---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-ko
  results: []
---

<!-- 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-ko

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Precision: 0.9859
- Recall: 0.9913
- F1: 0.9886
- Accuracy: 0.9980

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0649        | 1.0   | 1250 | 0.0295          | 0.9468    | 0.9679 | 0.9572 | 0.9919   |
| 0.0275        | 2.0   | 2500 | 0.0132          | 0.9777    | 0.9870 | 0.9823 | 0.9966   |
| 0.0141        | 3.0   | 3750 | 0.0083          | 0.9859    | 0.9913 | 0.9886 | 0.9980   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2