<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
bert-finetuned-ner_stake
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: 0.0071
- Precision: 0.9462
- Recall: 0.9784
- F1: 0.9620
- Accuracy: 0.9981
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 |
|---|---|---|---|---|---|---|---|
| 0.0166 | 1.0 | 1900 | 0.0095 | 0.9337 | 0.9666 | 0.9499 | 0.9972 |
| 0.0093 | 2.0 | 3800 | 0.0062 | 0.9526 | 0.9781 | 0.9652 | 0.9981 |
| 0.0048 | 3.0 | 5700 | 0.0071 | 0.9462 | 0.9784 | 0.9620 | 0.9981 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
