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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- liputan6 |
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model-index: |
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- name: IndoRetNet-Liputan6 |
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results: [] |
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license: apache-2.0 |
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language: |
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- id |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# IndoRetNet-Liputan6 |
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This model is a Indonesian RetNet model train using the Liputan6 dataset. |
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Using Tokenizer from [IndoBERT](https://huggingface.co/indolem/indobert-base-uncased) |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4936 |
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## Model description |
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Demonstrate training and recurrent inference using a retentive network (https://arxiv.org/pdf/2307.08621.pdf). |
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The code utilizes Sehyun Choi's implementation of retentive network (https://github.com/syncdoth/RetNet). |
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- **License:** Apache 2.0. |
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## Intended uses & limitations |
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Intended to demonstrate training and (recurrent O(1)) inference using a retentive network in Indonesian language. |
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## Training and evaluation data |
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Using Train and validation set from Liputan6 dataset provided by [NusaCrowd](https://github.com/IndoNLP/nusa-crowd). |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0006 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 4.5053 | 0.17 | 1000 | 4.5145 | |
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| 4.1281 | 0.34 | 2000 | 4.1702 | |
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| 3.9452 | 0.52 | 3000 | 4.0094 | |
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| 3.8302 | 0.69 | 4000 | 3.8972 | |
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| 3.6955 | 0.86 | 5000 | 3.8144 | |
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| 3.589 | 1.03 | 6000 | 3.7600 | |
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| 3.5279 | 1.21 | 7000 | 3.7088 | |
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| 3.4598 | 1.38 | 8000 | 3.6670 | |
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| 3.4445 | 1.55 | 9000 | 3.6259 | |
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| 3.4098 | 1.72 | 10000 | 3.5904 | |
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| 3.3455 | 1.9 | 11000 | 3.5610 | |
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| 3.2306 | 2.07 | 12000 | 3.5406 | |
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| 3.261 | 2.24 | 13000 | 3.5216 | |
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| 3.2204 | 2.41 | 14000 | 3.5111 | |
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| 3.2321 | 2.59 | 15000 | 3.5001 | |
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| 3.2514 | 2.76 | 16000 | 3.4941 | |
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| 3.233 | 2.93 | 17000 | 3.4936 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
