---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-indonli_mnli-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-NLI-indonli_mnli-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: 0.4582
- Accuracy: 0.8575
- F1: 0.8580

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4821        | 0.5   | 1574  | 0.4176          | 0.8402   | 0.8401 |
| 0.4442        | 1.0   | 3148  | 0.4007          | 0.8521   | 0.8523 |
| 0.3817        | 1.5   | 4722  | 0.3927          | 0.8529   | 0.8519 |
| 0.3635        | 2.0   | 6296  | 0.3838          | 0.8607   | 0.8609 |
| 0.3039        | 2.5   | 7870  | 0.3998          | 0.8601   | 0.8602 |
| 0.3198        | 3.0   | 9444  | 0.3914          | 0.8602   | 0.8603 |
| 0.2564        | 3.5   | 11018 | 0.4582          | 0.8575   | 0.8580 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.3