grobid
paperai
grobid | paperai | |
---|---|---|
12 | 19 | |
3,075 | 1,198 | |
- | 1.4% | |
9.2 | 5.9 | |
8 days ago | 5 months ago | |
Java | Python | |
Apache License 2.0 | Apache License 2.0 |
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grobid
- FLaNK-AIM Weekly 06 May 2024
- Show HN: Open-source Rule-based PDF parser for RAG
- How to ingest image based PDFs into private GPT model?
- 🥪 Best Sites For ebooks, articles, research papers etc..🥪
- Grobid – ML software for extracting information from scholarly documents
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How to create a web app that turns academic papers into text documents
Interesting concept. Grobid tries to do the same https://github.com/kermitt2/grobid
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Extract research paper`s references
I would suggest using grobid - a pipeline for extracting scientific PDFs into a common XML format which can be easily parsed. Grobid has quite a nice mature REST API that I've used in some of my own projects. It parses references and matches them to their DOI using the CrossRef API with a reported 95% F1 score. This should make your job pretty simple as far as I can tell - all you'd need to do is run your papers through grobid and then build a citation graph by comparing document DOIs.
- Free/open-source alternatives to Connected Papers...?
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Seeking Advice: How to extract Abstract from scientific journals (.pdfs) 10k+.
Just use science-parse or GROBID. They have been designed for that exact reason.
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Project to rebuild papers with plaintext markup languages
- I ended up using Grobid, which converts the PDF to a very detailed XML format. The format is not a word processing format though, but a format specifically for representing scientific documents. I don't know, if it would, for example, contain tags about bold or italicized text. The tool is working really well, but since you probably cannot use the output XML format directly, it will need some postprocessing, which would be relatively simple with XML parsing libraries.
paperai
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Oracle of Zotero: LLM QA of Your Research Library
Nice project!
I've spent quite a lot of time in the medical/scientific literature space. With regards to LLMs, specifically RAG, how the data is chunked is quite important. With that, I have a couple projects that might be beneficial additions.
paperetl (https://github.com/neuml/paperetl) - supports parsing arXiv, PubMed and integrates with GROBID to handle parsing metadata and text from arbitrary papers.
paperai (https://github.com/neuml/paperai) - builds embeddings databases of medical/scientific papers. Supports LLM prompting, semantic workflows and vector search. Built with txtai (https://github.com/neuml/txtai).
While arbitrary chunking/splitting can work, I've found that integrating parsing that has knowledge of medical/scientific paper structure increases the overall accuracy and experience of downstream applications.
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Build Personal ChatGPT Using Your Data
https://github.com/neuml/paperai
Disclaimer: I am the author of both
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[P] Parse research papers into structured data
paperai | paperetl
- Show HN: Semantic search and workflows for medical/scientific papers
- Semantic search and workflows for medical/scientific papers
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# Run txtai in native code
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
What are some alternatives?
Parsr - Transforms PDF, Documents and Images into Enriched Structured Data
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
CERMINE - Content ExtRactor and MINEr
tika-python - Tika-Python is a Python binding to the Apache Tikaâ„¢ REST services allowing Tika to be called natively in the Python community.
Smile - Statistical Machine Intelligence & Learning Engine
SciencePlots - Matplotlib styles for scientific plotting
science-parse - Science Parse parses scientific papers (in PDF form) and returns them in structured form.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
datahub - The Metadata Platform for your Data Stack
faiss - A library for efficient similarity search and clustering of dense vectors.
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
scibert - A BERT model for scientific text.