipelines meet Knowledge Graphs - The Python kgextension Package ESWC 2021

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Semantic Search with S-BERT is all you need

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Improving Quality of Search Results Clustering with Approximate Matrix Factorisations (2006)

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Lingo

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Carrot2 search results clustering engine (online)

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Carrot2

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Transformers Pipelines.ipynb - Colaboratory

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fastai v2 cheat sheets

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Teddy Koker sur Twitter : "Torchsort, an implementation of "Fast Differentiable Sorting and Ranking" in PyTorch"

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Rodrigo Nogueira sur Twitter : "Slides of our WSDM 2021 tutorial "Pretrained Transformers for Text Ranking: BERT and Beyond"

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Why we switched from Spacy to Flair to anonymize French case law | by Michaël Benesty | Towards Data Science

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arogozhnikov/einops: Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

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Weaviate

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Salmon Run: Word Sense Disambiguation using BERT as a Language Model

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2020-12-01 About

[2004.10964] Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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[2011.06993] FLERT: Document-Level Features for Named Entity Recognition

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raphaelsty/ckb: Contextual knowledge bases

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[2010.05234] A Practical Guide to Graph Neural Networks

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Chris Mungall sur Twitter : "Reading: OWL2Vec*: Embedding of OWL Ontologies"

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Which flavor of BERT should you use for your QA task? | by Olesya Bondarenko | Towards Data Science

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scikit-multilearn: Multi-Label Classification in Python

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Mulan: A Java library for multi-label learning

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Hugging Face sur Twitter : "No labeled data? No problem. The 🤗 Transformers master branch now includes a built-in pipeline for zero-shot text classification...

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scikit-multilearn/scikit-multilearn: A scikit-learn based module for multi-label et. al. classification

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awslabs/dgl-ke: package for learning large-scale knowledge graph embeddings.

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BERT Word Embeddings Tutorial · Chris McCormick

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dicksontsai/stanford-nlp-local-extension: Chrome extension for sending content to localhost server running Stanford NLP tools.

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facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.

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Knowledge Distillation - Neural Network Distiller

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AmbiverseNLU: A Natural Language Understanding suite by Max Planck Institute for Informatics

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My First NN Part 3. Multi-Layer Networks and Backpropagation | Scott H. Hawley (alt. blog via fastpages)

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FastHugs | ntentional

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Hugging Face: How to train a new language model from scratch using Transformers and Tokenizers

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Distilling BERT models with spaCy - Towards Data Science (2019)

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Hugging Face sur Twitter : DistilBERT-cased for Question Answering w/ just 3 lines of javascript

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Siamese Network for Image and Text similarity using Keras

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2020-01-22 About

Your own blog with GitHub Pages and fast_template (4 part tutorial) · fast.ai

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GitHub - OpenNMT/OpenNMT-py: Open Source Neural Machine Translation in PyTorch

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fastai/nbdev: Create delightful python projects using Jupyter Notebooks

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Building a Search Engine with BERT and TensorFlow - Towards Data Science

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Named Entity Recognition with Bert – Depends on the definition

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Joint Intent Classification and Slot Filling with Transformers (Jupyter Notebook Viewer)

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How to build deep neural network for custom NER with Keras

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Custom Named Entity Recognition Using spaCy - Towards Data Science

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2019-12-31 About

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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Graph Neural Networks for Natural Language Processing tutorial at EMNLP 2019

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Getting robots to listen: Using Watson's Speech to Text service - Watson

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Document analysis with machine learning

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2019-10-28 About

One Shot learning, Siamese networks and Triplet Loss with Keras

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2019-10-13 About

Machine Learning for Unbalanced Datasets using Neural Networks

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2019-09-29 About

The dangers of reshaping and other fun mistakes I’ve learnt from PyTorch

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Sebastian Ruder sur Twitter : "It's great to see the growing landscape of NLP transfer learning libraries"

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2019-09-13 About

Introducing Neural Structured Learning in TensorFlow

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An easy introduction to Pytorch for Neural Networks

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2019-08-09 About

huggingface/pytorch-transformers: A library of state-of-the-art pretrained models for NLP

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Pipelines and composite estimators / ColumnTransformer for heterogeneous data — scikit-learn documentation

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One Shot Learning with Siamese Networks using Keras

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2019-06-28 About

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 About

[1812.05944] A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments

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Towards Reproducible Research with PyTorch Hub | PyTorch

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2019-06-11 About

Transfer Learning in Natural Language Processing - Google Slides

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Python speech to text with PocketSphinx – sophie's blog

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2019-05-27 About

A Technique for Building NLP Classifiers Efficiently with Transfer Learning and Weak Supervision

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2019-05-22 About

Sebastian Ruder sur Twitter : "Besides the obvious things (ELMo, BERT, etc.), is there anything that we should definitely discuss at the NAACL "Transfer Learning in NLP" tutorial?"

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2019-05-20 About

PyTorch-BigGraph: Faster embeddings of large graphs - Facebook Code

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Radek Osmulski sur Twitter : "You would expect a difference in row access times depending on the type of a sparse matrix, but I didn't realize the difference would be so big!

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2019-05-05 About

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 About

android - speech recognition reduce possible search results - Stack Overflow

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Introduction to PyTorch Code Examples

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The Ultimate Guide To Speech Recognition With Python – Real Python

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Sent2Vec: An unsupervised approach towards learning sentence embeddings | RARE Technologies

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2019-03-25 About

huggingface/pytorch-pretrained-BERT: The Big-&-Extending-Repository-of-Transformers: Pretrained PyTorch models for Google's BERT, OpenAI GPT & GPT-2, Google/CMU Transformer-XL.

