---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: IndoJavaneseNLI-XLMR-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. -->

# IndoJavaneseNLI-XLMR-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7328
- Accuracy: 0.7770
- Precision: 0.7770
- Recall: 0.7770
- F1 Score: 0.7772

## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 101
- 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 | Precision | Recall | F1 Score |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.4856        | 1.0   | 10330  | 1.7105          | 0.6063   | 0.6063    | 0.6063 | 0.6096   |
| 1.8291        | 2.0   | 20660  | 1.7699          | 0.6800   | 0.6800    | 0.6800 | 0.6785   |
| 1.7113        | 3.0   | 30990  | 1.6908          | 0.7260   | 0.7260    | 0.7260 | 0.7254   |
| 1.6058        | 4.0   | 41320  | 1.6276          | 0.7456   | 0.7456    | 0.7456 | 0.7451   |
| 1.3499        | 5.0   | 51650  | 1.6436          | 0.7565   | 0.7565    | 0.7565 | 0.7568   |
| 1.1362        | 6.0   | 61980  | 1.6715          | 0.7615   | 0.7615    | 0.7615 | 0.7619   |
| 1.1918        | 7.0   | 72310  | 1.7237          | 0.7738   | 0.7738    | 0.7738 | 0.7743   |
| 0.9035        | 8.0   | 82640  | 1.7436          | 0.7751   | 0.7751    | 0.7751 | 0.7750   |
| 0.9824        | 9.0   | 92970  | 1.7354          | 0.7806   | 0.7806    | 0.7806 | 0.7804   |
| 0.9303        | 10.0  | 103300 | 1.7328          | 0.7770   | 0.7770    | 0.7770 | 0.7772   |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1