Access to high quality annotated data remains one of the biggest limiting factors preventing companies from progressing an AI use case into a working implementation. This is despite great advances in few shot learning and pre-trained models which lower the barrier to entry and reduce the amount of annotated data…

Organizations with a large collection (corpus) of documents often need to find similar documents. For a grant-giving organization (for example a charitable foundation like the Wellcome Trust), the use case could be finding experts to review grant applications by matching the expert’s publication history to the grant application. For a…

Large grant giving organizations like the Wellcome Trust or Cancer Research UK receive thousands of applications for funding grants each year. Analysis of this data can be complicated by the fact that adding useful metadata to funding grant applications is often time consuming to do manually. By metadata, we mean…

Customize train 🐳 — In an earlier post we went through how to run a training script using sklearn, PyTorch or transformers with SageMaker by leveraging their preconfigured framework containers. The training scripts we used were self contained, meaning they only used the respective framework and python standard library. …

How to effortlessly train sklearn 📊, pytorch🔥, and transformers 🤗 models in the cloud — SageMaker is a Machine Learning Operations (MLOps) platform, offered by AWS, that provides a number of tools for developing machine learning models from no code solutions to completely custom. With SageMaker, you can label data, train your own models in the cloud using hyperparameter optimization, and then deploy those models…

How we made our multilabel classification threshold optimizer converge in minutes instead of days — Multilabel classification is a common task in machine learning and Natural Language Processing (NLP). We approach it by training a model that can apply one or more labels to each new example that it sees. …

In a previous post we spoke about a neural architecture we developed for classifying our grants with ~5K disease tags from the MeSH (Medical subject Headings) hierarchy. In this post we will touch on the techniques needed to scale to a model to classify all ~29K MeSH tags. Our dataset…

In this blog post we introduce Mantis NLP, a remote first company we have founded to help companies put impactful data science into production. …

Ever since we started working on data science projects at Wellcome data labs we have been thinking a lot about reproducibility. As a team of data scientists, we wanted to ensure that the results of our work can be recreated by any member of the team. Among other things this…