bert-finetuned-ner
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: 0.0636
- Precision: 0.9343
- Recall: 0.9498
- F1: 0.9420
- Accuracy: 0.9861
Model description
This is a model for Named entity recognition NER
Intended uses & limitations
Open source
Training and evaluation data
The conll2003 dataset
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
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0757 | 1.0 | 1756 | 0.0638 | 0.9215 | 0.9362 | 0.9288 | 0.9833 |
0.0352 | 2.0 | 3512 | 0.0667 | 0.9360 | 0.9482 | 0.9421 | 0.9858 |
0.0215 | 3.0 | 5268 | 0.0636 | 0.9343 | 0.9498 | 0.9420 | 0.9861 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
google-bert/bert-base-casedDataset used to train HusseinEid/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.934
- Recall on conll2003validation set self-reported0.950
- F1 on conll2003validation set self-reported0.942
- Accuracy on conll2003validation set self-reported0.986