| --- |
| base_model: roberta-base |
| library_name: transformers |
| license: mit |
| pipeline_tag: text-classification |
| metrics: |
| - accuracy |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: roberta-sentence-classifier |
| results: [] |
| --- |
| |
| # roberta-sentence-classifier |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) presented in the paper **[Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction](https://huggingface.co/papers/2606.28186)**. |
|
|
| It serves as the sentence-level cognitive episode tagger in the **Epi2Diff** (Episode to Difficulty) framework. It maps Large Reasoning Model (LRM) reasoning traces into cognitively grounded episode sequences to support interpretable modeling of human item difficulty. |
|
|
| - **Repository:** [c-steve-wang/Epi2Diff](https://github.com/c-steve-wang/Epi2Diff) |
| - **Paper:** [Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction](https://huggingface.co/papers/2606.28186) |
|
|
| ## Model description |
|
|
| The model classifies sentence-level reasoning units into 8 problem-solving cognitive episode states: |
| - `Read` |
| - `Analyze` |
| - `Plan` |
| - `Implement` |
| - `Explore` |
| - `Verify` |
| - `Monitor` |
| - `Answer` |
|
|
| These classified sequences are then used by the Epi2Diff framework to extract compact episode-dynamic process features for downstream item difficulty prediction. |
|
|
| ## Intended uses & limitations |
|
|
| You can use this model to segment and tag raw reasoning traces into functional problem-solving states to evaluate reasoning behaviors, perform interpretability studies, or support downstream educational measurement tasks. |
|
|
| ## Training and evaluation data |
|
|
| The model was fine-tuned on annotated reasoning trace sentences derived from datasets such as SAT Math, SAT Reading & Writing, Cambridge, and USMLE. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.6266 |
| - Accuracy: 0.7990 |
| - Macro F1: 0.7614 |
| - Micro F1: 0.7990 |
| - Qwk: 0.6588 |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 | Qwk | |
| |:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|:--------:|:------:| |
| | 0.6267 | 1.0 | 27540 | 0.6108 | 0.7818 | 0.7364 | 0.7818 | 0.6352 | |
| | 0.5539 | 2.0 | 55080 | 0.5939 | 0.7911 | 0.7498 | 0.7911 | 0.6428 | |
| | 0.475 | 3.0 | 82620 | 0.6021 | 0.7977 | 0.7592 | 0.7977 | 0.6599 | |
| | 0.4204 | 4.0 | 110160 | 0.6266 | 0.7990 | 0.7614 | 0.7990 | 0.6588 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.3 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
| |
| ## Citation |
| |
| If you use this model, please cite: |
| |
| ```bibtex |
| @misc{wang2026epi2diff, |
| title = {Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction}, |
| author = {Wang, Chenguang and Li, Ming and Zeng, Xinyue and Li, Zhuochun and Jiao, Hong and Zhou, Tianyi and Zhou, Dawei}, |
| year = {2026} |
| } |
| ``` |
