---
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