---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: ModernBERT_wine_quality_reviews_ft
  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_quality_reviews_ft

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6800
- Accuracy: 0.6953
- F1: 0.6945

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.2688        | 0.0590 | 250  | 1.1315          | 0.4781   | 0.4463 |
| 1.0574        | 0.1181 | 500  | 0.9664          | 0.5575   | 0.5412 |
| 0.9229        | 0.1771 | 750  | 0.8647          | 0.6070   | 0.6007 |
| 0.8654        | 0.2361 | 1000 | 0.8665          | 0.6089   | 0.5922 |
| 0.8229        | 0.2952 | 1250 | 0.7857          | 0.6448   | 0.6448 |
| 0.8054        | 0.3542 | 1500 | 0.8515          | 0.6218   | 0.5993 |
| 0.786         | 0.4132 | 1750 | 0.7533          | 0.6601   | 0.6552 |
| 0.781         | 0.4723 | 2000 | 0.8133          | 0.6305   | 0.6278 |
| 0.7563        | 0.5313 | 2250 | 0.7770          | 0.6480   | 0.6473 |
| 0.7638        | 0.5903 | 2500 | 0.7248          | 0.6767   | 0.6769 |
| 0.7384        | 0.6494 | 2750 | 0.7520          | 0.6597   | 0.6574 |
| 0.7405        | 0.7084 | 3000 | 0.7615          | 0.6545   | 0.6515 |
| 0.7222        | 0.7674 | 3250 | 0.7191          | 0.6790   | 0.6716 |
| 0.7184        | 0.8264 | 3500 | 0.7037          | 0.6862   | 0.6837 |
| 0.6984        | 0.8855 | 3750 | 0.7264          | 0.6716   | 0.6678 |
| 0.6995        | 0.9445 | 4000 | 0.7455          | 0.6663   | 0.6646 |
| 0.713         | 1.0035 | 4250 | 0.7294          | 0.6752   | 0.6701 |
| 0.6508        | 1.0626 | 4500 | 0.6938          | 0.6872   | 0.6871 |
| 0.642         | 1.1216 | 4750 | 0.7266          | 0.6716   | 0.6691 |
| 0.635         | 1.1806 | 5000 | 0.6868          | 0.6913   | 0.6900 |
| 0.6278        | 1.2397 | 5250 | 0.6800          | 0.6953   | 0.6945 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0