---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ko_fin_ner_roberta_small_model
  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. -->

# ko_fin_ner_roberta_small_model

This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2873
- Precision: 0.7436
- Recall: 0.8774
- F1: 0.8050
- Accuracy: 0.9374

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 25   | 1.0272          | 0.1215    | 0.1662 | 0.1404 | 0.7237   |
| No log        | 2.0   | 50   | 0.7136          | 0.2360    | 0.4033 | 0.2978 | 0.7695   |
| No log        | 3.0   | 75   | 0.5289          | 0.3422    | 0.5586 | 0.4244 | 0.8285   |
| No log        | 4.0   | 100  | 0.4404          | 0.4184    | 0.6076 | 0.4956 | 0.8730   |
| No log        | 5.0   | 125  | 0.3768          | 0.4124    | 0.6540 | 0.5058 | 0.8866   |
| No log        | 6.0   | 150  | 0.3484          | 0.4758    | 0.6975 | 0.5657 | 0.8953   |
| No log        | 7.0   | 175  | 0.3236          | 0.5477    | 0.7357 | 0.6279 | 0.9039   |
| No log        | 8.0   | 200  | 0.3097          | 0.5702    | 0.7520 | 0.6486 | 0.9015   |
| No log        | 9.0   | 225  | 0.3168          | 0.6167    | 0.7629 | 0.6821 | 0.9096   |
| No log        | 10.0  | 250  | 0.2950          | 0.6176    | 0.8011 | 0.6975 | 0.9145   |
| No log        | 11.0  | 275  | 0.2806          | 0.6674    | 0.8147 | 0.7337 | 0.9195   |
| No log        | 12.0  | 300  | 0.2749          | 0.6853    | 0.8365 | 0.7534 | 0.9266   |
| No log        | 13.0  | 325  | 0.2743          | 0.7002    | 0.8338 | 0.7612 | 0.9292   |
| No log        | 14.0  | 350  | 0.2862          | 0.6774    | 0.8011 | 0.7341 | 0.9238   |
| No log        | 15.0  | 375  | 0.2703          | 0.6879    | 0.8529 | 0.7616 | 0.9276   |
| No log        | 16.0  | 400  | 0.2752          | 0.7036    | 0.8474 | 0.7689 | 0.9293   |
| No log        | 17.0  | 425  | 0.2721          | 0.6998    | 0.8447 | 0.7654 | 0.9305   |
| No log        | 18.0  | 450  | 0.2831          | 0.6979    | 0.8311 | 0.7587 | 0.9299   |
| No log        | 19.0  | 475  | 0.2857          | 0.7252    | 0.8556 | 0.7850 | 0.9319   |
| 0.2786        | 20.0  | 500  | 0.2792          | 0.7260    | 0.8665 | 0.7901 | 0.9319   |
| 0.2786        | 21.0  | 525  | 0.2604          | 0.7355    | 0.8638 | 0.7945 | 0.9349   |
| 0.2786        | 22.0  | 550  | 0.2603          | 0.7092    | 0.8638 | 0.7789 | 0.9359   |
| 0.2786        | 23.0  | 575  | 0.3026          | 0.7227    | 0.8665 | 0.7881 | 0.9342   |
| 0.2786        | 24.0  | 600  | 0.2800          | 0.7431    | 0.8747 | 0.8035 | 0.9375   |
| 0.2786        | 25.0  | 625  | 0.2838          | 0.7283    | 0.8692 | 0.7925 | 0.9361   |
| 0.2786        | 26.0  | 650  | 0.2813          | 0.7339    | 0.8719 | 0.7970 | 0.9371   |
| 0.2786        | 27.0  | 675  | 0.2881          | 0.7407    | 0.8719 | 0.8010 | 0.9358   |
| 0.2786        | 28.0  | 700  | 0.2894          | 0.7379    | 0.8747 | 0.8005 | 0.9362   |
| 0.2786        | 29.0  | 725  | 0.2889          | 0.7483    | 0.8747 | 0.8065 | 0.9368   |
| 0.2786        | 30.0  | 750  | 0.2873          | 0.7436    | 0.8774 | 0.8050 | 0.9374   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3