bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1838
- Precision: 0.5626
- Recall: 0.6396
- F1: 0.5986
- Accuracy: 0.9537
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 249 | 0.1846 | 0.4948 | 0.5417 | 0.5172 | 0.9482 |
| No log | 2.0 | 498 | 0.1801 | 0.5344 | 0.6113 | 0.5703 | 0.9500 |
| 0.2042 | 3.0 | 747 | 0.1838 | 0.5626 | 0.6396 | 0.5986 | 0.9537 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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Model tree for Refrainkana33/bert-finetuned-ner
Base model
google-bert/bert-base-cased