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ModernBERT_wine_quality_reviews_ft

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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: 0.4820
  • 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