---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlmr-nli-indoindo
  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. -->

# xlmr-nli-indoindo

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6699
- Accuracy: 0.7701
- Precision: 0.7701
- Recall: 0.7701
- F1: 0.7693

## 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: 3e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0444        | 1.0   | 1722  | 0.8481          | 0.6463   | 0.6463    | 0.6463 | 0.6483 |
| 0.7958        | 2.0   | 3444  | 0.7483          | 0.7369   | 0.7369    | 0.7369 | 0.7353 |
| 0.7175        | 3.0   | 5166  | 0.6812          | 0.7579   | 0.7579    | 0.7579 | 0.7576 |
| 0.66          | 4.0   | 6888  | 0.6293          | 0.7679   | 0.7679    | 0.7679 | 0.7674 |
| 0.6056        | 5.0   | 8610  | 0.6459          | 0.7651   | 0.7651    | 0.7651 | 0.7640 |
| 0.5769        | 6.0   | 10332 | 0.6699          | 0.7701   | 0.7701    | 0.7701 | 0.7693 |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3