---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: xlm-roberta-large
model-index:
- name: fine-tuned-NLI-indonli_mnli_squadid-nli-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_squadid-nli-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: 0.2874
- Accuracy: 0.9148
- F1: 0.9152

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3454        | 0.5   | 2499  | 0.2659          | 0.8987   | 0.8988 |
| 0.3177        | 1.0   | 4998  | 0.2420          | 0.9081   | 0.9087 |
| 0.2821        | 1.5   | 7497  | 0.2407          | 0.9111   | 0.9114 |
| 0.249         | 2.0   | 9996  | 0.2258          | 0.9159   | 0.9158 |
| 0.2246        | 2.5   | 12495 | 0.2454          | 0.9143   | 0.9146 |
| 0.2308        | 3.0   | 14994 | 0.2370          | 0.9155   | 0.9159 |
| 0.1869        | 3.5   | 17493 | 0.2691          | 0.9147   | 0.9149 |
| 0.18          | 4.0   | 19992 | 0.2616          | 0.9143   | 0.9151 |
| 0.1329        | 4.5   | 22491 | 0.2874          | 0.9148   | 0.9152 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2