bert-finetuned-ner-accelerated-v3
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: 0.0639
- Precision: 0.9375
- Recall: 0.9497
- F1: 0.9436
- Accuracy: 0.9865
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.074 | 1.0 | 1756 | 0.0644 | 0.9039 | 0.9359 | 0.9196 | 0.9825 |
| 0.0353 | 2.0 | 3512 | 0.0700 | 0.9295 | 0.9429 | 0.9362 | 0.9844 |
| 0.0203 | 3.0 | 5268 | 0.0639 | 0.9375 | 0.9497 | 0.9436 | 0.9865 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for martynab/bert-finetuned-ner-accelerated-v3
Base model
google-bert/bert-base-casedDataset used to train martynab/bert-finetuned-ner-accelerated-v3
Evaluation results
- Precision on conll2003validation set self-reported0.938
- Recall on conll2003validation set self-reported0.950
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.986