---
license: mit
base_model: indobenchmark/indobert-lite-base-p1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
language:
- ind
datasets:
- indonli
- MoritzLaurer/multilingual-NLI-26lang-2mil7
- LazarusNLP/multilingual-NLI-26lang-2mil7-id
widget:
- text: Andi tersenyum karena mendapat hasil baik. </s></s> Andi sedih.
model-index:
- name: indobert-lite-base-p1-indonli-multilingual-nli-distil-mdeberta
  results: []
---

# IndoBERT Lite Base IndoNLI Multilingual NLI Distil mDeBERTa

IndoBERT Lite Base IndoNLI Multilingual NLI Distil mDeBERTa is a natural language inference (NLI) model based on the [ALBERT](https://arxiv.org/abs/1909.11942) model. The model was originally the pre-trained [indobenchmark/indobert-lite-base-p1](https://huggingface.co/indobenchmark/indobert-lite-base-p1) model, which is then fine-tuned on [`IndoNLI`](https://github.com/ir-nlp-csui/indonli) and the [Indonesian subsets](https://huggingface.co/datasets/LazarusNLP/multilingual-NLI-26lang-2mil7-id) of [MoritzLaurer/multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7), whilst being distilled from [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7).

## Evaluation Results

|           | `dev` Acc. | `test_lay` Acc. | `test_expert` Acc. |
| --------- | :--------: | :-------------: | :----------------: |
| `IndoNLI` |   78.60    |      74.69      |       65.55        |

## Model

| Model                                                            | #params | Arch.       | Training/Validation data (text)    |
| ---------------------------------------------------------------- | ------- | ----------- | ---------------------------------- |
| `indobert-lite-base-p1-indonli-multilingual-nli-distil-mdeberta` | 11.7M   | ALBERT Base | `IndoNLI`, Multilingual NLI (`id`) |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- `learning_rate`: `2e-05`
- `train_batch_size`: `64`
- `eval_batch_size`: `64`
- `seed`: `42`
- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `num_epochs`: `5`

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |   F1   | Precision | Recall |
| :-----------: | :---: | :---: | :-------------: | :------: | :----: | :-------: | :----: |
|    0.4808     |  1.0  | 1803  |     0.4418      |  0.7683  | 0.7593 |  0.7904   | 0.7554 |
|    0.4529     |  2.0  | 3606  |     0.4343      |  0.7738  | 0.7648 |  0.7893   | 0.7619 |
|    0.4263     |  3.0  | 5409  |     0.4383      |  0.7861  | 0.7828 |  0.7874   | 0.7807 |
|     0.398     |  4.0  | 7212  |     0.4456      |  0.7792  | 0.7767 |  0.7792   | 0.7756 |
|    0.3772     |  5.0  | 9015  |     0.4499      |  0.7711  | 0.7674 |  0.7700   | 0.7661 |

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

## References

[1] Mahendra, R., Aji, A. F., Louvan, S., Rahman, F., & Vania, C. (2021, November). [IndoNLI: A Natural Language Inference Dataset for Indonesian](https://arxiv.org/abs/2110.14566). _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing_. Association for Computational Linguistics.