---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# nci-binary-detector

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/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