StanfordSCALE/assertion_sentence_has_comparison_terms

Assertion: sentence has comparison terms

This classifier was trained for EduBehaviors: Assertion-based schemas for auditable dialogue coding and is usable through the Python package EduBehaviors-kit. This classifier was trained on an LLM-annotated subset of teacher utterances from the TalkMoves Dataset. See the Datasets section below for more information.

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Training Details

Datasets

Dataset	Split	Size
StanfordSCALE/assertions_llm_annotated_talkmoves	train	3,430 (53.3%)
StanfordSCALE/assertions_llm_annotated_talkmoves	dev	858 (13.3%)
StanfordSCALE/assertions_llm_annotated_talkmoves	test	2,146 (33.4%)

This model's columns are assertion_sentence_has_comparison_terms and split_sentence_has_comparison_terms.

Base rate (share of rows labeled as True): 6.2% overall — 6.2% train, 6.3% dev, 6.2% test.

Labels and annotation

Labels were generated with LLM annotators. Krippendorff's alpha for this assertion is 0.657.

Hyperparameters

Parameter	Value
Base model (body)	sentence-transformers/paraphrase-mpnet-base-v2
Head	LogisticRegression
Body learning rate	2e-05
Head learning rate	0.01
Batch size	16 (contrastive phase) / 32 (head)
Epochs	10
Max steps	5000 (contrastive phase)
Eval max steps	100
Seed	20260904
Mixed precision	enabled on GPU

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Evaluation

Results

Split	n	Base rate	Precision	Recall	F1 (positive class)	ROC-AUC	Average precision
dev	858	6.3%	0.677	0.778	0.724	0.962	0.748
test	2,146	6.2%	0.779	0.813	0.796	0.943	0.840

Limitations

• Labels come from LLM annotators, not human coders. Agreement between annotators with Krippendorff's Alpha is 0.657.
• Trained on teacher utterances only. Behaviour on student speech is untested.

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How to Use

Message Structure

The model was trained on text built as:

{utterance}

The utterance is passed through as-is.

Running instructions

pip install setfit

from setfit import SetFitModel

model = SetFitModel.from_pretrained("StanfordSCALE/assertion_sentence_has_comparison_terms")

text = 'What I want you to focus on today is how can you relate the two diameters to the slant height and then how can you kind of think about the relationship between all three measurements'
model.predict([text])        # -> array([1]) when the assertion holds
model.predict_proba([text])  # -> [[P(no), P(yes)]]

Citation

@misc{assertion_sentence_has_comparison_terms,
  author = {Stanford SCALE Initiative},
  title  = {Assertion classifier: sentence has comparison terms},
  year   = {2026},
  url    = {https://huggingface.co/StanfordSCALE/assertion_sentence_has_comparison_terms}
}