Instructions to use archi-ai/Indo-LegalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use archi-ai/Indo-LegalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="archi-ai/Indo-LegalBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("archi-ai/Indo-LegalBERT") model = AutoModelForMaskedLM.from_pretrained("archi-ai/Indo-LegalBERT") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
model-index:
- name: Indonesian-LegalBERT-lite
results: []
Indonesian-LegalBERT-lite
This model is a fine-tuned version of indobenchmark/indobert-base-p2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.6121
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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.9701 | 1.0 | 93 | 5.5903 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3