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trained_FM_plus_minus_finetuned-finetuned

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metadata
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: trained_FM_plus_minus_finetuned-finetuned
    results: []

trained_FM_plus_minus_finetuned-finetuned

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0008
  • Accuracy: 1.0

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 6750

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.0201 0.0201 136 0.4265 0.8900
0.0655 1.0201 272 0.0014 1.0
0.0262 2.0201 408 1.2800 0.8034
0.045 3.0201 544 0.5063 0.9224
0.0027 4.0201 680 0.6001 0.9056
0.0475 5.0201 816 0.4429 0.9056
0.0512 6.0201 952 0.0526 0.9702
0.0005 7.0201 1088 0.8258 0.8978
0.001 8.0201 1224 0.9702 0.8978
0.0364 9.0201 1360 0.8333 0.8978
0.0335 10.0201 1496 0.4587 0.8978
0.0433 11.0201 1632 0.3666 0.9120
0.0015 12.0201 1768 0.8214 0.8978
0.0212 13.0201 1904 0.4954 0.9030
0.024 14.0201 2040 0.5142 0.9043
0.0029 15.0201 2176 0.7126 0.9030
0.039 16.0201 2312 0.9752 0.8978
0.0284 17.0201 2448 0.8832 0.8978
0.0007 18.0201 2584 1.0282 0.8978
0.0004 19.0201 2720 0.0305 0.9819
0.0293 20.0201 2856 0.7992 0.9017
0.0002 21.0201 2992 0.3760 0.9508
0.0006 22.0201 3128 0.3892 0.9107
0.0005 23.0201 3264 0.5964 0.9030
0.0338 24.0201 3400 0.1783 0.9431
0.0006 25.0201 3536 0.1233 0.9521
0.0135 26.0201 3672 0.4995 0.8978
0.0005 27.0201 3808 0.8097 0.8978
0.0446 28.0201 3944 0.9635 0.8978
0.001 29.0201 4080 0.8746 0.8978
0.0252 30.0201 4216 0.9896 0.8978
0.0002 31.0201 4352 0.9572 0.8978
0.0204 32.0201 4488 0.9263 0.8978
0.0365 33.0201 4624 0.1628 0.9379
0.0037 34.0201 4760 0.3472 0.9030
0.0004 35.0201 4896 0.0515 0.9702
0.0371 36.0201 5032 0.8920 0.8978
0.0002 37.0201 5168 0.5903 0.8978
0.0004 38.0201 5304 0.7191 0.8978
0.0004 39.0201 5440 0.7647 0.8978
0.0002 40.0201 5576 0.8480 0.8978
0.0001 41.0201 5712 0.9607 0.8978
0.0003 42.0201 5848 0.9465 0.8978
0.0002 43.0201 5984 0.9669 0.8978
0.0002 44.0201 6120 0.9628 0.8978
0.0003 45.0201 6256 0.9807 0.8978
0.0 46.0201 6392 0.9692 0.8978
0.0257 47.0201 6528 0.9883 0.8978
0.0001 48.0201 6664 0.9766 0.8978
0.0001 49.0127 6750 0.9790 0.8978

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1