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---
base_model: monologg/koelectra-small-v3-discriminator
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: chkpt
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8826086956521739
- name: F1
type: f1
value: 0.8275730495029622
- name: Precision
type: precision
value: 0.7789981096408317
- name: Recall
type: recall
value: 0.8826086956521739
---
<!-- 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. -->
# chkpt
This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2815
- Accuracy: 0.8826
- F1: 0.8276
- Precision: 0.7790
- Recall: 0.8826
## 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: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 29 | 1.2815 | 0.8826 | 0.8276 | 0.7790 | 0.8826 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|