Autofit2 – End-to-end pipeline for multilingual text classification
Hi HN, Stefan here. autofit2 is a project I have been using at my previous company and is now opensourced. It has been used extensively in automated text moderation, but can be applied to any text/document classification task. We had success modeling offensive texts in 20+ languages (cf. github.com/neospe/dataload for all the datasets). It's an integrated pipeline for lightweight multilingual text classification, covering preprocessing, training, and evaluation. It implements SetFit, a few-shot learning technique that works well for low-data regimes (down to a few dozen…
Autofit2 – End-to-end pipeline for multilingual text classification launched on June 25, 2026 with 28 votes, #755 of 22,647 launches that month and more than 95% of that year's launches.
In plain words
Autofit2 is an open-source pipeline for multilingual text classification that handles preprocessing, training, and evaluation end-to-end. It uses SetFit, a few-shot learning technique that works effectively with minimal training data, making it suitable for text moderation and document classification across 20+ languages. The tool runs efficiently on CPUs using Sentence Transformers, requires minimal dependencies beyond PyTorch, and outputs TorchServe model archives ready for deployment.
written from the facts on this page · September 2026
From the sources
Automated end-to-end data preprocessing, model training, and evaluation pipeline - neospe/autofit2
Few-shot text classification. Massively multilingual (50+ languages), fully automated pipeline built on setfit and SBERT embeddings. 1. Prepare Data Use dataload or implement a custom loader providing labeled examples. 2. Configure Create myproject.json specifying dataset paths, model settings, and output directories. Supports multi-language/task blocks. myproject.json defines the training parameters. Its structure depends on the target type: Base Models ( all ) or Custom Models ( custom ). { "<task-key>" : { "<language-key>" : { "base" : { "model file" : " <path> " , // Relative path, no trailing slash (e.g. "models-in/all-MiniLM-L6-v2") "model type" : " <string> " , // e.g., "bert"…from github.com
In the maker’s words, at launch
Hi HN, Stefan here. autofit2 is a project I have been using at my previous company and is now opensourced. It has been used extensively in automated text moderation, but can be applied to any text/document classification task. We had success modeling offensive texts in 20+ languages (cf.…
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