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scenario-MDBT-TCR-MSV-EN

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deberta-v2
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Eval Results
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scenario-MDBT-TCR-MSV-EN / README.md
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metadata
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
  - generated_from_trainer
datasets:
  - massive
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-MDBT-TCR_data-en-massive_all_1_1
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: massive
          type: massive
          config: all_1.1
          split: validation
          args: all_1.1
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7360994569987366
          - name: F1
            type: f1
            value: 0.688120673898054

scenario-MDBT-TCR_data-en-massive_all_1_1

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the massive dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5243
  • Accuracy: 0.7361
  • F1: 0.6881

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: 64
  • seed: 66
  • 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 0.28 100 2.9524 0.3055 0.0759
No log 0.56 200 2.0666 0.4969 0.2470
No log 0.83 300 1.6852 0.5878 0.3626
No log 1.11 400 1.4751 0.6376 0.4564
1.9292 1.39 500 1.4633 0.6522 0.4887
1.9292 1.67 600 1.4477 0.6604 0.5016
1.9292 1.94 700 1.3844 0.6758 0.5746
1.9292 2.22 800 1.3272 0.6937 0.5857
1.9292 2.5 900 1.2445 0.7178 0.6192
0.6258 2.78 1000 1.3348 0.7128 0.6198
0.6258 3.06 1100 1.5354 0.6734 0.6006
0.6258 3.33 1200 1.4149 0.7001 0.6258
0.6258 3.61 1300 1.4474 0.7032 0.6405
0.6258 3.89 1400 1.5031 0.7054 0.6395
0.3592 4.17 1500 1.3733 0.7225 0.6669
0.3592 4.44 1600 1.3757 0.7317 0.6555
0.3592 4.72 1700 1.4694 0.7134 0.6539
0.3592 5.0 1800 1.4733 0.7077 0.6461
0.3592 5.28 1900 1.5077 0.7232 0.6654
0.2316 5.56 2000 1.6396 0.7069 0.6482
0.2316 5.83 2100 1.5588 0.7178 0.6599
0.2316 6.11 2200 1.5611 0.7206 0.6507
0.2316 6.39 2300 1.7029 0.7155 0.6597
0.2316 6.67 2400 1.7865 0.7048 0.6407
0.1763 6.94 2500 1.7791 0.7065 0.6555
0.1763 7.22 2600 1.8013 0.7176 0.6572
0.1763 7.5 2700 1.8034 0.7149 0.6578
0.1763 7.78 2800 2.0082 0.6872 0.6234
0.1763 8.06 2900 2.0108 0.7011 0.6388
0.1136 8.33 3000 2.0779 0.6997 0.6513
0.1136 8.61 3100 1.9805 0.7122 0.6555
0.1136 8.89 3200 2.1014 0.7010 0.6485
0.1136 9.17 3300 1.9710 0.7133 0.6537
0.1136 9.44 3400 1.9677 0.7152 0.6564
0.0964 9.72 3500 2.0902 0.7079 0.6535
0.0964 10.0 3600 2.0776 0.7083 0.6529
0.0964 10.28 3700 2.0649 0.7191 0.6647
0.0964 10.56 3800 2.0690 0.7152 0.6551
0.0964 10.83 3900 2.1585 0.7055 0.6513
0.0721 11.11 4000 2.0158 0.7236 0.6660
