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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: sentiment_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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# sentiment_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: 0.1665 |
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- Accuracy: 0.9720 |
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- F1: 0.9259 |
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- Precision: 0.9615 |
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- Recall: 0.8929 |
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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 | 72 | 0.2060 | 0.9510 | 0.8727 | 0.8889 | 0.8571 | |
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| No log | 2.0 | 144 | 0.2337 | 0.9580 | 0.8846 | 0.9583 | 0.8214 | |
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| No log | 3.0 | 216 | 0.2416 | 0.9441 | 0.8571 | 0.8571 | 0.8571 | |
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| No log | 4.0 | 288 | 0.1905 | 0.9580 | 0.8846 | 0.9583 | 0.8214 | |
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| No log | 5.0 | 360 | 0.2029 | 0.9580 | 0.8929 | 0.8929 | 0.8929 | |
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| No log | 6.0 | 432 | 0.1665 | 0.9720 | 0.9259 | 0.9615 | 0.8929 | |
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| 0.0706 | 7.0 | 504 | 0.1899 | 0.9580 | 0.8889 | 0.9231 | 0.8571 | |
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| 0.0706 | 8.0 | 576 | 0.1990 | 0.9580 | 0.8889 | 0.9231 | 0.8571 | |
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| 0.0706 | 9.0 | 648 | 0.2139 | 0.9580 | 0.8889 | 0.9231 | 0.8571 | |
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| 0.0706 | 10.0 | 720 | 0.2171 | 0.9580 | 0.8889 | 0.9231 | 0.8571 | |
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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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