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bert-large-uncased_ner_wnut_17

This model is a fine-tuned version of bert-large-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2516
  • Precision: 0.7053
  • Recall: 0.5754
  • F1: 0.6337
  • Accuracy: 0.9603

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: cosine
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 213 0.2143 0.6353 0.4605 0.5340 0.9490
No log 2.0 426 0.2299 0.7322 0.5036 0.5967 0.9556
0.1489 3.0 639 0.2137 0.6583 0.5945 0.6248 0.9603
0.1489 4.0 852 0.2494 0.7035 0.5789 0.6352 0.9604
0.0268 5.0 1065 0.2516 0.7053 0.5754 0.6337 0.9603

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0
  • Datasets 2.1.0
  • Tokenizers 0.12.1
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Dataset used to train Gladiator/bert-large-uncased_ner_wnut_17

Evaluation results