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--- |
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license: apache-2.0 |
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base_model: LazarusNLP/IndoNanoT5-base |
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tags: |
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- generated_from_trainer |
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language: |
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- ind |
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datasets: |
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- GEM/indonlg |
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metrics: |
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- rouge |
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model-index: |
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- name: IndoNanoT5-base-Liputan6-Canonical |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: indonlg |
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type: indonlg |
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config: liputan6_canonical |
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split: test |
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args: liputan6_canonical |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3976 |
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- name: Rouge2 |
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type: rouge |
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value: 0.2229 |
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- name: RougeL |
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type: rouge |
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value: 0.3346 |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: indonlg |
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type: indonlg |
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config: liputan6_extreme |
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split: test |
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args: liputan6_extreme |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3323 |
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- name: Rouge2 |
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type: rouge |
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value: 0.1417 |
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- name: RougeL |
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type: rouge |
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value: 0.2621 |
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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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# LazarusNLP/IndoNanoT5-base-Liputan6-Canonical |
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This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co./LazarusNLP/IndoNanoT5-base) on the indonlg dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1194 |
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- Rouge1: 0.3976 |
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- Rouge2: 0.2229 |
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- Rougel: 0.3346 |
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- Rougelsum: 0.3345 |
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- Gen Len: 43.3808 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 0.9693 | 1.0 | 24236 | 1.3245 | 0.3082 | 0.1585 | 0.2687 | 0.2688 | 18.9956 | |
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| 0.9338 | 2.0 | 48472 | 1.2759 | 0.3105 | 0.159 | 0.2705 | 0.2706 | 18.9985 | |
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| 0.8632 | 3.0 | 72708 | 1.2698 | 0.3094 | 0.1586 | 0.2701 | 0.2702 | 18.9995 | |
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| 0.8257 | 4.0 | 96944 | 1.2631 | 0.312 | 0.1603 | 0.2716 | 0.2715 | 18.9993 | |
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| 0.7789 | 5.0 | 121180 | 1.2642 | 0.3149 | 0.1625 | 0.2748 | 0.2747 | 18.9998 | |
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| 0.7595 | 6.0 | 145416 | 1.2587 | 0.3202 | 0.1658 | 0.279 | 0.2791 | 18.9995 | |
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| 0.7343 | 7.0 | 169652 | 1.2644 | 0.3183 | 0.1647 | 0.2773 | 0.2773 | 18.9996 | |
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| 0.7165 | 8.0 | 193888 | 1.2635 | 0.3141 | 0.1605 | 0.2732 | 0.2732 | 18.9993 | |
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| 0.6697 | 9.0 | 218124 | 1.2856 | 0.316 | 0.162 | 0.275 | 0.275 | 18.9998 | |
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| 0.6729 | 10.0 | 242360 | 1.2809 | 0.3195 | 0.164 | 0.2775 | 0.2776 | 18.9992 | |
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| 0.6471 | 11.0 | 266596 | 1.2833 | 0.3185 | 0.1636 | 0.2769 | 0.277 | 18.9982 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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