SciencePlots VS paperai

What is it about matplotlib that you object to? If it’s just the number of commands needed to get it right, you can look at something like https://github.com/garrettj403/SciencePlots that will get you most of the way.

I know a lot of academics that do, but wouldn't recommend it personally. Also, there is https://github.com/garrettj403/SciencePlots

I have also found SciencePlots. Should I use this in addition to cmcrameri?

This guy's done some good work in styling matplotlib plots for articles (font sizes, colors, line styles and whatnot): https://github.com/garrettj403/SciencePlots

paperai | paperetl

action: translate input: txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. output: txtai exécute des workflows d'apprentissage automatique pour transformer les données et construire des applications de recherche sémantique alimentées par l'IA. action: translate input: Traditional search systems use keywords to find data output: Les systèmes de recherche traditionnels utilisent des mots-clés pour trouver des données action: summary input: https://github.com/neuml/txtai output: txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. API bindings for JavaScript, Java, Rust and Go. Cloud-native architecture scales out with container orchestration systems (e. g. Kubernetes) action: summary input: https://github.com/neuml/paperai output: paperai is an AI-powered literature discovery and review engine for medical/scientific papers. Paperai was used to analyze the COVID-19 Open Research Dataset (CORD-19) paperai and NeuML have been recognized in the following articles: Cord-19 Kaggle Challenge Awards Machine-Learning Experts Delve Into 47,000 Papers on Coronavirus Family. real 0m22.478s user 0m13.776s sys 0m3.218s

paperai and paperetl are a set of projects to consider for this task.

The best source of information right now is https://github.com/neuml/paperai

['Introducing txtai, an AI-powered search engine built on Transformers Add Natural Language Understanding to any application Search is the base of many applications.', 'Once data starts to pile up, users want to be able to find it.', 'It’s the foundation of the internet and an ever-growing challenge that is never solved or done.', 'The field of Natural Language Processing (NLP) is rapidly evolving with a number of new developments.', 'Large-scale general language models are an exciting new capability allowing us to add amazing functionality quickly with limited compute and people.', 'Innovation continues with new models and advancements coming in at what seems a weekly basis.', 'This article introduces txtai, an AI-powered search engine that enables Natural Language Understanding (NLU) based search in any application.', 'Introducing txtai txtai builds an AI-powered index over sections of text.', 'txtai supports building text indices to perform similarity searches and create extractive question-answering based systems.', 'txtai also has functionality for zero-shot classification.', 'txtai is open source and available on GitHub.', 'txtai and/or the concepts behind it has already been used to power the Natural Language Processing (NLP) applications listed below: • paperai — AI-powered literature discovery and review engine for medical/scientific papers • tldrstory — AI-powered understanding of headlines and story text • neuspo — Fact-driven, real-time sports event and news site • codequestion — Ask coding questions directly from the terminal Build an Embeddings index For small lists of texts, the method above works.', 'But for larger repositories of documents, it doesn’t make sense to tokenize and convert all embeddings for each query.', 'txtai supports building pre- computed indices which significantly improves performance.', 'Building on the previous example, the following example runs an index method to build and store the text embeddings.', 'In this case, only the query is converted to an embeddings vector each search.', 'https://github.com/neuml/codequestion https://neuspo.com/ https://github.com/neuml/tldrstory https://github.com/neuml/paperai Introducing txtai, an AI-powered search engine built on Transformers Add Natural Language Understanding to any application Introducing txtai Build an Embeddings index']

This example covers functionality found in the paperai library. See that library for a full solution that can be used with the dataset discussed below.

paperai is an example application that uses this module to find answers to questions in medical/scientific papers.

paperai - AI-powered literature discovery and review engine for medical/scientific papers

paperetl can be used standalone or with paperai

You are correct, building a database of scientific papers is the starting point. From there you can run queries against the data to ask questions. paperai is an example of this.