StanfordSCALE/assertion_invites_writing_on_the_board

Assertion: the utterance invites writing on the board

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	13,183 (53.3%)
StanfordSCALE/assertions_llm_annotated_talkmoves	dev	3,296 (13.3%)
StanfordSCALE/assertions_llm_annotated_talkmoves	test	8,236 (33.3%)

This model's columns are assertion_invites_writing_on_the_board and split_invites_writing_on_the_board.

Base rate (share of rows labeled as True): 0.4% overall — 0.4% train, 0.4% dev, 0.5% test.

Labels and annotation

Labels were generated with LLM annotators. Krippendorff's alpha for this assertion is 0.744.

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	3,296	0.4%	0.500	0.143	0.222	0.810	0.289
test	8,236	0.5%	0.821	0.575	0.676	0.849	0.566

Limitations

• Labels come from LLM annotators, not human coders. Agreement between annotators with Krippendorff's Alpha is 0.744.
• 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_invites_writing_on_the_board")

text = 'Andrew would you like to come also'
model.predict([text])        # -> array([1]) when the assertion holds
model.predict_proba([text])  # -> [[P(no), P(yes)]]

Citation

@misc{assertion_invites_writing_on_the_board,
  author = {Stanford SCALE Initiative},
  title  = {Assertion classifier: the utterance invites writing on the board},
  year   = {2026},
  url    = {https://huggingface.co/StanfordSCALE/assertion_invites_writing_on_the_board}
}