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case-analysis-roberta-base

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metadata
license: mit
tags:
  - generated_from_trainer
base_model: FacebookAI/roberta-base
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: case-analysis-roberta-base
    results: []

Metrics

  • loss: 1.6841
  • accuracy: 0.7884
  • precision: 0.8028
  • recall: 0.7884
  • precision_macro: 0.6408
  • recall_macro: 0.6436
  • macro_fpr: 0.0956
  • weighted_fpr: 0.0821
  • weighted_specificity: 0.8781
  • macro_specificity: 0.9166
  • weighted_sensitivity: 0.7884
  • macro_sensitivity: 0.6436
  • f1_micro: 0.7884
  • f1_macro: 0.6410
  • f1_weighted: 0.7953
  • runtime: 229.8279
  • samples_per_second: 1.9540
  • steps_per_second: 0.2480

case-analysis-roberta-base

This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6841
  • Accuracy: 0.7884
  • Precision: 0.8028
  • Recall: 0.7884
  • Precision Macro: 0.6320
  • Recall Macro: 0.6238
  • Macro Fpr: 0.0958
  • Weighted Fpr: 0.0781
  • Weighted Specificity: 0.8648
  • Macro Specificity: 0.9155
  • Weighted Sensitivity: 0.7973
  • Macro Sensitivity: 0.6238
  • F1 Micro: 0.7973
  • F1 Macro: 0.6277
  • F1 Weighted: 0.7968

