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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: 0.2715
  • Precision: 0.8333
  • Recall: 0.8333
  • F1: 0.8333
  • Accuracy: 0.9483

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 7 0.6895 0.0 0.0 0.0 0.7241
No log 2.0 14 0.5915 0.0 0.0 0.0 0.7241
No log 3.0 21 0.4062 0.2 0.0833 0.1176 0.7759
No log 4.0 28 0.3063 0.5 0.5833 0.5385 0.8966
No log 5.0 35 0.2520 0.5333 0.6667 0.5926 0.9138
No log 6.0 42 0.2474 0.6667 0.6667 0.6667 0.9310
No log 7.0 49 0.2140 0.6923 0.75 0.7200 0.9483
No log 8.0 56 0.1894 0.8333 0.8333 0.8333 0.9655
No log 9.0 63 0.1890 0.8333 0.8333 0.8333 0.9655
No log 10.0 70 0.2119 0.8182 0.75 0.7826 0.9483
No log 11.0 77 0.2343 0.8182 0.75 0.7826 0.9483
No log 12.0 84 0.2421 0.8182 0.75 0.7826 0.9483
No log 13.0 91 0.2379 0.8182 0.75 0.7826 0.9483
No log 14.0 98 0.2362 0.8182 0.75 0.7826 0.9483
No log 15.0 105 0.2357 0.8182 0.75 0.7826 0.9483
No log 16.0 112 0.2370 0.8182 0.75 0.7826 0.9483
No log 17.0 119 0.2383 0.8182 0.75 0.7826 0.9483
No log 18.0 126 0.2400 0.8182 0.75 0.7826 0.9483
No log 19.0 133 0.2424 0.8182 0.75 0.7826 0.9483
No log 20.0 140 0.2444 0.8182 0.75 0.7826 0.9483
No log 21.0 147 0.2461 0.8333 0.8333 0.8333 0.9655
No log 22.0 154 0.2481 0.8333 0.8333 0.8333 0.9655
No log 23.0 161 0.2422 0.8333 0.8333 0.8333 0.9655
No log 24.0 168 0.2408 0.8333 0.8333 0.8333 0.9655
No log 25.0 175 0.2418 0.8333 0.8333 0.8333 0.9655
No log 26.0 182 0.2444 0.8333 0.8333 0.8333 0.9655
No log 27.0 189 0.2477 0.8333 0.8333 0.8333 0.9483
No log 28.0 196 0.2504 0.8333 0.8333 0.8333 0.9483
No log 29.0 203 0.2527 0.8333 0.8333 0.8333 0.9483
No log 30.0 210 0.2545 0.8333 0.8333 0.8333 0.9483
No log 31.0 217 0.2561 0.8333 0.8333 0.8333 0.9483
No log 32.0 224 0.2572 0.8333 0.8333 0.8333 0.9483
No log 33.0 231 0.2584 0.8333 0.8333 0.8333 0.9483
No log 34.0 238 0.2596 0.8333 0.8333 0.8333 0.9483
No log 35.0 245 0.2606 0.8333 0.8333 0.8333 0.9483
No log 36.0 252 0.2613 0.8333 0.8333 0.8333 0.9483
No log 37.0 259 0.2621 0.8333 0.8333 0.8333 0.9483
No log 38.0 266 0.2629 0.8333 0.8333 0.8333 0.9483
No log 39.0 273 0.2638 0.8333 0.8333 0.8333 0.9483
No log 40.0 280 0.2645 0.8333 0.8333 0.8333 0.9483
No log 41.0 287 0.2652 0.8333 0.8333 0.8333 0.9483
No log 42.0 294 0.2659 0.8333 0.8333 0.8333 0.9483
No log 43.0 301 0.2666 0.8333 0.8333 0.8333 0.9483
No log 44.0 308 0.2672 0.8333 0.8333 0.8333 0.9483
No log 45.0 315 0.2678 0.8333 0.8333 0.8333 0.9483
No log 46.0 322 0.2683 0.8333 0.8333 0.8333 0.9483
No log 47.0 329 0.2686 0.8333 0.8333 0.8333 0.9483
No log 48.0 336 0.2689 0.8333 0.8333 0.8333 0.9483
No log 49.0 343 0.2693 0.8333 0.8333 0.8333 0.9483
No log 50.0 350 0.2697 0.8333 0.8333 0.8333 0.9483
No log 51.0 357 0.2699 0.8333 0.8333 0.8333 0.9483
No log 52.0 364 0.2702 0.8333 0.8333 0.8333 0.9483
No log 53.0 371 0.2705 0.8333 0.8333 0.8333 0.9483
No log 54.0 378 0.2708 0.8333 0.8333 0.8333 0.9483
No log 55.0 385 0.2710 0.8333 0.8333 0.8333 0.9483
No log 56.0 392 0.2711 0.8333 0.8333 0.8333 0.9483
No log 57.0 399 0.2713 0.8333 0.8333 0.8333 0.9483
No log 58.0 406 0.2714 0.8333 0.8333 0.8333 0.9483
No log 59.0 413 0.2715 0.8333 0.8333 0.8333 0.9483
No log 60.0 420 0.2715 0.8333 0.8333 0.8333 0.9483

Framework versions

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