SETFIT-NOTES.md

SetFit script: findings and behaviour details

Notes behind train-setfit.py. The README has what you need to run it; this file records the longer findings and the full behaviour details.

Compare more than the majority baseline

The emotion run reached 0.370 accuracy against a 0.352 majority baseline. Other single-seed body-model runs reached 0.418 (bge-small) and 0.410 (paraphrase-mpnet-base-v2). These results call for further evaluation; they do not establish a limit on the task or method.

SetFit's zero-shot guide reports 0.591 on emotion using BGE and training examples templated from the class names. It uses a different evaluation setup from the table above, so this is motivation for a matched comparison rather than a controlled comparison with this recipe. Templated training needs no labeled documents, but still uses compute.

For your task, compare with a simple baseline such as TF-IDF plus logistic regression using the same training and evaluation rows. A zero-shot comparison can also be useful when class names describe the task well. Use repeated seeds and appropriate task metrics before drawing conclusions from small accuracy differences. This recipe trains and evaluates a supervised classifier; built-in templated zero-shot training is a separate possible extension.

Real-world data: a worked failure

biglam/hansard_speech (2.7M parliamentary speeches, predicting party from speech) is the case where none of the convenient properties hold, and it is instructive precisely because it produces no score:

• No held-out split, so the eval set has to be carved from train — the numbers stop being comparable to anything published.
• ~9.5% of rows have a blank party, which without the drop trains an "" class.
• 28 parties after cleaning, nine of which cannot supply 8 examples (Respect 4, Independent SDP 2, Change UK 1). The requested eight-example budget cannot be met for those classes.
• 1,878 steps at ~11s/step on CPU — the script refuses it, projecting well past an hour.

On completed runs, the model card discloses a carved evaluation split, per-class training counts and classes below the requested sample count. Dropped-row counts and measured truncation are reported in the logs; retain those logs alongside the model when documenting data preparation.

Many classes: watch the pair count

SetFit trains on pairs drawn from every combination of training examples, so the pair count grows with the square of the training-set size — which is --num-samples x number of classes. The script logs the estimate before training starts:

Dataset	Strategy	Pairs	Steps
ag_news (4 classes x 8)	oversampling (default)	768	48
banking77 (77 classes x 8)	oversampling (default)	374,528	23,408
banking77 (77 classes x 8)	undersampling	4,312	270

At 77 classes the default would take roughly 15 hours on cpu-basic; --sampling-strategy undersampling finished in 18 seconds on a T4 in the recorded run. The script reports the pair and step counts, then measures step time to check --max-minutes. When it refuses training, it suggests undersampling where applicable and estimates whether that would fit the budget.

Choosing another body or longer context

--body-model accepts a Sentence Transformer checkpoint. Set --max-seq-length within that model's supported context window; increasing it cannot extend a model's native limit or restore text already shortened during dataset preparation. Longer sequences can need a smaller --batch-size or more GPU memory. The recipe measures training cost on the selected hardware.

Follow the body's task-prefix instructions when preparing inputs. For example, nomic-ai/modernbert-embed-base uses Nomic's task prefixes: classification inputs should begin with classification: . Include the same prefix during training, evaluation and inference. The recipe does not add it automatically. Retain the original texts and the preprocessing details with the model.

Behaviour details

• Evaluation split follows the same precedence as train-classifier.py: --eval-split if given, else validation, else test, else a stratified carve-out of --eval-fraction from train.
• Metrics match train-classifier.py (accuracy + macro F1). Match evaluation rows and preprocessing when comparing runs.
• --num-samples sets labelled examples per class (default 8). --sampling-strategy controls contrastive pairing: oversampling (default), undersampling, unique.
• Every run reports a majority baseline. The run warns when accuracy fails to beat it, or the gain is below five percentage points. That fixed threshold is a review heuristic, not a measured noise level or significance test.
• It estimates training time before starting. The script times forward/backward passes on actual texts and hardware, then refuses training projected above --max-minutes (default 60). Setup, evaluation and upload take additional time. A measurement error can skip this guard; use Jobs --timeout to enforce a wall-clock limit.
• Rows with missing or blank labels or texts are dropped, with a count. Missing labels include ClassLabel's -1 sentinel and numeric NaN; plain integer -1 remains a valid class. Splits with no usable labelled text, fewer than two observed training classes, and missing or non-string text columns exit before model loading.
• --private verifies the output repository is private before training. If the destination already exists publicly, choose a new repo or change its visibility first.