metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
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: 0.0583
- Precision: 0.9299
- Recall: 0.9487
- F1: 0.9392
- Accuracy: 0.9863
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 |
|---|---|---|---|---|---|---|---|
| 0.0802 | 1.0 | 1756 | 0.0786 | 0.9015 | 0.9298 | 0.9154 | 0.9794 |
| 0.0415 | 2.0 | 3512 | 0.0564 | 0.9253 | 0.9465 | 0.9358 | 0.9861 |
| 0.0248 | 3.0 | 5268 | 0.0583 | 0.9299 | 0.9487 | 0.9392 | 0.9863 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3