README.md

metadata

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: []

ModernBERT_wine_quality_reviews_ft

This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8255
• Accuracy: 0.6865
• F1: 0.6873

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: 64
• eval_batch_size: 64
• 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
1.0765	0.1653	350	0.8973	0.5849	0.5797
0.848	0.3305	700	0.7721	0.6516	0.6483
0.7796	0.4958	1050	0.7682	0.6466	0.6470
0.7671	0.6610	1400	0.7448	0.6611	0.6566
0.7434	0.8263	1750	0.7378	0.6643	0.6634
0.7232	0.9915	2100	0.7086	0.6789	0.6736
0.653	1.1568	2450	0.7150	0.6768	0.6764
0.6312	1.3220	2800	0.7119	0.6785	0.6761
0.6298	1.4873	3150	0.6982	0.6879	0.6843
0.6307	1.6525	3500	0.7072	0.6863	0.6864
0.6338	1.8178	3850	0.6950	0.6862	0.6813
0.6252	1.9830	4200	0.6996	0.6850	0.6853
0.4418	2.1483	4550	0.8353	0.6911	0.6899
0.4016	2.3135	4900	0.8428	0.6825	0.6815
0.404	2.4788	5250	0.8241	0.6824	0.6822
0.404	2.6440	5600	0.8255	0.6865	0.6873

Framework versions

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