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---
tags:
- generated_from_trainer
datasets:
- liputan6
model-index:
- name: IndoRetNet-Liputan6
results: []
license: apache-2.0
language:
- id
---
<!-- 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. -->
# 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 |