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scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only55

Text Classification
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deberta-v2
Generated from Trainer
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scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only55 / README.md
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metadata
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only55
    results: []

scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only55

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 6.8361
  • Accuracy: 0.3634
  • F1: 0.3600

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: 32
  • eval_batch_size: 32
  • seed: 55
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.0870 250 1.4035 0.3627 0.3384
0.9258 2.1739 500 1.8269 0.3688 0.3652
0.9258 3.2609 750 2.2003 0.3696 0.3691
0.3143 4.3478 1000 3.2084 0.3850 0.3842
0.3143 5.4348 1250 3.4181 0.3719 0.3668
0.1172 6.5217 1500 3.9886 0.3688 0.3622
0.1172 7.6087 1750 4.2183 0.3650 0.3626
0.0592 8.6957 2000 4.6155 0.3665 0.3545
0.0592 9.7826 2250 4.7510 0.3727 0.3685
0.0394 10.8696 2500 5.1707 0.3688 0.3628
0.0394 11.9565 2750 5.0827 0.3681 0.3636
0.0238 13.0435 3000 5.5056 0.3665 0.3535
0.0238 14.1304 3250 5.3337 0.3704 0.3661
0.0171 15.2174 3500 5.7582 0.3735 0.3709
0.0171 16.3043 3750 5.9369 0.3665 0.3598
0.011 17.3913 4000 6.0815 0.3765 0.3719
0.011 18.4783 4250 6.1316 0.3819 0.3802
0.0043 19.5652 4500 6.3789 0.3727 0.3705
0.0043 20.6522 4750 6.4273 0.3673 0.3664
0.0064 21.7391 5000 6.3039 0.3758 0.3743
0.0064 22.8261 5250 6.5675 0.3619 0.3540
0.0031 23.9130 5500 6.5657 0.3688 0.3650
0.0031 25.0 5750 6.6382 0.3696 0.3666
0.0016 26.0870 6000 6.7416 0.3681 0.3643
0.0016 27.1739 6250 6.7141 0.3711 0.3677
0.0006 28.2609 6500 6.7905 0.3642 0.3600
0.0006 29.3478 6750 6.8361 0.3634 0.3600

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.19.1