metadata
base_model: bert-base-cased
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-ner4
results: []
bert-finetuned-ner4
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0913
- Precision: 0.8712
- Recall: 0.8979
- F1: 0.8844
- Accuracy: 0.9850
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0612 | 1.0 | 2489 | 0.0625 | 0.8264 | 0.8552 | 0.8405 | 0.9816 |
| 0.0351 | 2.0 | 4978 | 0.0643 | 0.8474 | 0.8716 | 0.8594 | 0.9831 |
| 0.0189 | 3.0 | 7467 | 0.0651 | 0.8585 | 0.8995 | 0.8785 | 0.9843 |
| 0.0105 | 4.0 | 9956 | 0.0811 | 0.8690 | 0.8943 | 0.8815 | 0.9847 |
| 0.0047 | 5.0 | 12445 | 0.0913 | 0.8712 | 0.8979 | 0.8844 | 0.9850 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1