0.0721 11.39 4100 2.1559 0.7120 0.6616
0.0721 11.67 4200 2.0517 0.7253 0.6694
0.0721 11.94 4300 2.1721 0.7219 0.6662
0.0721 12.22 4400 2.2949 0.7079 0.6680
0.0448 12.5 4500 2.1676 0.7186 0.6685
0.0448 12.78 4600 2.0882 0.7227 0.6636
0.0448 13.06 4700 2.0149 0.7335 0.6736
0.0448 13.33 4800 2.2128 0.7243 0.6667
0.0448 13.61 4900 2.2664 0.7200 0.6577
0.0371 13.89 5000 2.3489 0.7100 0.6656
0.0371 14.17 5100 2.3454 0.7087 0.6531
0.0371 14.44 5200 2.2062 0.7296 0.6767
0.0371 14.72 5300 2.4544 0.7101 0.6661
0.0371 15.0 5400 2.2581 0.7275 0.6683
0.0227 15.28 5500 2.2904 0.7242 0.6697
0.0227 15.56 5600 2.3484 0.7152 0.6495
0.0227 15.83 5700 2.4505 0.7126 0.6599
0.0227 16.11 5800 2.2985 0.7236 0.6673
0.0227 16.39 5900 2.3929 0.7245 0.6751
0.022 16.67 6000 2.4606 0.7200 0.6643
0.022 16.94 6100 2.3481 0.7276 0.6689
0.022 17.22 6200 2.3302 0.7273 0.6724
0.022 17.5 6300 2.3566 0.7292 0.6787
0.022 17.78 6400 2.3972 0.7281 0.6785
0.0133 18.06 6500 2.5105 0.7205 0.6705
0.0133 18.33 6600 2.3785 0.7295 0.6775
0.0133 18.61 6700 2.4367 0.7220 0.6676
0.0133 18.89 6800 2.4496 0.7255 0.6690
0.0133 19.17 6900 2.4133 0.7279 0.6720
0.0097 19.44 7000 2.5588 0.7140 0.6652
0.0097 19.72 7100 2.4906 0.7210 0.6656
0.0097 20.0 7200 2.5187 0.7199 0.6619
0.0097 20.28 7300 2.4627 0.7254 0.6686
0.0097 20.56 7400 2.5543 0.7187 0.6615
0.0096 20.83 7500 2.4262 0.7259 0.6676
0.0096 21.11 7600 2.4768 0.7256 0.6699
0.0096 21.39 7700 2.5336 0.7220 0.6724
0.0096 21.67 7800 2.5221 0.7240 0.6703
0.0096 21.94 7900 2.5008 0.7269 0.6712
0.0086 22.22 8000 2.4998 0.7278 0.6703
0.0086 22.5 8100 2.4611 0.7319 0.6842
0.0086 22.78 8200 2.5119 0.7313 0.6832
0.0086 23.06 8300 2.4329 0.7300 0.6764
0.0086 23.33 8400 2.4080 0.7317 0.6822
0.007 23.61 8500 2.4054 0.7313 0.6802
0.007 23.89 8600 2.4345 0.7334 0.6851
0.007 24.17 8700 2.4735 0.7326 0.6865
0.007 24.44 8800 2.4718 0.7313 0.6843
0.007 24.72 8900 2.4391 0.7328 0.6818
0.0029 25.0 9000 2.5152 0.7290 0.6869
0.0029 25.28 9100 2.4609 0.7365 0.6908
0.0029 25.56 9200 2.4717 0.7359 0.6932
0.0029 25.83 9300 2.5283 0.7337 0.6881
0.0029 26.11 9400 2.4831 0.7342 0.6866
0.0026 26.39 9500 2.5291 0.7325 0.6861
0.0026 26.67 9600 2.5201 0.7344 0.6855
0.0026 26.94 9700 2.5496 0.7322 0.6857
0.0026 27.22 9800 2.5302 0.7332 0.6853
0.0026 27.5 9900 2.5388 0.7329 0.6871
0.0025 27.78 10000 2.5210 0.7326 0.6845
0.0025 28.06 10100 2.5482 0.7319 0.6841
0.0025 28.33 10200 2.5628 0.7315 0.6853
0.0025 28.61 10300 2.5439 0.7341 0.6870
0.0025 28.89 10400 2.5241 0.7356 0.6875
0.001 29.17 10500 2.5238 0.7354 0.6873
0.001 29.44 10600 2.5186 0.7362 0.6880
0.001 29.72 10700 2.5237 0.7360 0.6880
0.001 30.0 10800 2.5243 0.7361 0.6881

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3