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dit-tiny_tobacco3482_kd_CEKD_t2.0_a0.5

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

  • Loss: 3.5976
  • Accuracy: 0.18
  • Brier Loss: 0.8781
  • Nll: 6.8947
  • F1 Micro: 0.18
  • F1 Macro: 0.0306
  • Ece: 0.2499
  • Aurc: 0.8510

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 0.96 3 3.8479 0.145 0.8999 10.1604 0.145 0.0253 0.2222 0.8467
No log 1.96 6 3.8090 0.145 0.8946 10.5967 0.145 0.0253 0.2246 0.8470
No log 2.96 9 3.7500 0.16 0.8866 8.6365 0.16 0.0406 0.2205 0.8486
No log 3.96 12 3.7003 0.16 0.8805 6.5484 0.16 0.0327 0.2242 0.8816
No log 4.96 15 3.6677 0.155 0.8776 6.7592 0.155 0.0271 0.2365 0.8919
No log 5.96 18 3.6477 0.155 0.8770 7.2639 0.155 0.0278 0.2368 0.8961
No log 6.96 21 3.6339 0.18 0.8774 7.3546 0.18 0.0313 0.2486 0.8556
No log 7.96 24 3.6240 0.18 0.8781 7.0685 0.18 0.0308 0.2654 0.8528
No log 8.96 27 3.6163 0.18 0.8784 7.0041 0.18 0.0306 0.2561 0.8532
No log 9.96 30 3.6114 0.18 0.8787 6.9904 0.18 0.0306 0.2584 0.8537
No log 10.96 33 3.6078 0.18 0.8788 6.9806 0.18 0.0306 0.2594 0.8538
No log 11.96 36 3.6052 0.18 0.8789 6.9768 0.18 0.0306 0.2596 0.8537
No log 12.96 39 3.6034 0.18 0.8788 6.9716 0.18 0.0306 0.2507 0.8532
No log 13.96 42 3.6018 0.18 0.8786 6.9683 0.18 0.0306 0.2548 0.8527
No log 14.96 45 3.6005 0.18 0.8786 6.9040 0.18 0.0306 0.2597 0.8524
No log 15.96 48 3.5995 0.18 0.8784 6.8978 0.18 0.0306 0.2685 0.8518
No log 16.96 51 3.5989 0.18 0.8784 6.8972 0.18 0.0306 0.2641 0.8515
No log 17.96 54 3.5989 0.18 0.8784 6.8961 0.18 0.0306 0.2550 0.8513
No log 18.96 57 3.5988 0.18 0.8784 6.8968 0.18 0.0306 0.2505 0.8510
No log 19.96 60 3.5982 0.18 0.8782 6.8956 0.18 0.0306 0.2478 0.8511
No log 20.96 63 3.5980 0.18 0.8782 6.8954 0.18 0.0306 0.2456 0.8507
No log 21.96 66 3.5978 0.18 0.8782 6.8951 0.18 0.0306 0.2499 0.8511
No log 22.96 69 3.5976 0.18 0.8781 6.8949 0.18 0.0306 0.2499 0.8510
No log 23.96 72 3.5976 0.18 0.8781 6.8949 0.18 0.0306 0.2499 0.8510
No log 24.96 75 3.5976 0.18 0.8781 6.8947 0.18 0.0306 0.2499 0.8510

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1.post200
  • Datasets 2.9.0
  • Tokenizers 0.13.2
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