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--- |
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license: mit |
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base_model: indobenchmark/indobert-base-p1 |
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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: indobert-finetuned-aspect-happiness-index |
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results: [] |
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pipeline_tag: text-classification |
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language: |
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- id |
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widget: |
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- text: Aku senang kuliah di Undip |
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example_title: Aspect Detection |
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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-finetuned-aspect-happiness-index |
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This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an own private dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1476 |
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- Accuracy: 0.9732 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 270 | 0.1291 | 0.9648 | |
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| 0.301 | 2.0 | 540 | 0.1708 | 0.9593 | |
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| 0.301 | 3.0 | 810 | 0.1350 | 0.9685 | |
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| 0.0655 | 4.0 | 1080 | 0.1734 | 0.9648 | |
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| 0.0655 | 5.0 | 1350 | 0.1323 | 0.9713 | |
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| 0.023 | 6.0 | 1620 | 0.1551 | 0.9676 | |
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| 0.023 | 7.0 | 1890 | 0.1558 | 0.9704 | |
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| 0.0137 | 8.0 | 2160 | 0.1531 | 0.9732 | |
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| 0.0137 | 9.0 | 2430 | 0.1493 | 0.9722 | |
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| 0.0056 | 10.0 | 2700 | 0.1476 | 0.9732 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
