bert-base-cased-ner-conll2003
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: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911
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: 5e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
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Finetuned from
Dataset used to train kamalkraj/bert-base-cased-ner-conll2003
Evaluation results
- Precision on conll2003self-reported0.944
- Recall on conll2003self-reported0.953
- F1 on conll2003self-reported0.948
- Accuracy on conll2003self-reported0.991
- Accuracy on conll2003test set self-reported0.912
- Precision on conll2003test set self-reported0.937
- Recall on conll2003test set self-reported0.926
- F1 on conll2003test set self-reported0.931
- loss on conll2003test set self-reported0.437