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generic_ner_model

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0999
  • Precision: 0.8727
  • Recall: 0.8953
  • F1: 0.8838
  • Accuracy: 0.9740

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Used the Ontonotes 5 data for fine-tuning. https://huggingface.co/datasets/tner/ontonotes5#label-id

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: 3

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1083 1.0 1958 0.1007 0.8684 0.8836 0.8759 0.9723
0.0679 2.0 3916 0.0977 0.8672 0.8960 0.8813 0.9738
0.0475 3.0 5874 0.0999 0.8727 0.8953 0.8838 0.9740

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2
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