pii-dual-nym-mmbert-base-v6
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: 0.0025
- Precision: 0.9957
- Recall: 0.9974
- F1: 0.9965
- Accuracy: 0.9994
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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_with_restarts
- lr_scheduler_warmup_steps: 200
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0178 | 0.8002 | 2000 | 0.0059 | 0.9856 | 0.9910 | 0.9883 | 0.9983 |
| 0.0082 | 1.6001 | 4000 | 0.0041 | 0.9906 | 0.9949 | 0.9928 | 0.9988 |
| 0.0020 | 2.4001 | 6000 | 0.0031 | 0.9948 | 0.9962 | 0.9955 | 0.9992 |
| 0.0004 | 3.2000 | 8000 | 0.0023 | 0.9959 | 0.9975 | 0.9967 | 0.9994 |
| 0.0003 | 4.0 | 10000 | 0.0025 | 0.9957 | 0.9974 | 0.9965 | 0.9994 |
Framework versions
- Transformers 5.19.0
- Pytorch 2.14.1+cu130
- Datasets 5.1.0
- Tokenizers 0.23.3