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update model card README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.5
    results: []

dit-tiny_tobacco3482_kd_CEKD_t2.5_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: 3.9560
  • Accuracy: 0.18
  • Brier Loss: 0.8800
  • Nll: 6.8606
  • F1 Micro: 0.18
  • F1 Macro: 0.0306
  • Ece: 0.2612
  • Aurc: 0.8512

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 4.2281 0.145 0.8999 10.1620 0.145 0.0253 0.2222 0.8467
No log 1.96 6 4.1872 0.145 0.8946 10.5915 0.145 0.0253 0.2275 0.8468
No log 2.96 9 4.1248 0.155 0.8866 8.6280 0.155 0.0360 0.2179 0.8487
No log 3.96 12 4.0716 0.155 0.8806 6.5480 0.155 0.0272 0.2254 0.8851
No log 4.96 15 4.0359 0.155 0.8778 6.7781 0.155 0.0271 0.2310 0.8931
No log 5.96 18 4.0135 0.155 0.8774 7.8547 0.155 0.0271 0.2345 0.8965
No log 6.96 21 3.9978 0.185 0.8779 8.3528 0.185 0.0468 0.2615 0.8612
No log 7.96 24 3.9867 0.18 0.8789 7.6001 0.18 0.0308 0.2618 0.8546
No log 8.96 27 3.9782 0.18 0.8796 7.0871 0.18 0.0306 0.2613 0.8538
No log 9.96 30 3.9726 0.18 0.8800 7.0519 0.18 0.0306 0.2687 0.8545
No log 10.96 33 3.9684 0.18 0.8803 7.0277 0.18 0.0306 0.2656 0.8537
No log 11.96 36 3.9654 0.18 0.8805 7.0162 0.18 0.0306 0.2708 0.8536
No log 12.96 39 3.9633 0.18 0.8805 7.0056 0.18 0.0306 0.2619 0.8535
No log 13.96 42 3.9614 0.18 0.8804 6.9981 0.18 0.0306 0.2617 0.8532
No log 14.96 45 3.9598 0.18 0.8804 6.9923 0.18 0.0306 0.2669 0.8531
No log 15.96 48 3.9586 0.18 0.8803 6.9334 0.18 0.0306 0.2669 0.8529
No log 16.96 51 3.9578 0.18 0.8802 6.9237 0.18 0.0306 0.2716 0.8522
No log 17.96 54 3.9576 0.18 0.8802 6.8704 0.18 0.0306 0.2666 0.8521
No log 18.96 57 3.9574 0.18 0.8802 6.8662 0.18 0.0306 0.2664 0.8523
No log 19.96 60 3.9568 0.18 0.8801 6.8641 0.18 0.0306 0.2614 0.8518
No log 20.96 63 3.9566 0.18 0.8801 6.8634 0.18 0.0306 0.2659 0.8516
No log 21.96 66 3.9563 0.18 0.8800 6.8632 0.18 0.0306 0.2612 0.8516
No log 22.96 69 3.9561 0.18 0.8800 6.8620 0.18 0.0306 0.2612 0.8513
No log 23.96 72 3.9561 0.18 0.8800 6.8611 0.18 0.0306 0.2612 0.8513
No log 24.96 75 3.9560 0.18 0.8800 6.8606 0.18 0.0306 0.2612 0.8512

Framework versions

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