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distilbert-q-classifier-2

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: 0.2779
  • Accuracy: 0.9421
  • Precision Weighted: 0.9429
  • Recall Weighted: 0.9421
  • F1 Weighted: 0.9421
  • Precision Macro: 0.9429
  • Recall Macro: 0.9421
  • F1 Macro: 0.9421

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: 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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Weighted Recall Weighted F1 Weighted Precision Macro Recall Macro F1 Macro
No log 1.0 48 0.2252 0.9144 0.9144 0.9144 0.9144 0.9144 0.9144 0.9144
No log 2.0 96 0.1682 0.9329 0.9333 0.9329 0.9329 0.9333 0.9329 0.9329
No log 3.0 144 0.2251 0.9236 0.9269 0.9236 0.9235 0.9269 0.9236 0.9235
No log 4.0 192 0.2421 0.9352 0.9376 0.9352 0.9351 0.9376 0.9352 0.9351
No log 5.0 240 0.2138 0.9375 0.9383 0.9375 0.9375 0.9383 0.9375 0.9375
No log 6.0 288 0.2165 0.9398 0.9399 0.9398 0.9398 0.9399 0.9398 0.9398
No log 7.0 336 0.2470 0.9398 0.9408 0.9398 0.9398 0.9408 0.9398 0.9398
No log 8.0 384 0.2509 0.9352 0.9353 0.9352 0.9352 0.9353 0.9352 0.9352
No log 9.0 432 0.2686 0.9352 0.9355 0.9352 0.9352 0.9355 0.9352 0.9352
No log 10.0 480 0.2779 0.9421 0.9429 0.9421 0.9421 0.9429 0.9421 0.9421

Framework versions

  • Transformers 4.43.3
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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