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copilot_relex_v1

Text Classification
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TensorBoard
Safetensors
deberta-v2
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copilot_relex_v1

This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0134
  • Accuracy: 0.0038
  • F1: 0.0062
  • Precision: 0.0031
  • Recall: 0.625
  • Learning Rate: 0.0

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall Rate
No log 1.0 20 0.5469 0.0994 0.0093 0.0047 0.8438 0.0000
No log 2.0 40 0.3859 0.0050 0.0100 0.0050 1.0 0.0000
No log 3.0 60 0.2662 0.0050 0.0100 0.0050 1.0 0.0000
No log 4.0 80 0.1781 0.0050 0.0100 0.0050 1.0 0.0000
No log 5.0 100 0.1183 0.0050 0.0100 0.0050 1.0 0.0000
No log 6.0 120 0.0823 0.0050 0.0100 0.0050 1.0 0.0000
No log 7.0 140 0.0614 0.0050 0.0100 0.0050 1.0 0.0000
No log 8.0 160 0.0494 0.0050 0.0100 0.0050 1.0 0.0000
No log 9.0 180 0.0423 0.0050 0.0100 0.0050 1.0 0.0000
No log 10.0 200 0.0379 0.0050 0.0100 0.0050 1.0 0.0000
No log 11.0 220 0.0350 0.0050 0.0100 0.0050 1.0 0.0000
No log 12.0 240 0.0331 0.0050 0.0100 0.0050 1.0 0.0000
No log 13.0 260 0.0318 0.0050 0.0100 0.0050 1.0 0.0000
No log 14.0 280 0.0307 0.0050 0.0100 0.0050 1.0 0.0000
No log 15.0 300 0.0300 0.0050 0.0100 0.0050 1.0 0.0000
No log 16.0 320 0.0294 0.0050 0.0100 0.0050 1.0 0.0000
No log 17.0 340 0.0290 0.0050 0.0100 0.0050 1.0 0.0000
No log 18.0 360 0.0286 0.0050 0.0100 0.0050 1.0 0.0000
No log 19.0 380 0.0283 0.0050 0.0100 0.0050 1.0 0.0000
No log 20.0 400 0.0300 0.0050 0.0100 0.0050 1.0 0.0000
No log 21.0 420 0.0290 0.0050 0.0100 0.0050 1.0 0.0000
No log 22.0 440 0.0252 0.0050 0.0100 0.0050 1.0 0.0000
No log 23.0 460 0.0246 0.0050 0.0100 0.0050 1.0 0.0000
No log 24.0 480 0.0242 0.0050 0.0100 0.0050 1.0 0.0000
0.1127 25.0 500 0.0239 0.0050 0.0100 0.0050 1.0 0.0000
0.1127 26.0 520 0.0233 0.0050 0.0100 0.0050 1.0 0.0000
0.1127 27.0 540 0.0226 0.0050 0.0100 0.0050 1.0 0.0000
0.1127 28.0 560 0.0224 0.0050 0.0100 0.0050 1.0 0.0000
0.1127 29.0 580 0.0217 0.0050 0.0100 0.0050 1.0 0.0000
0.1127 30.0 600 0.0211 0.0047 0.0093 0.0047 0.9375 0.0000
0.1127 31.0 620 0.0206 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 32.0 640 0.0207 0.0047 0.0090 0.0045 0.9062 0.0000
0.1127 33.0 660 0.0198 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 34.0 680 0.0205 0.0047 0.0090 0.0045 0.9062 0.0000
0.1127 35.0 700 0.0193 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 36.0 720 0.0198 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 37.0 740 0.0190 0.0047 0.0090 0.0045 0.9062 0.0000
0.1127 38.0 760 0.0197 0.0049 0.0090 0.0045 0.9062 0.0000
0.1127 39.0 780 0.0185 0.0047 0.0090 0.0045 0.9062 0.0000
0.1127 40.0 800 0.0184 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 41.0 820 0.0188 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 42.0 840 0.0179 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 43.0 860 0.0178 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 44.0 880 0.0174 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 45.0 900 0.0182 0.0041 0.0081 0.0041 0.8125 0.0000
0.1127 46.0 920 0.0171 0.0045 0.0090 0.0045 0.9062 0.0000
0.1127 47.0 940 0.0168 0.0044 0.0087 0.0044 0.875 0.0000
