| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: answerdotai/ModernBERT-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: modernbert-wine-classification |
| | 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-wine-classification |
| |
|
| | 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: 0.6234 |
| | - Accuracy: 0.9414 |
| | - F1: 0.9411 |
| |
|
| | ## 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: 8e-05 |
| | - train_batch_size: 192 |
| | - eval_batch_size: 192 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.08 |
| | - num_epochs: 5 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
| | | 1.7318 | 0.2591 | 100 | 0.8266 | 0.6828 | 0.7221 | |
| | | 0.8015 | 0.5181 | 200 | 0.5297 | 0.8188 | 0.8271 | |
| | | 0.6969 | 0.7772 | 300 | 0.4705 | 0.8342 | 0.8482 | |
| | | 0.5794 | 1.0363 | 400 | 0.4594 | 0.8686 | 0.8714 | |
| | | 0.4761 | 1.2953 | 500 | 0.4386 | 0.8736 | 0.8777 | |
| | | 0.4406 | 1.5544 | 600 | 0.4049 | 0.8858 | 0.8874 | |
| | | 0.4279 | 1.8135 | 700 | 0.4328 | 0.8955 | 0.8965 | |
| | | 0.4206 | 2.0725 | 800 | 0.4511 | 0.9006 | 0.9011 | |
| | | 0.2375 | 2.3316 | 900 | 0.4064 | 0.9018 | 0.9034 | |
| | | 0.1855 | 2.5907 | 1000 | 0.4421 | 0.9099 | 0.9109 | |
| | | 0.1951 | 2.8497 | 1100 | 0.3979 | 0.9140 | 0.9150 | |
| | | 0.1464 | 3.1088 | 1200 | 0.5462 | 0.9253 | 0.9253 | |
| | | 0.0533 | 3.3679 | 1300 | 0.5703 | 0.9313 | 0.9314 | |
| | | 0.0508 | 3.6269 | 1400 | 0.5185 | 0.9343 | 0.9342 | |
| | | 0.0488 | 3.8860 | 1500 | 0.5403 | 0.9378 | 0.9375 | |
| | | 0.0268 | 4.1451 | 1600 | 0.5958 | 0.9399 | 0.9396 | |
| | | 0.007 | 4.4041 | 1700 | 0.5955 | 0.9379 | 0.9377 | |
| | | 0.0052 | 4.6632 | 1800 | 0.6330 | 0.9400 | 0.9397 | |
| | | 0.0049 | 4.9223 | 1900 | 0.6234 | 0.9414 | 0.9411 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.48.3 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.2.0 |
| | - Tokenizers 0.21.0 |
| | |
