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on-task-bert
edsi-umd/on-task-bert
on-task-bert is a text classification model from edsi-umd. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model classifies a student's classroom utterance as off-task (0) or on-task (1). It is a BERT-base-uncased sequence classifier fine-tuned on English student utterances from mathematics classroom transcripts.
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jul 22, 2026
- Updated
- Jul 22, 2026
- SHA
- d7af4c0f
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Model card
From the Hugging Face model README
On-Task BERT
This model classifies a student's classroom utterance as off-task (0) or
on-task (1). It is a BERT-base-uncased sequence classifier fine-tuned on
English student utterances from mathematics classroom transcripts.
Evaluation
The final model was selected using five-fold cross-validation on the training and validation data, retrained on 1,878 examples, and evaluated once on a held-out test set of 470 examples.
| Metric | Result |
|---|---|
| Accuracy | 0.900 |
| Macro F1 | 0.813 |
| On-task precision | 0.935 |
| On-task recall | 0.947 |
| On-task F1 | 0.941 |
The test confusion counts were 372 true positives, 26 false positives, 21 false negatives, and 51 true negatives. On the same test split, a math-vocabulary baseline achieved 0.849 accuracy and 0.559 macro F1.
Training details
- Base model:
bert-base-uncased - Maximum sequence length: 256
- Epochs: 5
- Learning rate: 3e-5
- Input: student utterance only
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- en
- Created
- Jul 22, 2026
- Updated
- Jul 22, 2026