---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: KR-FinBert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: klue
      type: klue
      config: ner
      split: train
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.70817831734221
    - name: Recall
      type: recall
      value: 0.7610296696359683
    - name: F1
      type: f1
      value: 0.7336533910338766
    - name: Accuracy
      type: accuracy
      value: 0.9504335292160994
---

<!-- 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. -->

# KR-FinBert-finetuned-ner

This model is a fine-tuned version of [snunlp/KR-FinBert](https://huggingface.co/snunlp/KR-FinBert) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1634
- Precision: 0.7082
- Recall: 0.7610
- F1: 0.7337
- Accuracy: 0.9504

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2028        | 1.0   | 1313 | 0.1852          | 0.6650    | 0.7060 | 0.6849 | 0.9406   |
| 0.1232        | 2.0   | 2626 | 0.1627          | 0.7028    | 0.7459 | 0.7237 | 0.9487   |
| 0.0942        | 3.0   | 3939 | 0.1634          | 0.7082    | 0.7610 | 0.7337 | 0.9504   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2