AI and ML

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ML Introduction

Welcome to my Machine Learning and AI notebook. 🧱 AI and ML Fundamentals AI Best Practices - ...

ML Best Practices

Machine learning is a complex and multifaceted activity that requires the combination of a number...

Model Quantization

Deploying models that are performant (obviously statistically but in this context I primarily mea...

SpaCy GPU

Set Up Environment It's relatively easy to use SpaCy with a GPU these days. First set up your con...

SpaCy CoRef

Spacy Coref is an experimental coreference resolution model in spacy The project repository is he...

Core Scientific Concepts (CoreSC)

Core Scientific Concepts (CoreSC) is an annotation scheme used to delineate different parts of sc...

Stratified Sampling in Pandas

Use groupby on the label column to create sub-frames for each label and then use the sample() fu...

From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism

Paper Link Authors: Sachita Nishal Nicholas Diakopoulos Notes Their work comes at the pr...

Stable Diffusion

Web UI API Run the web UI with: ./webui.sh --api Example Python Script import webuiapi ...

AI Causing Chaos

So much AI turns out to be low-waged people in a call center in the Global South pretending to ...

Tasks

Tasks within ML and NLP

Question Answering

Approaches Fine-Tuning Sentence-BERT for Question Answering CapitalOne produced a tutorial (mirr...

Coreference Resolution

Co-reference Resolution (CR) is the task of deciding whether two entity mentions refer to the sam...

Keyword Extraction

Graph-Based Keyword Extraction Graph-based approaches like TextRank allows the extraction of keyw...

Relationship Extraction

Relationship Extraction (RE) is a task that is related to Coreference Resolution but with a focus...

Federated Learning

Flower is a federated learning framework with compatibility with Torch, Tensorflow and others

Large Scale Multi-Label Learning

The Keras website has a tutorial on how to do multi-label learning with a large number of labels:...

Machine Learning with Limited Data

Pattern Exploitative Training

PET or Pattern Exploitative Training @article{schick2020exploiting, title={Exploiting Cloze Qu...

Learning with Limited Data

Good machine learning is heavily dependent on good data. A few more good data-points is likely to...

DeBERTa Zero Shot

The DeBERTa zero-shot model is an NLP zero-shot classifier trained by Moritz Laurer and made publ...

Explainability and Model Analysis

Explainability

Explainability is a big challenge in machine learning. I wrote a blog post about the ELI5 library...

Model Confidence Scores

Many ML classification models can provide a confidence score which tells the user how confident t...

Argilla

Argilla is a data annotation tool with an API that allows you to use external models to automatic...