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: 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
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall Precision Macro Recall Macro Macro Fpr Weighted Fpr Weighted Specificity Macro Specificity Weighted Sensitivity Macro Sensitivity F1 Micro F1 Macro F1 Weighted
No log 1.0 224 0.8771 0.7528 0.7120 0.7528 0.5404 0.5402 0.1314 0.0987 0.7806 0.8833 0.7528 0.5402 0.7528 0.5389 0.7301
No log 2.0 448 0.7936 0.7728 0.7420 0.7728 0.5529 0.5937 0.1080 0.0892 0.8458 0.9046 0.7728 0.5937 0.7728 0.5712 0.7555
0.8855 3.0 672 0.8127 0.7305 0.7321 0.7305 0.5336 0.5600 0.1288 0.1095 0.8209 0.8879 0.7305 0.5600 0.7305 0.5313 0.7208
0.8855 4.0 896 1.0186 0.7795 0.7503 0.7795 0.5722 0.5654 0.1184 0.0862 0.8004 0.8950 0.7795 0.5654 0.7795 0.5605 0.7561
0.5551 5.0 1120 0.7591 0.8085 0.7674 0.8085 0.5833 0.5963 0.0988 0.0732 0.8375 0.9115 0.8085 0.5963 0.8085 0.5892 0.7867
0.5551 6.0 1344 0.9522 0.8174 0.7816 0.8174 0.6117 0.5988 0.0967 0.0693 0.8297 0.9118 0.8174 0.5988 0.8174 0.6030 0.7967
0.386 7.0 1568 1.0569 0.7706 0.7610 0.7706 0.5710 0.5858 0.1089 0.0903 0.8522 0.9057 0.7706 0.5858 0.7706 0.5782 0.7656
0.386 8.0 1792 1.1957 0.7572 0.7918 0.7572 0.6175 0.6264 0.1052 0.0965 0.8905 0.9119 0.7572 0.6264 0.7572 0.6162 0.7715
0.2709 9.0 2016 1.2092 0.7728 0.7897 0.7728 0.6331 0.6301 0.1021 0.0892 0.8751 0.9120 0.7728 0.6301 0.7728 0.6264 0.7773
0.2709 10.0 2240 1.3830 0.7706 0.7782 0.7706 0.6112 0.6073 0.1094 0.0903 0.8464 0.9043 0.7706 0.6073 0.7706 0.6072 0.7728
0.2709 11.0 2464 1.4518 0.7817 0.7944 0.7817 0.6157 0.6059 0.1030 0.0851 0.8606 0.9106 0.7817 0.6059 0.7817 0.6077 0.7856
0.1837 12.0 2688 1.5283 0.7684 0.7840 0.7684 0.6143 0.6003 0.1058 0.0913 0.8701 0.9096 0.7684 0.6003 0.7684 0.6022 0.7726
0.1837 13.0 2912 1.5136 0.7817 0.7907 0.7817 0.6231 0.6472 0.0979 0.0851 0.8733 0.9137 0.7817 0.6472 0.7817 0.6332 0.7848
0.1212 14.0 3136 1.6569 0.7506 0.8138 0.7506 0.6380 0.6499 0.1039 0.0997 0.8911 0.9104 0.7506 0.6499 0.7506 0.6327 0.7764
0.1212 15.0 3360 1.5305 0.7661 0.7714 0.7661 0.5965 0.6203 0.1054 0.0923 0.8710 0.9093 0.7661 0.6203 0.7661 0.6068 0.7669
0.0793 16.0 3584 1.4931 0.7996 0.7896 0.7996 0.6016 0.6193 0.0947 0.0771 0.8625 0.9155 0.7996 0.6193 0.7996 0.6085 0.7933
0.0793 17.0 3808 1.4582 0.8018 0.7911 0.8018 0.6143 0.6131 0.0963 0.0761 0.8523 0.9135 0.8018 0.6131 0.8018 0.6132 0.7958
0.0473 18.0 4032 1.6772 0.7795 0.7924 0.7795 0.6154 0.6342 0.0990 0.0862 0.8742 0.9134 0.7795 0.6342 0.7795 0.6224 0.7843
0.0473 19.0 4256 1.5707 0.7929 0.7890 0.7929 0.6409 0.6339 0.0966 0.0801 0.8666 0.9149 0.7929 0.6339 0.7929 0.6348 0.7892
0.0473 20.0 4480 1.4891 0.8018 0.8136 0.8018 0.6441 0.6284 0.0916 0.0761 0.8768 0.9196 0.8018 0.6284 0.8018 0.6355 0.8073
0.0476 21.0 4704 1.5064 0.8062 0.8181 0.8062 0.6511 0.6320 0.0896 0.0742 0.8754 0.9204 0.8062 0.6320 0.8062 0.6407 0.8117
0.0476 22.0 4928 1.5076 0.8107 0.8003 0.8107 0.6296 0.6247 0.0913 0.0722 0.8642 0.9187 0.8107 0.6247 0.8107 0.6265 0.8050
0.0366 23.0 5152 1.5891 0.7973 0.8113 0.7973 0.6455 0.6382 0.0929 0.0781 0.8763 0.9184 0.7973 0.6382 0.7973 0.6407 0.8038
0.0366 24.0 5376 1.6779 0.7951 0.7990 0.7951 0.6306 0.5982 0.0994 0.0791 0.8581 0.9133 0.7951 0.5982 0.7951 0.6123 0.7956
0.0368 25.0 5600 1.6211 0.8040 0.8024 0.8040 0.6420 0.6223 0.0952 0.0751 0.8570 0.9152 0.8040 0.6223 0.8040 0.6313 0.8023
0.0368 26.0 5824 1.4841 0.8062 0.8060 0.8062 0.6364 0.6416 0.0894 0.0742 0.8775 0.9209 0.8062 0.6416 0.8062 0.6385 0.8058
0.0252 27.0 6048 1.6841 0.7884 0.8028 0.7884 0.6408 0.6436 0.0956 0.0821 0.8781 0.9166 0.7884 0.6436 0.7884 0.6410 0.7953
0.0252 28.0 6272 1.7185 0.7929 0.8006 0.7929 0.6386 0.6338 0.0954 0.0801 0.8725 0.9163 0.7929 0.6338 0.7929 0.6355 0.7964
0.0252 29.0 6496 1.6500 0.7996 0.7989 0.7996 0.6338 0.6276 0.0942 0.0771 0.8678 0.9168 0.7996 0.6276 0.7996 0.6306 0.7992
0.0147 30.0 6720 1.6506 0.7973 0.7965 0.7973 0.6320 0.6238 0.0958 0.0781 0.8648 0.9155 0.7973 0.6238 0.7973 0.6277 0.7968

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1