setfit

Low latency, High Accuracy, Custom Query routers for Humans and Agents. Built by Prithivi Da

Hierarchical RAG architecture scaling to 693K chunks on consumer hardware (4GB VRAM). Features 3-address routing, hybrid vector+graph fusion, and SetFit classification.

Playing with SetFit approach for few-shot transfer for text classification

Slides, Notebook and Data for Presentation: DataHour: Harnessing ML and NLP for Elevated Customer Experiences

Issue report classification demo with SetFit and Ollama for NASA's Flight System software repositories

Repository for the paper "Literary Metaphor Detection with LLM Fine-Tuning and Few-Shot Learning".

Hacking SetFit so that it works with integrated gradients.

This repository contains code developed for the SVAR project (Trafikverket 2023-2025).

SciBERT-based scientific abstract classification using SetFit framework with context injection data augmentation

A multi-head classifier using SetFit for query preprocessing

Weighted Attention Message Pruner (WAMP) Proxy - prunes redundant messages from LLM history to save up to 50% in token costs while preserving core context

A short path to a world model in your LLM — few-shot fine-tune it to predict in latent space (emit a set of next states, a JEPA world model). Also makes bespoke embedding models. lang·set·fit.

Independent SetFit reproduction and paper-code audit: ten-seed CR results, deterministic few-shot pipeline, trained MPNet release, terminal model lab, tests, and self-contained research site.

Crawler for mapping networks of accounts posting violent content on Instagram Reels

A sentiment analysis API server

Veille et POC sur le Few-Shot Learning : SetFit atteint 93 % de la performance d'une baseline avec seulement 13 % des données annotées.

This project implements and extends SetFit for few-shot text classification, along with advanced improvements such as hard negative mining and open-set recognition.

End-to-end NLP analysis of 62k+ German restaurant profiles using SetFit, multilingual Sentence Transformers, web scraping and market analytics.

Toolkit for the paper "Explainable Subjective Stance Classification with SetFit in Political Discourse". The project leverages the SetFit few-shot learning framework, Sentence Transformers architecture, and traditional linguistic ML to enhance explainability in stance classification.