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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - filter_v2
metrics:
  - f1
  - accuracy
base_model: bert-base-cased
model-index:
  - name: favs_filter_classification_v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: filter_v2
          type: filter_v2
          config: default
          split: train
          args: default
        metrics:
          - type: f1
            value: 0.9761904761904762
            name: F1
          - type: accuracy
            value: 0.9545454545454546
            name: Accuracy

favs_filter_classification_v2

This model is a fine-tuned version of bert-base-cased on the filter_v2 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2016
  • F1: 0.9762
  • Roc Auc: 0.9844
  • Accuracy: 0.9545

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: 1.5e-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: 20

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
0.6596 1.0 16 0.6086 0.2687 0.5474 0.0
0.5448 2.0 32 0.5354 0.3824 0.6063 0.0
0.5106 3.0 48 0.4874 0.4444 0.6382 0.0455
0.4353 4.0 64 0.4301 0.5352 0.6889 0.1818
0.3699 5.0 80 0.3890 0.6579 0.7640 0.3636
0.349 6.0 96 0.3663 0.6667 0.7633 0.3182
0.3104 7.0 112 0.3327 0.7105 0.7953 0.4545
0.3023 8.0 128 0.2971 0.7733 0.8303 0.5455
0.2676 9.0 144 0.2766 0.8395 0.8861 0.7727
0.2374 10.0 160 0.2541 0.8537 0.8980 0.7727
0.2238 11.0 176 0.2399 0.9024 0.9293 0.8182
0.2084 12.0 192 0.2221 0.9286 0.9531 0.8636
0.2143 13.0 208 0.2138 0.9286 0.9531 0.8636
0.1846 14.0 224 0.2016 0.9762 0.9844 0.9545
0.1812 15.0 240 0.1957 0.9762 0.9844 0.9545
0.1756 16.0 256 0.1881 0.9647 0.9806 0.9091
0.1662 17.0 272 0.1845 0.9762 0.9844 0.9545
0.1715 18.0 288 0.1802 0.9762 0.9844 0.9545
0.1585 19.0 304 0.1782 0.9762 0.9844 0.9545
0.1595 20.0 320 0.1775 0.9762 0.9844 0.9545

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.1
  • Datasets 2.4.0
  • Tokenizers 0.12.1