---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large
  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. -->

# fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4428
- Accuracy: 0.8439
- F1: 0.8445

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4595        | 0.5   | 3654  | 0.4630          | 0.8064   | 0.8089 |
| 0.4138        | 1.0   | 7308  | 0.4497          | 0.8146   | 0.8165 |
| 0.3748        | 1.5   | 10962 | 0.4280          | 0.8420   | 0.8422 |
| 0.3687        | 2.0   | 14616 | 0.4161          | 0.8363   | 0.8376 |
| 0.3265        | 2.5   | 18270 | 0.4209          | 0.8459   | 0.8465 |
| 0.3392        | 3.0   | 21924 | 0.4107          | 0.8459   | 0.8453 |
| 0.2928        | 3.5   | 25578 | 0.4479          | 0.8395   | 0.8401 |
| 0.2975        | 4.0   | 29232 | 0.4428          | 0.8439   | 0.8445 |


### Framework versions

- Transformers 4.31.0
- Pytorch 1.13.1
- Datasets 2.14.4
- Tokenizers 0.13.3