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Ahmed87
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bert-cased-ner-fcit499

Token Classification
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PyTorch
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bert
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bert-cased-ner-fcit499

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0404
  • Precision: 0.9417
  • Recall: 0.9502
  • F1: 0.9460
  • Accuracy: 0.9905

Model description

More information neededx

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 157 0.0578 0.8782 0.8976 0.8878 0.9825
No log 2.0 314 0.0425 0.9317 0.9343 0.9330 0.9885
No log 3.0 471 0.0391 0.9381 0.9433 0.9407 0.9897
0.1097 4.0 628 0.0397 0.9377 0.9467 0.9422 0.9900
0.1097 5.0 785 0.0404 0.9417 0.9502 0.9460 0.9905

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.2
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Dataset used to train Ahmed87/bert-cased-ner-fcit499

Evaluation results

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