all_mini_lm_text_classifier_nyn

This model is a fine-tuned version of KasuleTrevor/all-MiniLM-L12-v2-rny-intents on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3562
  • Accuracy: 0.943
  • Precision: 0.945
  • Recall: 0.943
  • F1: 0.942

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
2.5968 1.0 293 1.7473 0.633 0.535 0.633 0.555
1.2106 2.0 586 0.6798 0.828 0.82 0.828 0.804
0.5287 3.0 879 0.4751 0.896 0.894 0.896 0.891
0.3188 4.0 1172 0.3477 0.904 0.916 0.904 0.901
0.2335 5.0 1465 0.2991 0.936 0.94 0.936 0.936
0.1855 6.0 1758 0.3184 0.932 0.937 0.932 0.932
0.1609 7.0 2051 0.3253 0.934 0.939 0.934 0.933
0.1398 8.0 2344 0.2569 0.949 0.952 0.949 0.949
0.1151 9.0 2637 0.3135 0.943 0.946 0.943 0.943
0.0997 10.0 2930 0.3000 0.951 0.953 0.951 0.951
0.0866 11.0 3223 0.3140 0.945 0.948 0.945 0.945
0.0615 12.0 3516 0.3165 0.945 0.948 0.945 0.945
0.0574 13.0 3809 0.3870 0.934 0.94 0.934 0.935
0.0459 14.0 4102 0.3428 0.947 0.951 0.947 0.947
0.0423 15.0 4395 0.3565 0.945 0.948 0.945 0.945
0.0412 16.0 4688 0.3655 0.945 0.948 0.945 0.945
0.0343 17.0 4981 0.3528 0.947 0.95 0.947 0.947
0.0289 18.0 5274 0.3567 0.943 0.945 0.943 0.942
0.0297 19.0 5567 0.3565 0.943 0.945 0.943 0.942
0.0286 20.0 5860 0.3562 0.943 0.945 0.943 0.942

Framework versions

  • Transformers 4.52.4
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1
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