---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: KoELECTRA-small-v3-modu-ner
  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. -->

# 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: 0.1350
- Precision: 0.8068
- Recall: 0.8308
- F1: 0.8186
- Accuracy: 0.9598

## 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  | 0.3021          | 0.6356    | 0.6380 | 0.6368 | 0.9223   |
| No log        | 2.0   | 7576  | 0.1905          | 0.7397    | 0.7441 | 0.7419 | 0.9431   |
| No log        | 3.0   | 11364 | 0.1612          | 0.7611    | 0.7897 | 0.7751 | 0.9505   |
| No log        | 4.0   | 15152 | 0.1494          | 0.7855    | 0.7998 | 0.7926 | 0.9544   |
| No log        | 5.0   | 18940 | 0.1427          | 0.7833    | 0.8194 | 0.8009 | 0.9559   |
| No log        | 6.0   | 22728 | 0.1398          | 0.7912    | 0.8223 | 0.8064 | 0.9572   |
| No log        | 7.0   | 26516 | 0.1361          | 0.8035    | 0.8240 | 0.8136 | 0.9587   |
| No log        | 8.0   | 30304 | 0.1360          | 0.8047    | 0.8280 | 0.8162 | 0.9592   |
| No log        | 9.0   | 34092 | 0.1346          | 0.8058    | 0.8299 | 0.8177 | 0.9596   |
| 0.2256        | 10.0  | 37880 | 0.1350          | 0.8068    | 0.8308 | 0.8186 | 0.9598   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2