nci-ner-v2-staging
This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0727
- Precision: 0.8815
- Recall: 0.9097
- F1: 0.8954
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.2779 | 0.5339 | 500 | 0.1139 | 0.8024 | 0.8149 | 0.8086 |
| 0.1683 | 1.0673 | 1000 | 0.0903 | 0.8271 | 0.8766 | 0.8512 |
| 0.1431 | 1.6012 | 1500 | 0.0836 | 0.8484 | 0.8855 | 0.8666 |
| 0.1106 | 2.1345 | 2000 | 0.0833 | 0.8535 | 0.8895 | 0.8711 |
| 0.0950 | 2.6684 | 2500 | 0.0879 | 0.8532 | 0.8895 | 0.8710 |
| 0.0832 | 3.2018 | 3000 | 0.0832 | 0.8624 | 0.8919 | 0.8769 |
| 0.0728 | 3.7357 | 3500 | 0.0833 | 0.8619 | 0.8925 | 0.8769 |
| 0.0688 | 4.2691 | 4000 | 0.0853 | 0.8599 | 0.8956 | 0.8774 |
| 0.0734 | 4.8030 | 4500 | 0.0846 | 0.8610 | 0.8956 | 0.8780 |
| 0.0667 | 5.0 | 4685 | 0.0847 | 0.8607 | 0.8953 | 0.8777 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.13.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2