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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - sms_spam
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: bert-base-uncased-finetuned-smsspam
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: sms_spam
          type: sms_spam
          config: plain_text
          split: train
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9904420549581839
          - name: Precision
            type: precision
            value: 0.9814814814814815
          - name: Recall
            type: recall
            value: 0.9464285714285714
          - name: F1
            type: f1
            value: 0.9636363636363636

bert-base-uncased-finetuned-smsspam

This model is a fine-tuned version of bert-base-uncased on the sms_spam dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0637
  • Accuracy: 0.9904
  • Precision: 0.9815
  • Recall: 0.9464
  • F1: 0.9636

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: 4

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.0828 1.0 593 0.0538 0.9892 0.9725 0.9464 0.9593
0.0269 2.0 1186 0.1792 0.9677 0.8244 0.9643 0.8889
0.0229 3.0 1779 0.0623 0.9916 0.9817 0.9554 0.9683
0.0043 4.0 2372 0.0637 0.9904 0.9815 0.9464 0.9636

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3