SetFit GitHub Issue Classifier (bug / feature / question / support)
Contrastive fine-tune of sentence-transformers/all-MiniLM-L6-v2 (22M params) with a logistic-regression head, trained with SetFit 1.2.0 following the NLBSE issue-report-classification recipe (CosineSimilarityLoss, 20 pair-iterations, batch 16, 1 epoch, 256-token max length). The smallest model in the NLBSE winner family — 3x smaller than DistilBERT and ~5x smaller than the MPNet used by the competition baseline, while scoring better here.
Training data: pngwn/github-issues-4class — 1,997 balanced issues (bug/feature/question from NLBSE'24 + support class sourced from maintainer-assigned GitHub labels).
Test-set results (1,997 balanced examples)
| metric | value |
|---|---|
| macro-F1 (4-class) | 0.807 |
| accuracy | 0.806 |
| macro-F1 on bug/feature/question subset | 0.780 |
| F1 bug | 0.800 |
| F1 feature | 0.789 |
| F1 question | 0.717 |
| F1 support | 0.922 |
Beat a DistilBERT-base fine-tune (66M params, 0.794 macro-F1) on the same data. For reference, published NLBSE'24 numbers on the 3-class benchmark: SetFit baseline 0.827, competition winners 0.84–0.89 with much larger models (MPNet, RoBERTa+adapters).
Usage
from setfit import SetFitModel
model = SetFitModel.from_pretrained("pngwn/github-issue-classifier-setfit-minilm")
preds = model(["App crashes when opening settings", "How do I configure the API key?"])
Labels (id → name): 0 bug, 1 feature, 2 question, 3 support.
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Base model
nreimers/MiniLM-L6-H384-uncased