metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: nci-binary-detector
results: []
nci-binary-detector
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0031
- Accuracy: 0.9954
- F1: 0.9959
- Precision: 0.9919
- Recall: 1.0
- Roc Auc: 0.9986
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: 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.0093 | 0.1634 | 100 | 0.0043 | 0.9844 | 0.9865 | 0.9763 | 0.9970 | 0.9990 |
| 0.0021 | 0.3268 | 200 | 0.0036 | 0.9954 | 0.9960 | 0.9930 | 0.9990 | 0.9978 |
| 0.0001 | 0.4902 | 300 | 0.0011 | 0.9988 | 0.9990 | 0.9980 | 1.0 | 0.9999 |
| 0.0043 | 0.6536 | 400 | 0.0009 | 0.9959 | 0.9965 | 0.9930 | 1.0 | 1.0000 |
| 0.0001 | 0.8170 | 500 | 0.0006 | 0.9988 | 0.9990 | 0.9980 | 1.0 | 1.0000 |
| 0.0006 | 0.9804 | 600 | 0.0010 | 0.9977 | 0.9980 | 0.9980 | 0.9980 | 0.9999 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1