0.1127 48.0 960 0.0167 0.0041 0.0081 0.0041 0.8125 0.0000
0.1127 49.0 980 0.0165 0.0039 0.0078 0.0039 0.7812 0.0000
0.0253 50.0 1000 0.0162 0.0039 0.0078 0.0039 0.7812 1e-05
0.0253 51.0 1020 0.0160 0.0041 0.0081 0.0041 0.8125 0.0000
0.0253 52.0 1040 0.0159 0.0038 0.0075 0.0038 0.75 0.0000
0.0253 53.0 1060 0.0158 0.0038 0.0075 0.0038 0.75 0.0000
0.0253 54.0 1080 0.0163 0.0041 0.0075 0.0038 0.75 0.0000
0.0253 55.0 1100 0.0160 0.0039 0.0072 0.0036 0.7188 9e-06
0.0253 56.0 1120 0.0161 0.0034 0.0069 0.0034 0.6875 0.0000
0.0253 57.0 1140 0.0156 0.0036 0.0069 0.0034 0.6875 0.0000
0.0253 58.0 1160 0.0154 0.0041 0.0069 0.0034 0.6875 0.0000
0.0253 59.0 1180 0.0155 0.0039 0.0072 0.0036 0.7188 0.0000
0.0253 60.0 1200 0.0155 0.0036 0.0069 0.0034 0.6875 0.0000
0.0253 61.0 1220 0.0154 0.0038 0.0069 0.0034 0.6875 0.0000
0.0253 62.0 1240 0.0156 0.0041 0.0069 0.0034 0.6875 0.0000
0.0253 63.0 1260 0.0152 0.0038 0.0069 0.0034 0.6875 0.0000
0.0253 64.0 1280 0.0146 0.0036 0.0069 0.0034 0.6875 0.0000
0.0253 65.0 1300 0.0147 0.0041 0.0069 0.0034 0.6875 7e-06
0.0253 66.0 1320 0.0149 0.0039 0.0066 0.0033 0.6562 0.0000
0.0253 67.0 1340 0.0148 0.0038 0.0062 0.0031 0.625 0.0000
0.0253 68.0 1360 0.0148 0.0039 0.0066 0.0033 0.6562 0.0000
0.0253 69.0 1380 0.0143 0.0041 0.0069 0.0034 0.6875 0.0000
0.0253 70.0 1400 0.0144 0.0039 0.0062 0.0031 0.625 6e-06
0.0253 71.0 1420 0.0145 0.0039 0.0066 0.0033 0.6562 0.0000
0.0253 72.0 1440 0.0141 0.0038 0.0066 0.0033 0.6562 0.0000
0.0253 73.0 1460 0.0144 0.0039 0.0066 0.0033 0.6562 0.0000
0.0253 74.0 1480 0.0144 0.0039 0.0066 0.0033 0.6562 0.0000
0.019 75.0 1500 0.0142 0.0036 0.0062 0.0031 0.625 5e-06
0.019 76.0 1520 0.0140 0.0041 0.0066 0.0033 0.6562 0.0000
0.019 77.0 1540 0.0139 0.0039 0.0066 0.0033 0.6562 0.0000
0.019 78.0 1560 0.0140 0.0039 0.0066 0.0033 0.6562 0.0000
0.019 79.0 1580 0.0139 0.0038 0.0059 0.0030 0.5938 0.0000
0.019 80.0 1600 0.0139 0.0039 0.0066 0.0033 0.6562 0.0000
0.019 81.0 1620 0.0139 0.0042 0.0066 0.0033 0.6562 0.0000
0.019 82.0 1640 0.0136 0.0036 0.0062 0.0031 0.625 0.0000
0.019 83.0 1660 0.0138 0.0041 0.0062 0.0031 0.625 0.0000
0.019 84.0 1680 0.0136 0.0039 0.0059 0.0030 0.5938 0.0000
0.019 85.0 1700 0.0136 0.0038 0.0059 0.0030 0.5938 3e-06
0.019 86.0 1720 0.0136 0.0038 0.0059 0.0030 0.5938 0.0000
0.019 87.0 1740 0.0133 0.0038 0.0062 0.0031 0.625 0.0000
0.019 88.0 1760 0.0137 0.0039 0.0059 0.0030 0.5938 0.0000
0.019 89.0 1780 0.0134 0.0036 0.0059 0.0030 0.5938 0.0000
0.019 90.0 1800 0.0133 0.0038 0.0059 0.0030 0.5938 0.0000
0.019 91.0 1820 0.0137 0.0041 0.0066 0.0033 0.6562 0.0000
0.019 92.0 1840 0.0134 0.0038 0.0059 0.0030 0.5938 0.0000
0.019 93.0 1860 0.0135 0.0038 0.0059 0.0030 0.5938 0.0000
0.019 94.0 1880 0.0134 0.0038 0.0062 0.0031 0.625 0.0000
0.019 95.0 1900 0.0136 0.0039 0.0059 0.0030 0.5938 0.0000
0.019 96.0 1920 0.0135 0.0039 0.0059 0.0030 0.5938 0.0000
0.019 97.0 1940 0.0134 0.0038 0.0062 0.0031 0.625 0.0000
0.019 98.0 1960 0.0134 0.0038 0.0062 0.0031 0.625 0.0000
0.019 99.0 1980 0.0134 0.0038 0.0062 0.0031 0.625 0.0000
0.0156 100.0 2000 0.0134 0.0038 0.0062 0.0031 0.625 0.0

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1
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Evaluation results

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