| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: answerdotai/ModernBERT-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: modernbert-output |
| 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. --> |
|
|
| # modernbert-output |
|
|
| 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: 0.6180 |
| - Accuracy: 0.8867 |
| - Macro F1: 0.8872 |
|
|
| ## 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: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - 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 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
| | 3.0392 | 1.0 | 140 | 0.6617 | 0.8267 | 0.8251 | |
| | 1.4070 | 2.0 | 280 | 0.5377 | 0.8733 | 0.8759 | |
| | 0.5328 | 3.0 | 420 | 0.6954 | 0.86 | 0.8646 | |
| | 0.3036 | 4.0 | 560 | 0.6180 | 0.8867 | 0.8872 | |
| | 0.0971 | 5.0 | 700 | 0.6528 | 0.88 | 0.8813 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.3.0 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.4 |
| - Tokenizers 0.22.2 |
|
|
