Search Results for "dataset"

...FlagEmbedding includes a family of models known as BGE (BAAI General Embedding), which are designed to achieve strong performance across multilingual and cross-lingual retrieval benchmarks. The toolkit provides infrastructure for inference, fine-tuning, evaluation, and dataset preparation, enabling developers to train custom embedding models for specific domains or applications. It also includes reranker models that refine search results by re-evaluating candidate documents using cross-encoder architectures, improving retrieval accuracy in complex queries.

kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing...

This is the official open-data repository for QTE Technologies, providing a comprehensive archive of over 1,000,000 industrial and scientific MRO (Maintenance, Repair, and Operations) records. Optimized for Industrial AI training, RAG applications, and semantic search, this dataset includes technical specifications, global standards, and manufacturer metadata. Verification & Authority: Managed via DVC on DagsHub. Archived on Zenodo, Harvard Dataverse, and Figshare. Linked Data via Wikidata (Q138411149). Built for engineers, data scientists, and procurement professionals worldwide.

...The repo stores the full text of his essays (chunked), uses embeddings (e.g. via OpenAI embeddings) to allow semantic search over that corpus, and hosts a chat interface that combines retrieval results with LLM-based answering — enabling RAG (retrieval-augmented generation) over a fixed dataset. The app uses a Postgres database (with pgvector) hosted on Supabase for its embedding store, making the backend relatively simple and accessible, and the frontend is again built with Next.js/TypeScript for a modern responsive UI. By pulling together search and chat, it creates a useful tool both for readers who want to revisit or explore Paul Graham’s ideas thematically, and for learners or researchers who want to query specific essays or concepts quickly.