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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
base_model: bert-base-uncased
model-index:
  - name: bert-base-uncased-finetuned-detests
    results: []

bert-base-uncased-finetuned-detests

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

  • Loss: 1.5026
  • Accuracy: 0.7856
  • F1-score: 0.7175
  • Precision: 0.7058
  • Recall: 0.7369
  • Auc: 0.7369

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1-score Precision Recall Auc
0.271 1.0 174 0.4648 0.7954 0.7005 0.7070 0.6950 0.6950
0.2246 2.0 348 0.5229 0.7987 0.7053 0.7119 0.6997 0.6997
0.3814 3.0 522 0.7043 0.7676 0.7018 0.6896 0.7278 0.7278
0.1343 4.0 696 0.8843 0.7938 0.7217 0.7124 0.7346 0.7346
0.0063 5.0 870 1.0890 0.7807 0.7040 0.6955 0.7159 0.7159
0.063 6.0 1044 1.1208 0.8101 0.7378 0.7316 0.7452 0.7452
0.0022 7.0 1218 1.1989 0.8249 0.7318 0.7543 0.7166 0.7166
0.0356 8.0 1392 1.5295 0.7758 0.7151 0.7016 0.7457 0.7457
0.0002 9.0 1566 1.4269 0.8003 0.7202 0.7171 0.7236 0.7236
0.0004 10.0 1740 1.5026 0.7856 0.7175 0.7058 0.7369 0.7369

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3