bert-pos
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: 0.3112
- Precision: 0.9201
- Recall: 0.9267
- F1: 0.9234
- Accuracy: 0.9293
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1761 | 1.0 | 1756 | 0.3112 | 0.9201 | 0.9267 | 0.9234 | 0.9293 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- 4
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for baxi099/bert-pos
Base model
google-bert/bert-base-casedDataset used to train baxi099/bert-pos
Evaluation results
- Precision on conll2003validation set self-reported0.920
- Recall on conll2003validation set self-reported0.927
- F1 on conll2003validation set self-reported0.923
- Accuracy on conll2003validation set self-reported0.929