---
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: 0.6671
- Accuracy: 0.7019
- F1: 0.7024

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.8,0.8) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 1.1457        | 0.0826 | 350   | 0.9894          | 0.5461   | 0.5305 |
| 0.9441        | 0.1653 | 700   | 1.1213          | 0.4977   | 0.4827 |
| 0.8589        | 0.2479 | 1050  | 0.8232          | 0.6297   | 0.6277 |
| 0.8131        | 0.3306 | 1400  | 0.8268          | 0.6177   | 0.5956 |
| 0.7837        | 0.4132 | 1750  | 0.7474          | 0.6679   | 0.6663 |
| 0.7726        | 0.4959 | 2100  | 0.8008          | 0.6397   | 0.6269 |
| 0.7576        | 0.5785 | 2450  | 0.7571          | 0.6533   | 0.6550 |
| 0.7528        | 0.6612 | 2800  | 0.7414          | 0.6666   | 0.6598 |
| 0.7588        | 0.7438 | 3150  | 0.7627          | 0.6588   | 0.6397 |
| 0.7416        | 0.8264 | 3500  | 0.7259          | 0.6736   | 0.6739 |
| 0.7303        | 0.9091 | 3850  | 0.7052          | 0.6847   | 0.6812 |
| 0.7313        | 0.9917 | 4200  | 0.7059          | 0.6860   | 0.6799 |
| 0.6647        | 1.0744 | 4550  | 0.7002          | 0.6890   | 0.6887 |
| 0.6606        | 1.1570 | 4900  | 0.7712          | 0.6583   | 0.6502 |
| 0.65          | 1.2397 | 5250  | 0.6868          | 0.6917   | 0.6904 |
| 0.6464        | 1.3223 | 5600  | 0.7371          | 0.6757   | 0.6673 |
| 0.6494        | 1.4050 | 5950  | 0.7323          | 0.6751   | 0.6724 |
| 0.6505        | 1.4876 | 6300  | 0.6952          | 0.6877   | 0.6856 |
| 0.6499        | 1.5702 | 6650  | 0.6935          | 0.6893   | 0.6812 |
| 0.6399        | 1.6529 | 7000  | 0.7099          | 0.6873   | 0.6826 |
| 0.632         | 1.7355 | 7350  | 0.6912          | 0.6942   | 0.6915 |
| 0.6488        | 1.8182 | 7700  | 0.6741          | 0.6971   | 0.6972 |
| 0.6331        | 1.9008 | 8050  | 0.6881          | 0.6933   | 0.6932 |
| 0.6339        | 1.9835 | 8400  | 0.6671          | 0.7019   | 0.7024 |
| 0.4914        | 2.0661 | 8750  | 0.7598          | 0.6989   | 0.6982 |
| 0.4498        | 2.1488 | 9100  | 0.7617          | 0.6997   | 0.6996 |
| 0.4407        | 2.2314 | 9450  | 0.7674          | 0.6950   | 0.6945 |
| 0.4468        | 2.3140 | 9800  | 0.7978          | 0.6946   | 0.6932 |
| 0.4486        | 2.3967 | 10150 | 0.7718          | 0.6929   | 0.6926 |
| 0.4462        | 2.4793 | 10500 | 0.7928          | 0.6808   | 0.6811 |
| 0.4483        | 2.5620 | 10850 | 0.7678          | 0.6957   | 0.6966 |
| 0.4347        | 2.6446 | 11200 | 0.7687          | 0.6935   | 0.6938 |
| 0.4429        | 2.7273 | 11550 | 0.7496          | 0.6969   | 0.6973 |
| 0.4415        | 2.8099 | 11900 | 0.7621          | 0.6968   | 0.6963 |


### Framework versions

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