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CONTEXT_one

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CONTEXT_one / README.md
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metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CONTEXT_one
    results: []

CONTEXT_one

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2483
  • Precision: 0.8152
  • Recall: 0.8158
  • F1: 0.8141
  • Accuracy: 0.8158

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: 8
  • eval_batch_size: 8
  • seed: 42
  • 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 Precision Recall F1 Accuracy
1.3127 0.62 30 1.1497 0.6818 0.5263 0.4785 0.5263
0.8414 1.25 60 0.8096 0.7479 0.75 0.7472 0.75
0.597 1.88 90 0.6579 0.7904 0.7895 0.7873 0.7895
0.4417 2.5 120 0.5761 0.8072 0.8026 0.8026 0.8026
0.3041 3.12 150 0.6691 0.7665 0.7632 0.7598 0.7632
0.2384 3.75 180 0.6736 0.7717 0.7632 0.7645 0.7632
0.28 4.38 210 0.7949 0.7602 0.7632 0.7574 0.7632
0.22 5.0 240 0.8305 0.7917 0.7895 0.7879 0.7895
0.1427 5.62 270 0.7339 0.8041 0.8026 0.8025 0.8026
0.1875 6.25 300 0.7198 0.8031 0.7895 0.7909 0.7895
0.1216 6.88 330 0.7462 0.8315 0.8289 0.8287 0.8289
0.0895 7.5 360 0.8646 0.8070 0.8026 0.8006 0.8026
0.0758 8.12 390 1.0129 0.7883 0.7632 0.7642 0.7632
0.0636 8.75 420 0.9161 0.7893 0.7895 0.7866 0.7895
0.0239 9.38 450 0.9354 0.7409 0.7368 0.7367 0.7368
0.0449 10.0 480 1.0156 0.7994 0.8026 0.7980 0.8026
0.0089 10.62 510 0.9735 0.8125 0.8158 0.8125 0.8158
0.0348 11.25 540 1.0077 0.7867 0.7895 0.7848 0.7895
0.0037 11.88 570 1.1631 0.7868 0.7895 0.7857 0.7895
0.0022 12.5 600 1.1037 0.7998 0.8026 0.7993 0.8026
0.026 13.12 630 1.0309 0.8152 0.8158 0.8141 0.8158
0.0118 13.75 660 1.0360 0.8152 0.8158 0.8141 0.8158
0.0125 14.38 690 1.2095 0.7867 0.7895 0.7848 0.7895
0.0158 15.0 720 1.0658 0.8152 0.8158 0.8141 0.8158
0.0072 15.62 750 1.1267 0.7708 0.7763 0.7688 0.7763
0.0015 16.25 780 1.1247 0.8152 0.8158 0.8141 0.8158
0.0018 16.88 810 1.1386 0.8152 0.8158 0.8141 0.8158
0.0013 17.5 840 1.1468 0.8152 0.8158 0.8141 0.8158
0.0011 18.12 870 1.1692 0.8152 0.8158 0.8141 0.8158
0.0013 18.75 900 1.1734 0.8152 0.8158 0.8141 0.8158
0.0011 19.38 930 1.1857 0.8152 0.8158 0.8141 0.8158
0.001 20.0 960 1.1890 0.8152 0.8158 0.8141 0.8158
0.001 20.62 990 1.1924 0.8152 0.8158 0.8141 0.8158
0.0009 21.25 1020 1.2005 0.8152 0.8158 0.8141 0.8158
0.0009 21.88 1050 1.2084 0.8152 0.8158 0.8141 0.8158
0.0009 22.5 1080 1.2216 0.8152 0.8158 0.8141 0.8158
0.0009 23.12 1110 1.2237 0.8152 0.8158 0.8141 0.8158
0.0008 23.75 1140 1.2231 0.8152 0.8158 0.8141 0.8158
0.0008 24.38 1170 1.2286 0.8152 0.8158 0.8141 0.8158
0.0008 25.0 1200 1.2312 0.8152 0.8158 0.8141 0.8158
0.0008 25.62 1230 1.2325 0.8152 0.8158 0.8141 0.8158
0.0008 26.25 1260 1.2362 0.8152 0.8158 0.8141 0.8158
0.0008 26.88 1290 1.2415 0.8152 0.8158 0.8141 0.8158
0.0007 27.5 1320 1.2462 0.8152 0.8158 0.8141 0.8158
0.0008 28.12 1350 1.2471 0.8152 0.8158 0.8141 0.8158
0.0007 28.75 1380 1.2466 0.8152 0.8158 0.8141 0.8158
0.0007 29.38 1410 1.2478 0.8152 0.8158 0.8141 0.8158
0.0007 30.0 1440 1.2483 0.8152 0.8158 0.8141 0.8158

Framework versions

  • Transformers 4.37.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1