Model save

README.md

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+---
+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: 0.0010
+- Accuracy: 0.9977
+- F1: 0.9980
+- Precision: 0.9970
+- Recall: 0.9990
+- Roc Auc: 0.9999
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
+| 0.011         | 0.1634 | 100  | 0.0047          | 0.9867   | 0.9883 | 0.9949    | 0.9818 | 0.9987  |
+| 0.0017        | 0.3268 | 200  | 0.0046          | 0.9971   | 0.9975 | 0.9970    | 0.9980 | 0.9993  |
+| 0.0004        | 0.4902 | 300  | 0.0028          | 0.9971   | 0.9975 | 0.9980    | 0.9970 | 0.9999  |
+| 0.0106        | 0.6536 | 400  | 0.0008          | 0.9983   | 0.9985 | 0.9980    | 0.9990 | 1.0000  |
+| 0.0001        | 0.8170 | 500  | 0.0011          | 0.9983   | 0.9985 | 0.9980    | 0.9990 | 1.0000  |
+| 0.0007        | 0.9804 | 600  | 0.0012          | 0.9983   | 0.9985 | 0.9980    | 0.9990 | 1.0000  |
+| 0.0           | 1.1438 | 700  | 0.0010          | 0.9988   | 0.9990 | 0.9980    | 1.0    | 1.0000  |
+| 0.0021        | 1.3072 | 800  | 0.0006          | 0.9977   | 0.9980 | 0.9980    | 0.9980 | 1.0000  |
+| 0.0018        | 1.4706 | 900  | 0.0010          | 0.9988   | 0.9990 | 0.9980    | 1.0    | 1.0000  |
+| 0.0012        | 1.6340 | 1000 | 0.0017          | 0.9977   | 0.9980 | 0.9960    | 1.0    | 1.0000  |
+
+
+### Framework versions
+
+- Transformers 4.57.3
+- Pytorch 2.9.1+cu128
+- Datasets 4.4.1
+- Tokenizers 0.22.1

model.safetensors

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