bert-base-cased-ft-conll-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: 0.0576
- Precision: 0.9131
- Recall: 0.9404
- F1: 0.9265
- Accuracy: 0.9847
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2855 | 1.0 | 220 | 0.0768 | 0.8557 | 0.9100 | 0.8820 | 0.9783 |
| 0.0655 | 2.0 | 440 | 0.0633 | 0.9026 | 0.9327 | 0.9174 | 0.9825 |
| 0.0437 | 3.0 | 660 | 0.0576 | 0.9131 | 0.9404 | 0.9265 | 0.9847 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for sauc-abadal-lloret/bert-base-cased-ft-conll-ner
Base model
google-bert/bert-base-casedDataset used to train sauc-abadal-lloret/bert-base-cased-ft-conll-ner
Evaluation results
- Precision on conll2003validation set self-reported0.913
- Recall on conll2003validation set self-reported0.940
- F1 on conll2003validation set self-reported0.927
- Accuracy on conll2003validation set self-reported0.985