mmbert-small-vi-exam-seq-labeling · Model Card
mmbert-small-vi-exam-seq-labeling: Model Card
Written by Dao Minh, published under mit, revision 7b3607a47e94, read 2026-09-24. Shown as written; SAVRN's own facts about this model are on its page.
This model is a fine-tuned version of jhu-clsp/mmBERT-base on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0737 - Precision: 0.2997 - Recall: 0.5324 - F1: 0.3835 - Accuracy: 0.7187
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 8 - 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: cosine - lr_scheduler_warmup_steps: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.8259 | 1.0 | 26 | 0.2873 | 0.0791 | 0.4344 | 0.1339 | 0.5528 |
| 0.2752 | 2.0 | 52 | 0.0903 | 0.2176 | 0.4726 | 0.2980 | 0.7671 |
| 0.1365 | 3.0 | 78 | 0.0586 | 0.2805 | 0.4862 | 0.3558 | 0.7965 |
| 0.1134 | 4.0 | 104 | 0.0531 | 0.2974 | 0.4862 | 0.3691 | 0.8044 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2