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--- |
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license: mit |
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base_model: ilos-vigil/bigbird-small-indonesian |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: test_trainer |
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results: [] |
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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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# test_trainer |
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This model is a fine-tuned version of [ilos-vigil/bigbird-small-indonesian](https://huggingface.co/ilos-vigil/bigbird-small-indonesian) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2500 |
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- Accuracy: 0.5 |
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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: 0.0005 |
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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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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 16 |
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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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- num_epochs: 1 |
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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 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.2611 | 0.04 | 500 | 0.2617 | 0.5 | |
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| 0.2547 | 0.07 | 1000 | 0.2525 | 0.5 | |
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| 0.2527 | 0.11 | 1500 | 0.2500 | 0.5 | |
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| 0.2523 | 0.14 | 2000 | 0.2522 | 0.5 | |
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| 0.253 | 0.18 | 2500 | 0.2504 | 0.5 | |
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| 0.252 | 0.21 | 3000 | 0.2553 | 0.5 | |
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| 0.2662 | 0.25 | 3500 | 0.2501 | 0.5 | |
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| 0.2869 | 0.29 | 4000 | 0.2705 | 0.5 | |
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| 0.2534 | 0.32 | 4500 | 0.2505 | 0.5 | |
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| 0.252 | 0.36 | 5000 | 0.2504 | 0.5 | |
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| 0.2518 | 0.39 | 5500 | 0.2526 | 0.5 | |
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| 0.2508 | 0.43 | 6000 | 0.2501 | 0.5 | |
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| 0.2512 | 0.46 | 6500 | 0.2501 | 0.5 | |
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| 0.251 | 0.5 | 7000 | 0.2500 | 0.5 | |
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| 0.2502 | 0.54 | 7500 | 0.2502 | 0.5 | |
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| 0.2507 | 0.57 | 8000 | 0.2545 | 0.5 | |
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| 0.2511 | 0.61 | 8500 | 0.2509 | 0.5 | |
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| 0.2503 | 0.64 | 9000 | 0.2500 | 0.5 | |
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| 0.2506 | 0.68 | 9500 | 0.2501 | 0.5 | |
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| 0.2505 | 0.71 | 10000 | 0.2504 | 0.5 | |
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| 0.2505 | 0.75 | 10500 | 0.2504 | 0.5 | |
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| 0.2504 | 0.79 | 11000 | 0.2501 | 0.5 | |
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| 0.2502 | 0.82 | 11500 | 0.2500 | 0.5 | |
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| 0.2503 | 0.86 | 12000 | 0.2500 | 0.5 | |
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| 0.2501 | 0.89 | 12500 | 0.2500 | 0.5 | |
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| 0.2503 | 0.93 | 13000 | 0.2500 | 0.5 | |
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| 0.2502 | 0.96 | 13500 | 0.2500 | 0.5 | |
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| 0.2502 | 1.0 | 14000 | 0.2500 | 0.5 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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