bert-finetuned-ner-100percent
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: 0.5711
- Precision: 0.8227
- Recall: 0.8498
- F1: 0.8360
- Accuracy: 0.9254
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: 2022
- 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 | 75 | 0.5329 | 0.8228 | 0.8438 | 0.8332 | 0.9277 |
| No log | 2.0 | 150 | 0.5674 | 0.8110 | 0.8438 | 0.8271 | 0.9242 |
| No log | 3.0 | 225 | 0.5711 | 0.8227 | 0.8498 | 0.8360 | 0.9254 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2