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: 0.0652
- Precision: 0.9330
- Recall: 0.9488
- F1: 0.9408
- Accuracy: 0.9858
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 |
---|---|---|---|---|---|---|---|
0.0743 | 1.0 | 1756 | 0.0706 | 0.8966 | 0.9310 | 0.9135 | 0.9801 |
0.0344 | 2.0 | 3512 | 0.0714 | 0.9334 | 0.9441 | 0.9388 | 0.9843 |
0.0213 | 3.0 | 5268 | 0.0652 | 0.9330 | 0.9488 | 0.9408 | 0.9858 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for victorbarra/bert-finetuned-ner
Base model
google-bert/bert-base-cased
Finetuned
this model
Dataset used to train victorbarra/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.933
- Recall on conll2003validation set self-reported0.949
- F1 on conll2003validation set self-reported0.941
- Accuracy on conll2003validation set self-reported0.986