---
base_model: monologg/koelectra-small-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: find_tune_bert_output
  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. -->

# find_tune_bert_output

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: 0.2110
- Overall Precision: 0.8468
- Overall Recall: 0.8561
- Overall F1: 0.8514
- Overall Accuracy: 0.9405
- Loc F1: 0.9090
- Org F1: 0.7685
- Per F1: 0.8477

## 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:|
| 0.2146        | 0.8   | 1000 | 0.2903          | 0.7632            | 0.8340         | 0.7970     | 0.9175           | 0.8729 | 0.6812 | 0.7966 |
| 0.2538        | 1.6   | 2000 | 0.2374          | 0.8183            | 0.8290         | 0.8236     | 0.9299           | 0.8940 | 0.7187 | 0.8178 |
| 0.2192        | 2.4   | 3000 | 0.2265          | 0.8246            | 0.8437         | 0.8340     | 0.9340           | 0.8956 | 0.7403 | 0.8322 |
| 0.1967        | 3.2   | 4000 | 0.2206          | 0.8261            | 0.8529         | 0.8393     | 0.9354           | 0.9047 | 0.7499 | 0.8290 |
| 0.1814        | 4.0   | 5000 | 0.2169          | 0.8371            | 0.8538         | 0.8453     | 0.9379           | 0.9057 | 0.7605 | 0.8388 |
| 0.1661        | 4.8   | 6000 | 0.2169          | 0.8403            | 0.8490         | 0.8446     | 0.9382           | 0.9050 | 0.7583 | 0.8378 |
| 0.1577        | 5.6   | 7000 | 0.2116          | 0.8413            | 0.8604         | 0.8507     | 0.9401           | 0.9088 | 0.7670 | 0.8472 |
| 0.1544        | 6.4   | 8000 | 0.2110          | 0.8468            | 0.8561         | 0.8514     | 0.9405           | 0.9090 | 0.7685 | 0.8477 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2