bert-finetuned-ner4
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0775
- eval_precision: 0.9251
- eval_recall: 0.9460
- eval_f1: 0.9354
- eval_accuracy: 0.9841
- eval_runtime: 9.2322
- eval_samples_per_second: 352.028
- eval_steps_per_second: 44.085
- epoch: 1.0
- step: 1756
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for kabear/bert-finetuned-ner4
Base model
google-bert/bert-base-cased