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---
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. |