---
license: mit
base_model: beomi/KcELECTRA-base-v2022
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: KcELECTRA-base-v2022-KEmoFact-0925
  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. -->

# KcELECTRA-base-v2022-KEmoFact-0925

This model is a fine-tuned version of [beomi/KcELECTRA-base-v2022](https://huggingface.co/beomi/KcELECTRA-base-v2022) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3810
- Precision: 0.2344
- Recall: 0.2824
- F1: 0.2562
- Accuracy: 0.7161
- Jaccard Scores: 0.6958
- Cls Accuracy: 0.6348

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Jaccard Scores | Cls Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------:|:------------:|
| No log        | 1.0   | 414  | 1.0972          | 0.2070    | 0.2510 | 0.2269 | 0.7237   | 0.6675         | 0.6273       |
| 0.4703        | 2.0   | 828  | 1.1390          | 0.2038    | 0.2510 | 0.2249 | 0.7180   | 0.6755         | 0.6195       |
| 0.3437        | 3.0   | 1242 | 1.2184          | 0.1977    | 0.2510 | 0.2212 | 0.7121   | 0.6756         | 0.6340       |
| 0.2478        | 4.0   | 1656 | 1.2727          | 0.2035    | 0.2572 | 0.2272 | 0.7167   | 0.6779         | 0.6407       |
| 0.1769        | 5.0   | 2070 | 1.3348          | 0.2098    | 0.2572 | 0.2311 | 0.7209   | 0.6772         | 0.6407       |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3