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---
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: 0.4820
- Accuracy: 0.7864
- F1: 0.6767
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.7593 | 0.0497 | 250 | 0.5938 | 0.7402 | 0.5293 |
| 0.6378 | 0.0993 | 500 | 0.6212 | 0.7308 | 0.5121 |
| 0.6001 | 0.1490 | 750 | 0.5726 | 0.7499 | 0.5482 |
| 0.5945 | 0.1987 | 1000 | 0.5661 | 0.7504 | 0.5390 |
| 0.5806 | 0.2484 | 1250 | 0.5519 | 0.7513 | 0.5693 |
| 0.6042 | 0.2980 | 1500 | 0.5528 | 0.7535 | 0.5945 |
| 0.5719 | 0.3477 | 1750 | 0.5290 | 0.7705 | 0.6045 |
| 0.5699 | 0.3974 | 2000 | 0.5247 | 0.7637 | 0.5958 |
| 0.5622 | 0.4470 | 2250 | 0.5318 | 0.7700 | 0.5960 |
| 0.5597 | 0.4967 | 2500 | 0.5344 | 0.7588 | 0.6152 |
| 0.5511 | 0.5464 | 2750 | 0.5767 | 0.7373 | 0.5459 |
| 0.5464 | 0.5961 | 3000 | 0.5078 | 0.7787 | 0.6460 |
| 0.5375 | 0.6457 | 3250 | 0.5029 | 0.7821 | 0.6175 |
| 0.5468 | 0.6954 | 3500 | 0.5027 | 0.7835 | 0.6312 |
| 0.5232 | 0.7451 | 3750 | 0.5053 | 0.7765 | 0.6365 |
| 0.5459 | 0.7948 | 4000 | 0.4983 | 0.7889 | 0.6723 |
| 0.5241 | 0.8444 | 4250 | 0.5092 | 0.7826 | 0.6751 |
| 0.5094 | 0.8941 | 4500 | 0.5215 | 0.7831 | 0.6233 |
| 0.5223 | 0.9438 | 4750 | 0.4986 | 0.7881 | 0.6243 |
| 0.5161 | 0.9934 | 5000 | 0.5013 | 0.7869 | 0.6205 |
| 0.4474 | 1.0431 | 5250 | 0.5010 | 0.7847 | 0.6689 |
| 0.4417 | 1.0928 | 5500 | 0.4923 | 0.7898 | 0.6251 |
| 0.4316 | 1.1425 | 5750 | 0.4968 | 0.7903 | 0.6950 |
| 0.4244 | 1.1921 | 6000 | 0.4958 | 0.7867 | 0.6950 |
| 0.4479 | 1.2418 | 6250 | 0.4952 | 0.7874 | 0.6864 |
| 0.4332 | 1.2915 | 6500 | 0.4957 | 0.7801 | 0.6741 |
| 0.4357 | 1.3411 | 6750 | 0.4887 | 0.7864 | 0.6397 |
| 0.439 | 1.3908 | 7000 | 0.5425 | 0.7654 | 0.6739 |
| 0.4298 | 1.4405 | 7250 | 0.4830 | 0.7864 | 0.6849 |
| 0.4345 | 1.4902 | 7500 | 0.4970 | 0.7821 | 0.6324 |
| 0.432 | 1.5398 | 7750 | 0.4890 | 0.7874 | 0.6772 |
| 0.433 | 1.5895 | 8000 | 0.4920 | 0.7893 | 0.6716 |
| 0.4364 | 1.6392 | 8250 | 0.5033 | 0.7818 | 0.6840 |
| 0.4196 | 1.6889 | 8500 | 0.4845 | 0.7886 | 0.6674 |
| 0.4199 | 1.7385 | 8750 | 0.5046 | 0.7814 | 0.6377 |
| 0.4121 | 1.7882 | 9000 | 0.5066 | 0.7845 | 0.6852 |
| 0.4222 | 1.8379 | 9250 | 0.4951 | 0.7852 | 0.6669 |
| 0.4217 | 1.8875 | 9500 | 0.4820 | 0.7864 | 0.6767 |
| 0.3973 | 1.9372 | 9750 | 0.4964 | 0.7872 | 0.7037 |
| 0.4297 | 1.9869 | 10000 | 0.4872 | 0.7840 | 0.6811 |
| 0.3039 | 2.0366 | 10250 | 0.6112 | 0.7755 | 0.6741 |
| 0.2271 | 2.0862 | 10500 | 0.6606 | 0.7797 | 0.6878 |
| 0.2149 | 2.1359 | 10750 | 0.6955 | 0.7736 | 0.6823 |
| 0.2202 | 2.1856 | 11000 | 0.7086 | 0.7772 | 0.6681 |
| 0.2226 | 2.2352 | 11250 | 0.6691 | 0.7743 | 0.6797 |
| 0.2162 | 2.2849 | 11500 | 0.6852 | 0.7741 | 0.6702 |
| 0.198 | 2.3346 | 11750 | 0.7187 | 0.7763 | 0.6592 |
| 0.2053 | 2.3843 | 12000 | 0.6847 | 0.7782 | 0.6816 |
| 0.2099 | 2.4339 | 12250 | 0.7302 | 0.7748 | 0.6789 |
| 0.214 | 2.4836 | 12500 | 0.7198 | 0.7726 | 0.6831 |
| 0.1938 | 2.5333 | 12750 | 0.7529 | 0.7741 | 0.6390 |
| 0.1959 | 2.5830 | 13000 | 0.7467 | 0.7724 | 0.6783 |
| 0.2012 | 2.6326 | 13250 | 0.7245 | 0.7736 | 0.6456 |
| 0.1915 | 2.6823 | 13500 | 0.7603 | 0.7678 | 0.6823 |
| 0.2 | 2.7320 | 13750 | 0.7175 | 0.7748 | 0.6795 |
| 0.1921 | 2.7816 | 14000 | 0.7458 | 0.7755 | 0.6809 |
| 0.1895 | 2.8313 | 14250 | 0.7607 | 0.7729 | 0.6603 |
| 0.1855 | 2.8810 | 14500 | 0.7625 | 0.7736 | 0.6705 |
| 0.1878 | 2.9307 | 14750 | 0.7598 | 0.7709 | 0.6707 |
| 0.1899 | 2.9803 | 15000 | 0.7604 | 0.7729 | 0.6714 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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