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2019-03-15 About

François Chollet sur Twitter : a crash course on everything you need to know to use TensorFlow 2.0 + Keras

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fast.ai Embracing Swift for Deep Learning · fast.ai

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Measuring patent claim breadth using Google Patents Public Datasets | Google Cloud Blog

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Plotting Similar Patents | Kaggle

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Using BERT for state-of-the-art pre-training for natural language processing

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Keywords2vec

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nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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BPEmb: Subword Embeddings

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2019-01-31 About

Cheatsheet · fastText

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Troubleshooting Deep Neural Networks

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Training Cutting-Edge Neural Networks with Tensor2Tensor and 10 lines of code

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Tensor2Tensor

2019-01-21 About

What is torch.nn really? — PyTorch Tutorials 1.0.0

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Combining numerical and text features in (deep) neural networks - Digital Thinking

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2018-12-12 About

How to Use the Keras Functional API for Deep Learning

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Keras Functional API

2018-12-12 About

DeepLearning Images Revision M13/14. PyTorch 1.0. Git Integration. Smaller Boot Time.

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Prodigy · An annotation tool for AI, Machine Learning & NLP

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Neural Information Processing Systems - Tutorial Sessions: Unsupervised Deep Learning "predict everything"

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Advances in few-shot learning: reproducing results in PyTorch

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Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python - Analytics Vidhya

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Matthew Honnibal sur Twitter : "Have been experimenting with an unsupervised pre-training technique for @spacy_io, similar to ULMFit/Elmo/BERT etc.

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iliaschalkidis/ELMo-keras: Re-implementation of ELMo on Keras

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ML Resources

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2018-11-08 About

pandas cheat sheet

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Joint Models in NLP - Slides - Tutorial (EMNLP 2018) - Yue Zhang

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**Joint models: solve 2 tasks at once.**

Related tasks: POS tagging, NER, chuncking. Pipeline tasks

Motivations:

- reduce error propagation
- information exchange between tasks

Challenges:

- Joint learning
- Search

History: statistical models. 2 kinds:

- Graph-Based Methods
    - Traditional solution:
        - Score each candidate, select the highest-scored output
        - Search-space typically exponential
- Transition-Based Methods
    - Transition-Based systems: Automata
        - State: partial result during decoding, Action: operations that can be applied for state transition
        - Output constructed incrementally

- Deep learning based model
    - Neural transition based models
    - Neural graph-based models
        - Cross task
            - Seminal work: Collobert, Ronan, et al. "Natural language processing (almost) from scratch."
            - Not all tasks are mutually beneficial
            - Ramachandran, et al.  “Unsupervised pretraining for sequence to sequence learning.”
            - Peters, Matthew E., et al. "Deep contextualized word representations." (ELMo)
            - "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding."
            - ULMFIT
            - Correlation between multi-task learning and pretraining
        - Cross lingual
        - Cross domain
        - Cross standard

2018-11-06 About

Cross-Lingual Word Representations: Induction and Evaluation (Tutorial EMNLP 2017)

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Deep Latent-Variable Models for Natural Language - Tutorial - harvardnlp

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How to boost a Keras based neural network using AdaBoost? - Stack Overflow

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TensorFlow: how to load and save models at every epoch so you never lose time or data.

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Practical Text Classification With Python and Keras – Real Python

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python - Pythonic way of detecting outliers in one dimensional observation data - Stack Overflow

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The Annotated Transformer

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TensorFlow.js

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Unsupervised Text Summarization using Sentence Embeddings

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Neural Network Embeddings Explained – Towards Data Science

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pandas Multi-index and groupbys (article) - DataCamp

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pandas

2018-10-08 About

Deploy Your First Deep Learning Model On Kubernetes With Python, Keras, Flask, and Docker

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Training on TPU

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Machine Learning - Cheatsheet (Teaching - CS 229)

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2018-09-20 About

Key topics extraction and contextual sentiment of users’ reviews

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datas-frame – Tabular Data in Scikit-Learn and Dask-ML

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Named Entity Recognition and Classification with Scikit-Learn

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Strategies to scale computationally: bigger data — scikit-learn documentation

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Depends on the definition - it's about machine learning, data science and more

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2018-09-09 About

For what tasks is Pytorch preferable to Tensorflow? - Quora

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2018-08-28 About

zalandoresearch/flair: A very simple framework for state-of-the-art NLP

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2018-08-24 About

Practical guide to text classification | Google Developers

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2018-07-23 About

SIGIR 2018 Tutorial - Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between

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2018-07-09 About

What is Candidate Sampling

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2018-07-07 About

Python Data Science Handbook (Jake VanderPlas)

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2018-06-29 About

Aurélien Geron sur Twitter : "In @TensorFlow 1.9, it is much easier to use Keras with the Data API..."

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Keras

2018-06-10 About

How to do Unsupervised Clustering with Keras – Chengwei Zhang – Medium

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How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning

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Cloud TPU – Accélérateurs de ML pour TensorFlow | Google Cloud

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2018-05-31 About

Module google/universal-sentence-encoder | TensorFlow

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2018-05-23 About

Testing Tensorflow code

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2018-05-21 About

How to Train your Own Model with NLTK and Stanford NER Tagger? (for English, French, German…)

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2018-05-20 About

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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Implementing Poincaré Embeddings | RARE Technologies

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NLP using Word Vectors with Spacy - CLDSPN | Kaggle

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How to use Dataset in TensorFlow – Towards Data Science

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2018-04-21 About

Text Classification with TensorFlow Estimators

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2018-04-17 About

Part-of-Speech tagging tutorial with the Keras Deep Learning library - Cdiscount TechBlog

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2018-04-13 About

Sense2vec with spaCy and Gensim · Blog · Explosion AI

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2018-04-08 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About

Multiclass and multilabel algorithms — scikit-learn documentation

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2018-03-17 About

Présentation rapide de Pandas

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