---
tags:
- generated_from_trainer
datasets:
- liputan6
model-index:
- name: IndoRetNet-Liputan6
  results: []
license: apache-2.0
language:
- id
---

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# IndoRetNet-Liputan6

This model is a Indonesian RetNet model train using the Liputan6 dataset.
Using Tokenizer from [IndoBERT](https://huggingface.co/indolem/indobert-base-uncased) 
It achieves the following results on the evaluation set:
- Loss: 3.4936

## Model description

Demonstrate training and recurrent inference using a retentive network (https://arxiv.org/pdf/2307.08621.pdf). 
The code utilizes Sehyun Choi's implementation of retentive network (https://github.com/syncdoth/RetNet).

- **License:** Apache 2.0.

## Intended uses & limitations

Intended to demonstrate training and (recurrent O(1)) inference using a retentive network in Indonesian language. 

## Training and evaluation data

Using Train and validation set from Liputan6 dataset provided by [NusaCrowd](https://github.com/IndoNLP/nusa-crowd).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.5053        | 0.17  | 1000  | 4.5145          |
| 4.1281        | 0.34  | 2000  | 4.1702          |
| 3.9452        | 0.52  | 3000  | 4.0094          |
| 3.8302        | 0.69  | 4000  | 3.8972          |
| 3.6955        | 0.86  | 5000  | 3.8144          |
| 3.589         | 1.03  | 6000  | 3.7600          |
| 3.5279        | 1.21  | 7000  | 3.7088          |
| 3.4598        | 1.38  | 8000  | 3.6670          |
| 3.4445        | 1.55  | 9000  | 3.6259          |
| 3.4098        | 1.72  | 10000 | 3.5904          |
| 3.3455        | 1.9   | 11000 | 3.5610          |
| 3.2306        | 2.07  | 12000 | 3.5406          |
| 3.261         | 2.24  | 13000 | 3.5216          |
| 3.2204        | 2.41  | 14000 | 3.5111          |
| 3.2321        | 2.59  | 15000 | 3.5001          |
| 3.2514        | 2.76  | 16000 | 3.4941          |
| 3.233         | 2.93  | 17000 | 3.4936          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0