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scenario-TCR_data-en-massive_all_1_1

Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
Eval Results
Inference Endpoints
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scenario-TCR_data-en-massive_all_1_1 / 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-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: 0.7256830917315278
          - name: F1
            type: f1
            value: 0.6761346748529903

scenario-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: 2.6335
  • Accuracy: 0.7257
  • F1: 0.6761

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: 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 2.9382 0.2614 0.0710
No log 0.56 200 1.9636 0.5368 0.2848
No log 0.83 300 1.7094 0.5934 0.3887
No log 1.11 400 1.5733 0.6305 0.4633
1.8822 1.39 500 1.4046 0.6635 0.5200
1.8822 1.67 600 1.4016 0.6794 0.5558
1.8822 1.94 700 1.4019 0.6775 0.5858
1.8822 2.22 800 1.3179 0.7026 0.6044
1.8822 2.5 900 1.3087 0.7145 0.6295
0.576 2.78 1000 1.4452 0.6947 0.6119
0.576 3.06 1100 1.5017 0.6958 0.6297
0.576 3.33 1200 1.3701 0.7107 0.6439
0.576 3.61 1300 1.4868 0.7064 0.6435
0.576 3.89 1400 1.3839 0.7175 0.6397
0.3185 4.17 1500 1.5691 0.7013 0.6411
0.3185 4.44 1600 1.5106 0.7084 0.6481
0.3185 4.72 1700 1.6129 0.6979 0.6499
0.3185 5.0 1800 1.5121 0.7142 0.6551
0.3185 5.28 1900 1.6968 0.7039 0.6432
0.1966 5.56 2000 1.7057 0.7012 0.6333
0.1966 5.83 2100 1.6411 0.7165 0.6564
0.1966 6.11 2200 1.5510 0.7274 0.6709
0.1966 6.39 2300 1.7691 0.7172 0.6623
0.1966 6.67 2400 1.7955 0.7152 0.6529
0.156 6.94 2500 1.9122 0.7018 0.6548
0.156 7.22 2600 1.7143 0.7242 0.6694
0.156 7.5 2700 1.9184 0.7071 0.6528
0.156 7.78 2800 1.9581 0.7086 0.6454
0.156 8.06 2900 1.7750 0.7203 0.6643
0.0983 8.33 3000 1.9790 0.7136 0.6658
0.0983 8.61 3100 1.9127 0.7101 0.6499
0.0983 8.89 3200 2.0017 0.7121 0.6501
0.0983 9.17 3300 1.9420 0.7216 0.6650
0.0983 9.44 3400 2.0679 0.7082 0.6517
0.0767 9.72 3500 2.1093 0.7046 0.6458
0.0767 10.0 3600 2.1402 0.7126 0.6600
0.0767 10.28 3700 2.0547 0.7157 0.6578
0.0767 10.56 3800 2.1029 0.7180 0.6624
0.0767 10.83 3900 2.2774 0.7075 0.6501
0.0532 11.11 4000 2.2711 0.7005 0.6460
0.0532 11.39 4100 2.2347 0.7038 0.6500
0.0532 11.67 4200 2.3489 0.6997 0.6462
0.0532 11.94 4300 2.3262 0.7092 0.6539
0.0532 12.22 4400 2.4171 0.6990 0.6523
0.0378 12.5 4500 2.2400 0.7145 0.6600
0.0378 12.78 4600 2.2622 0.7107 0.6518
0.0378 13.06 4700 2.2886 0.6952 0.6397
0.0378 13.33 4800 2.2268 0.7128 0.6570
0.0378 13.61 4900 2.3858 0.7022 0.6453
0.0307 13.89 5000 2.2298 0.7171 0.6609
0.0307 14.17 5100 2.3298 0.7183 0.6599
0.0307 14.44 5200 2.3642 0.7117 0.6502
0.0307 14.72 5300 2.4279 0.7179 0.6681
0.0307 15.0 5400 2.5524 0.6995 0.6481
0.0264 15.28 5500 2.4293 0.7121 0.6596
0.0264 15.56 5600 2.3810 0.7163 0.6583
0.0264 15.83 5700 2.2901 0.7317 0.6745
0.0264 16.11 5800 2.3646 0.7250 0.6696
0.0264 16.39 5900 2.3795 0.7233 0.6718
0.019 16.67 6000 2.5199 0.7153 0.6647
0.019 16.94 6100 2.4350 0.7222 0.6719
0.019 17.22 6200 2.4837 0.7180 0.6702
0.019 17.5 6300 2.4684 0.7230 0.6756
0.019 17.78 6400 2.4124 0.7241 0.6743
0.0144 18.06 6500 2.5430 0.7170 0.6709
0.0144 18.33 6600 2.5298 0.7104 0.6599
0.0144 18.61 6700 2.4784 0.7217 0.6716
0.0144 18.89 6800 2.5899 0.7101 0.6703
0.0144 19.17 6900 2.4036 0.7317 0.6815
0.0127 19.44 7000 2.5389 0.7188 0.6696
0.0127 19.72 7100 2.4397 0.7263 0.6767
0.0127 20.0 7200 2.3838 0.7264 0.6734
0.0127 20.28 7300 2.4933 0.7222 0.6763
0.0127 20.56 7400 2.4831 0.7291 0.6773
0.0077 20.83 7500 2.4833 0.7255 0.6747
0.0077 21.11 7600 2.5969 0.7188 0.6728
0.0077 21.39 7700 2.5866 0.7180 0.6739
0.0077 21.67 7800 2.5581 0.7255 0.6799
0.0077 21.94 7900 2.5420 0.7266 0.6764
0.0052 22.22 8000 2.6534 0.7184 0.6670
0.0052 22.5 8100 2.5060 0.7286 0.6797
0.0052 22.78 8200 2.5219 0.7283 0.6823
0.0052 23.06 8300 2.5787 0.7220 0.6804
0.0052 23.33 8400 2.6081 0.7228 0.6784
0.0047 23.61 8500 2.5537 0.7271 0.6786
0.0047 23.89 8600 2.6520 0.7229 0.6776
0.0047 24.17 8700 2.6277 0.7261 0.6791
0.0047 24.44 8800 2.6475 0.7231 0.6759
0.0047 24.72 8900 2.6349 0.7232 0.6754
0.0031 25.0 9000 2.5821 0.7256 0.6747
0.0031 25.28 9100 2.6122 0.7241 0.6744
0.0031 25.56 9200 2.6335 0.7223 0.6727
0.0031 25.83 9300 2.6440 0.7237 0.6736
0.0031 26.11 9400 2.6027 0.7257 0.6746
0.0017 26.39 9500 2.6251 0.7240 0.6735
0.0017 26.67 9600 2.7213 0.7177 0.6711
0.0017 26.94 9700 2.7145 0.7190 0.6712
0.0017 27.22 9800 2.6901 0.7208 0.6722
0.0017 27.5 9900 2.6853 0.7207 0.6724
0.0015 27.78 10000 2.6557 0.7223 0.6731
0.0015 28.06 10100 2.6671 0.7224 0.6728
0.0015 28.33 10200 2.6418 0.7236 0.6744
0.0015 28.61 10300 2.6298 0.7255 0.6755
0.0015 28.89 10400 2.6226 0.7265 0.6775
0.0008 29.17 10500 2.6252 0.7267 0.6773
0.0008 29.44 10600 2.6322 0.7262 0.6766
0.0008 29.72 10700 2.6345 0.7255 0.6761
0.0008 30.0 10800 2.6335 0.7257 0.6761

Framework versions

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