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distilbert_finetune_own_data_model

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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert_finetune_own_data_model
    results: []

distilbert_finetune_own_data_model

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

  • Loss: 0.0047
  • Precision: 1.0
  • Recall: 1.0
  • F1: 1.0
  • Accuracy: 1.0

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: 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: 60

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 6 0.6465 0.0 0.0 0.0 0.7429
No log 2.0 12 0.5074 0.0 0.0 0.0 0.7429
No log 3.0 18 0.3464 0.6 0.375 0.4615 0.8571
No log 4.0 24 0.2325 0.6667 0.5 0.5714 0.8857
No log 5.0 30 0.1652 0.75 0.75 0.75 0.9429
No log 6.0 36 0.1230 0.7778 0.875 0.8235 0.9714
No log 7.0 42 0.0933 0.7778 0.875 0.8235 0.9714
No log 8.0 48 0.0789 0.7778 0.875 0.8235 0.9714
No log 9.0 54 0.0681 0.7778 0.875 0.8235 0.9714
No log 10.0 60 0.0519 1.0 1.0 1.0 1.0
No log 11.0 66 0.0395 1.0 1.0 1.0 1.0
No log 12.0 72 0.0309 1.0 1.0 1.0 1.0
No log 13.0 78 0.0250 1.0 1.0 1.0 1.0
No log 14.0 84 0.0208 1.0 1.0 1.0 1.0
No log 15.0 90 0.0179 1.0 1.0 1.0 1.0
No log 16.0 96 0.0154 1.0 1.0 1.0 1.0
No log 17.0 102 0.0136 1.0 1.0 1.0 1.0
No log 18.0 108 0.0123 1.0 1.0 1.0 1.0
No log 19.0 114 0.0115 1.0 1.0 1.0 1.0
No log 20.0 120 0.0107 1.0 1.0 1.0 1.0
No log 21.0 126 0.0100 1.0 1.0 1.0 1.0
No log 22.0 132 0.0095 1.0 1.0 1.0 1.0
No log 23.0 138 0.0091 1.0 1.0 1.0 1.0
No log 24.0 144 0.0086 1.0 1.0 1.0 1.0
No log 25.0 150 0.0082 1.0 1.0 1.0 1.0
No log 26.0 156 0.0079 1.0 1.0 1.0 1.0
No log 27.0 162 0.0076 1.0 1.0 1.0 1.0
No log 28.0 168 0.0074 1.0 1.0 1.0 1.0
No log 29.0 174 0.0077 1.0 1.0 1.0 1.0
No log 30.0 180 0.0080 1.0 1.0 1.0 1.0
No log 31.0 186 0.0081 1.0 1.0 1.0 1.0
No log 32.0 192 0.0077 1.0 1.0 1.0 1.0
No log 33.0 198 0.0067 1.0 1.0 1.0 1.0
No log 34.0 204 0.0062 1.0 1.0 1.0 1.0
No log 35.0 210 0.0057 1.0 1.0 1.0 1.0
No log 36.0 216 0.0055 1.0 1.0 1.0 1.0
No log 37.0 222 0.0054 1.0 1.0 1.0 1.0
No log 38.0 228 0.0063 1.0 1.0 1.0 1.0
No log 39.0 234 0.0070 1.0 1.0 1.0 1.0
No log 40.0 240 0.0070 1.0 1.0 1.0 1.0
No log 41.0 246 0.0069 1.0 1.0 1.0 1.0
No log 42.0 252 0.0067 1.0 1.0 1.0 1.0
No log 43.0 258 0.0065 1.0 1.0 1.0 1.0
No log 44.0 264 0.0062 1.0 1.0 1.0 1.0
No log 45.0 270 0.0060 1.0 1.0 1.0 1.0
No log 46.0 276 0.0058 1.0 1.0 1.0 1.0
No log 47.0 282 0.0057 1.0 1.0 1.0 1.0
No log 48.0 288 0.0056 1.0 1.0 1.0 1.0
No log 49.0 294 0.0055 1.0 1.0 1.0 1.0
No log 50.0 300 0.0054 1.0 1.0 1.0 1.0
No log 51.0 306 0.0051 1.0 1.0 1.0 1.0
No log 52.0 312 0.0050 1.0 1.0 1.0 1.0
No log 53.0 318 0.0049 1.0 1.0 1.0 1.0
No log 54.0 324 0.0048 1.0 1.0 1.0 1.0
No log 55.0 330 0.0048 1.0 1.0 1.0 1.0
No log 56.0 336 0.0047 1.0 1.0 1.0 1.0
No log 57.0 342 0.0047 1.0 1.0 1.0 1.0
No log 58.0 348 0.0047 1.0 1.0 1.0 1.0
No log 59.0 354 0.0047 1.0 1.0 1.0 1.0
No log 60.0 360 0.0047 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2