Explosion builds developer tools for AI, Machine Learning and Natural Language Processing.
Project
Topics
Tasks
Select...Code GenerationCoreference ResolutionDependency ParsingDistillationEmbeddings & VectorsEntity LinkingEvaluationImage ClassificationImage SegmentationLayout AnalysisLemmatizationNamed Entity RecognitionObject DetectionOptical Character Recognition (OCR)Part-of-Speech TaggingPII AnonymizationQuestion AnsweringRelation ExtractionRetrieval-Augmented Generation (RAG)Rule-Based MatchingSpan CategorizationText ClassificationText GenerationTokenizationWeak Supervision
Authors
Select...Adriane BoydÁkos KádárBasile DuraChung-Fan TsaiDamian RomeroDaniël de KokDuygu AltinokEdward SchmuhlHelena SteckmeisterIndia KerleInes MontaniKabir KhanLj MirandaMadeesh KannanMagdalena AniołMatthew HonnibalPaul O’Leary McCannPeter BaumgartnerPhilip VolletRaphael MitschRehan AhmedRichard HudsonRyan WesslenSofie Van LandeghemVictoria SlocumVincent D. WarmerdamVinit RavishankarWalter Henry
What if we could take learnings from AI-powered coding agents and apply them to solving real-world NLP problems? In this talk, I’ll show how we’ve built a powerful virtual NLP assistant to help developers create practical and modular solutions that are small, fast and fully data-private.
AI-powered coding assistants have transformed the way we build software, and AI itself. In this talk, Ines shows why we should use LLMs to build systems instead of as systems, and why code is more important than ever, not less.
Instead of using LLMs for entity extraction, we employ the traditional NLP tool spaCy to extract entities, and use their co-occurrence in a chunk as relations.
Keyword extraction in English is performed with a hybrid approach comprising a fine-tuned spaCy NER model, FinBERT-based KeyBERT embeddings, YAKE, and EmbedRank, which results in a combined accuracy of 91.2%.
German talk on the future of Artificial Intelligence and the impact of open-source software and models.
CT reports were annotated by MD raters using Prodigy software to develop a stepwise NLP “pipeline” that first excluded prior or known malignancy, determined the presence of a lung nodule, and then categorized any recommended follow-up. NLP was developed using a RoBERTa large language model on the spaCy platform.
Build structured NLP solutions with custom components and models powered by LLMs. By end of the book you will be empowered to build robust NLP pipelines and integrate them with web applications to build end-to-end solutions.
Short video interview with Ines about the 4 skills job hunters can cultivate for a career in artificial intelligence.
A modern approach and mindset for building future-proof NLP pipelines in-house, focusing on use cases from banking, finance and economics.