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--- |
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license: mit |
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base_model: indobenchmark/indobert-base-p2 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: story_model |
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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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# story_model |
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This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2576 |
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- Accuracy: 0.9409 |
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- F1: 0.9622 |
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- Precision: 0.9929 |
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- Recall: 0.9333 |
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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: 2e-05 |
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- train_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 94 | 0.3243 | 0.8817 | 0.9308 | 0.8810 | 0.9867 | |
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| No log | 2.0 | 188 | 0.2781 | 0.8925 | 0.9363 | 0.8963 | 0.98 | |
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| No log | 3.0 | 282 | 0.2576 | 0.9409 | 0.9622 | 0.9929 | 0.9333 | |
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| No log | 4.0 | 376 | 0.3482 | 0.9247 | 0.9545 | 0.9304 | 0.98 | |
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| No log | 5.0 | 470 | 0.3785 | 0.9301 | 0.9574 | 0.9419 | 0.9733 | |
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| 0.1516 | 6.0 | 564 | 0.4252 | 0.9140 | 0.9467 | 0.9467 | 0.9467 | |
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| 0.1516 | 7.0 | 658 | 0.5097 | 0.9086 | 0.9428 | 0.9524 | 0.9333 | |
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| 0.1516 | 8.0 | 752 | 0.5036 | 0.9086 | 0.9431 | 0.9463 | 0.94 | |
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| 0.1516 | 9.0 | 846 | 0.5268 | 0.9086 | 0.9439 | 0.9346 | 0.9533 | |
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| 0.1516 | 10.0 | 940 | 0.5314 | 0.9086 | 0.9439 | 0.9346 | 0.9533 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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