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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indonlu |
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metrics: |
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- accuracy |
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model-index: |
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- name: indobert-base-uncased-finetuned-indonlu-smsa |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: indonlu |
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type: indonlu |
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args: smsa |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9365079365079365 |
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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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# indobert-base-uncased-finetuned-indonlu-smsa |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2416 |
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- Accuracy: 0.9365 |
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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: 32 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 344 | 0.6655 | 0.7206 | |
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| 0.7832 | 2.0 | 688 | 0.3297 | 0.8651 | |
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| 0.3331 | 3.0 | 1032 | 0.2184 | 0.9254 | |
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| 0.3331 | 4.0 | 1376 | 0.2057 | 0.9302 | |
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| 0.2053 | 5.0 | 1720 | 0.2105 | 0.9270 | |
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| 0.1408 | 6.0 | 2064 | 0.2036 | 0.9270 | |
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| 0.1408 | 7.0 | 2408 | 0.2416 | 0.9365 | |
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| 0.1044 | 8.0 | 2752 | 0.3145 | 0.9302 | |
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| 0.0637 | 9.0 | 3096 | 0.3095 | 0.9294 | |
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| 0.0637 | 10.0 | 3440 | 0.3354 | 0.9286 | |
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### Framework versions |
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- Transformers 4.14.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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