---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: KoELECTRA-small-v3-modu-ner
  results: []
language:
- ko
pipeline_tag: token-classification
examples:
widget:
- text: "서울역으로 안내해줘"
  example_title: "Sentence_1"
- text: "에어컨 온도를 3도 올려줘"
  example_title: "Sentence_2"
---

<!-- 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. -->

# KoELECTRA-small-v3-modu-ner

This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1354
- Precision: 0.8084
- Recall: 0.8311
- F1: 0.8196
- Accuracy: 0.9599

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 7575
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 3788  | 0.2991          | 0.6481    | 0.6373 | 0.6426 | 0.9229   |
| No log        | 2.0   | 7576  | 0.1904          | 0.7479    | 0.7418 | 0.7448 | 0.9438   |
| No log        | 3.0   | 11364 | 0.1620          | 0.7577    | 0.7940 | 0.7754 | 0.9502   |
| No log        | 4.0   | 15152 | 0.1505          | 0.7890    | 0.7982 | 0.7936 | 0.9544   |
| No log        | 5.0   | 18940 | 0.1417          | 0.7905    | 0.8163 | 0.8032 | 0.9563   |
| No log        | 6.0   | 22728 | 0.1392          | 0.7914    | 0.8250 | 0.8079 | 0.9572   |
| No log        | 7.0   | 26516 | 0.1363          | 0.8060    | 0.8231 | 0.8144 | 0.9589   |
| No log        | 8.0   | 30304 | 0.1367          | 0.8035    | 0.8294 | 0.8162 | 0.9592   |
| No log        | 9.0   | 34092 | 0.1349          | 0.8085    | 0.8296 | 0.8189 | 0.9597   |
| 0.2299        | 10.0  | 37880 | 0.1354          | 0.8084    | 0.8311 | 0.8196 | 0.9599   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2