Local-GenAI-Search is a generative search engine based on Llama 3, langchain and qdrant that answers questions based on your local files
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Updated
Aug 19, 2024 - Python
Local-GenAI-Search is a generative search engine based on Llama 3, langchain and qdrant that answers questions based on your local files
CauseNet: Towards a Causality Graph Extracted from the Web
Machine Comprehension Train on MSMARCO with S-NET Extraction Modification
Analysis on the MS-MARCO leaderboard regarding the machine reading comprehension task.
RAG-based question answering system on MS MARCO with retrieval, reranking, evaluation, and reproducibility checks.
This repo shows how foundational model hallucinates and how we can fix such hallucinations using fine-tuning them
Fixed some bugs and edited original baseline scripts from MS MARCO
A Fine-Grained Analysis of Distribution Shifts in MSMARCO (MS-Shift). Evaluation benchmark on three types of distribution shifts, all conditioned on MSMARCO queries.
Python code to train SPLADE sparse retrieval models based on BERT-Tiny (4M) and BERT-Mini (11M) by distilling a Cross-Encoder on the MSMARCO dataset
阅读/论文笔记
MONSERRATE is a dataset specifically created to evaluate Question Generation systems. It has, on average, 26 questions associated to each source sentence, attempting to be an “exhaustive” reference.
Final project for course on deep learning for nlp (IA376E/1s2020 @ Unicamp)
Information Retrieval Using TFIDF and Conv-KNRM
A mvp product multi-stage search engine over MS MARCO (8.8M passages). BM25 + dense retrieval with RRF fusion, cross-encoder reranking, and FastAPI serving. Built with dependency injection, full test coverage (139 tests), and benchmark-validated evaluation (MRR@10: 0.3660).
Voice-enabled multilingual RAG system over MSMARCO-XI. Self-hosted BM25+HNSW hybrid retrieval, under 200ms. Built for HackerHouse Goa 2026 Task 2.
Doc-Phi: An Intelligent Document Finder
Document ranking with BM25, DirichletLM, RM3, GloVe and LLM query expansion on MSMARCO
Learning-to-Rank on MS MARCO Passages: candidate generation from prebuilt indexes and re-ranking for QA search
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