metadata
library_name: transformers
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: 0.0623
- Precision: 0.9406
- Recall: 0.9464
- F1: 0.9435
- Accuracy: 0.9860
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0389 | 1.0 | 1756 | 0.0623 | 0.9339 | 0.9386 | 0.9362 | 0.9845 |
| 0.019 | 2.0 | 3512 | 0.0623 | 0.9406 | 0.9464 | 0.9435 | 0.9860 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3