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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: general_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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# general_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: 0.2986 |
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- Accuracy: 0.9119 |
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- F1: 0.8872 |
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- Precision: 0.8921 |
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- Recall: 0.8827 |
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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: 5 |
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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 | 0.06 | 50 | 0.3626 | 0.8748 | 0.8410 | 0.8423 | 0.8398 | |
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| No log | 0.13 | 100 | 0.3231 | 0.8962 | 0.8699 | 0.8666 | 0.8734 | |
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| No log | 0.19 | 150 | 0.4256 | 0.8974 | 0.8626 | 0.8892 | 0.8437 | |
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| No log | 0.25 | 200 | 0.3339 | 0.9031 | 0.8744 | 0.8845 | 0.8658 | |
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| No log | 0.31 | 250 | 0.3043 | 0.8823 | 0.8587 | 0.8446 | 0.8792 | |
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| No log | 0.38 | 300 | 0.3125 | 0.9056 | 0.8808 | 0.8802 | 0.8813 | |
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| No log | 0.44 | 350 | 0.2946 | 0.9075 | 0.8838 | 0.8813 | 0.8863 | |
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| No log | 0.5 | 400 | 0.2924 | 0.9125 | 0.8898 | 0.8884 | 0.8912 | |
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| No log | 0.57 | 450 | 0.2991 | 0.8855 | 0.8632 | 0.8480 | 0.8865 | |
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| 0.3562 | 0.63 | 500 | 0.2986 | 0.9119 | 0.8872 | 0.8921 | 0.8827 | |
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| 0.3562 | 0.69 | 550 | 0.2851 | 0.8779 | 0.8564 | 0.8395 | 0.8864 | |
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| 0.3562 | 0.75 | 600 | 0.3272 | 0.9125 | 0.8868 | 0.8968 | 0.8781 | |
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| 0.3562 | 0.82 | 650 | 0.3438 | 0.8987 | 0.8636 | 0.8933 | 0.8431 | |
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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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