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alimazhar-110
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website_classification

Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
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website_classification / README.md
Ali Mazhar Luqmani
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: website_classification
    results: []

website_classification

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

  • Loss: 0.2292
  • Accuracy: 0.9504
  • F1: 0.9489
  • Precision: 0.9510
  • Recall: 0.9504

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
2.404 1.0 71 1.7840 0.8865 0.8776 0.8785 0.8865
1.295 2.0 142 0.8539 0.8972 0.8871 0.8803 0.8972
0.6186 3.0 213 0.4818 0.9326 0.9263 0.9266 0.9326
0.3103 4.0 284 0.3101 0.9397 0.9343 0.9324 0.9397
0.1618 5.0 355 0.3001 0.9291 0.9251 0.9278 0.9291
0.0893 6.0 426 0.2743 0.9291 0.9251 0.9276 0.9291
0.0547 7.0 497 0.2605 0.9255 0.9236 0.9334 0.9255
0.028 8.0 568 0.2167 0.9397 0.9375 0.9403 0.9397
0.0186 9.0 639 0.2096 0.9468 0.9467 0.9499 0.9468
0.0134 10.0 710 0.2219 0.9362 0.9354 0.9402 0.9362
0.0107 11.0 781 0.2124 0.9468 0.9466 0.9507 0.9468
0.0087 12.0 852 0.2119 0.9504 0.9497 0.9534 0.9504
0.0075 13.0 923 0.2141 0.9504 0.9497 0.9534 0.9504
0.0066 14.0 994 0.2198 0.9433 0.9415 0.9442 0.9433
0.0058 15.0 1065 0.2188 0.9468 0.9454 0.9474 0.9468
0.0052 16.0 1136 0.2181 0.9468 0.9454 0.9474 0.9468
0.0047 17.0 1207 0.2220 0.9504 0.9489 0.9510 0.9504
0.0044 18.0 1278 0.2232 0.9504 0.9489 0.9510 0.9504
0.004 19.0 1349 0.2216 0.9539 0.9535 0.9565 0.9539
0.0037 20.0 1420 0.2251 0.9504 0.9489 0.9510 0.9504
0.0036 21.0 1491 0.2275 0.9468 0.9451 0.9470 0.9468
0.0034 22.0 1562 0.2264 0.9539 0.9535 0.9565 0.9539
0.0032 23.0 1633 0.2283 0.9504 0.9489 0.9510 0.9504
0.003 24.0 1704 0.2299 0.9504 0.9489 0.9510 0.9504
0.0029 25.0 1775 0.2282 0.9468 0.9451 0.9470 0.9468
0.0029 26.0 1846 0.2288 0.9468 0.9451 0.9470 0.9468
0.0028 27.0 1917 0.2286 0.9504 0.9489 0.9510 0.9504
0.0027 28.0 1988 0.2293 0.9504 0.9489 0.9510 0.9504
0.0026 29.0 2059 0.2291 0.9504 0.9489 0.9510 0.9504
0.0026 30.0 2130 0.2292 0.9504 0.9489 0.9510 0.9504

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2