StanfordSCALE/assertion_sentence_grants_or_requests_permission

Assertion: sentence grants or requests permission

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_grants_or_requests_permission and split_sentence_grants_or_requests_permission.

Base rate (share of rows labeled as True): 1.2% overall — 1.0% train, 2.1% dev, 1.2% test.

Labels and annotation

Labels were generated with LLM annotators. Krippendorff's alpha for this assertion is 0.500.

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	2.1%	0.333	0.111	0.167	0.679	0.191
test	2,146	1.2%	0.250	0.077	0.118	0.651	0.135

Limitations

• Labels come from LLM annotators, not human coders. Agreement between annotators with Krippendorff's Alpha is 0.500.
• Trained on teacher utterances only. Behaviour on student speech is untested.
• Only 26 positive examples in the test split, so the test scores above carry a wide margin of error.
• Test F1 is 0.118. This model does not work well enough to be used on its own.

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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_grants_or_requests_permission")

text = 'Go ahead'
model.predict([text])        # -> array([1]) when the assertion holds
model.predict_proba([text])  # -> [[P(no), P(yes)]]

Citation

@misc{assertion_sentence_grants_or_requests_permission,
  author = {Stanford SCALE Initiative},
  title  = {Assertion classifier: sentence grants or requests permission},
  year   = {2026},
  url    = {https://huggingface.co/StanfordSCALE/assertion_sentence_grants_or_requests_permission}
}