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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
base_model: roberta-base
model-index:
  - name: run-4
    results: []

run-4

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

  • Loss: 2.6296
  • Accuracy: 0.685
  • Precision: 0.6248
  • Recall: 0.6164
  • F1: 0.6188

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: 32
  • eval_batch_size: 32
  • 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 Accuracy Precision Recall F1
1.0195 1.0 50 0.8393 0.615 0.4126 0.5619 0.4606
0.7594 2.0 100 0.7077 0.7 0.6896 0.6663 0.6178
0.5515 3.0 150 0.9342 0.68 0.6334 0.5989 0.6016
0.3739 4.0 200 0.7755 0.735 0.7032 0.7164 0.7063
0.2648 5.0 250 0.9200 0.7 0.6584 0.6677 0.6611
0.1726 6.0 300 1.1898 0.71 0.6653 0.6550 0.6570
0.1452 7.0 350 1.5086 0.73 0.6884 0.6768 0.6812
0.0856 8.0 400 2.6159 0.68 0.6754 0.5863 0.5951
0.1329 9.0 450 1.9491 0.71 0.6692 0.6442 0.6463
0.0322 10.0 500 1.7897 0.74 0.6977 0.6939 0.6946
0.0345 11.0 550 1.9100 0.725 0.6827 0.6853 0.6781
0.026 12.0 600 2.5041 0.68 0.6246 0.6115 0.6137
0.0084 13.0 650 2.5343 0.715 0.6708 0.6617 0.6637
0.0145 14.0 700 2.4112 0.715 0.6643 0.6595 0.6614
0.0119 15.0 750 2.5303 0.705 0.6479 0.6359 0.6390
0.0026 16.0 800 2.6299 0.705 0.6552 0.6447 0.6455
0.0077 17.0 850 2.4044 0.715 0.6667 0.6576 0.6596
0.0055 18.0 900 2.8077 0.68 0.6208 0.6065 0.6098
0.0078 19.0 950 2.5608 0.68 0.6200 0.6104 0.6129
0.0018 20.0 1000 2.6296 0.685 0.6248 0.6164 0.6188

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu116
  • Tokenizers 0.13.2