---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-indonli-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-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: 0.4642
- Accuracy: 0.8521
- F1: 0.8520

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0772        | 0.5   | 40   | 1.0981          | 0.3473   | 0.1940 |
| 1.1047        | 0.99  | 80   | 1.0967          | 0.3878   | 0.2972 |
| 1.1123        | 1.5   | 120  | 0.7637          | 0.7128   | 0.7099 |
| 0.8279        | 1.99  | 160  | 0.5739          | 0.7870   | 0.7848 |
| 0.5873        | 2.5   | 200  | 0.5059          | 0.8229   | 0.8232 |
| 0.5873        | 2.99  | 240  | 0.5047          | 0.8234   | 0.8258 |
| 0.5418        | 3.5   | 280  | 0.4696          | 0.8380   | 0.8381 |
| 0.4472        | 3.99  | 320  | 0.4415          | 0.8457   | 0.8458 |
| 0.4041        | 4.5   | 360  | 0.4622          | 0.8521   | 0.8522 |
| 0.3767        | 4.99  | 400  | 0.4435          | 0.8489   | 0.8498 |
| 0.3767        | 5.5   | 440  | 0.4731          | 0.8498   | 0.8503 |
| 0.3307        | 5.99  | 480  | 0.4642          | 0.8521   | 0.8520 |


### Framework versions

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