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--- |
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language: |
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- id |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment-lora-r8a1d0.2-0 |
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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-lora-r8a1d0.2-0 |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3274 |
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- Accuracy: 0.8622 |
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- Precision: 0.8319 |
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- Recall: 0.8400 |
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- F1: 0.8357 |
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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: 5e-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 8 |
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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: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5608 | 1.0 | 122 | 0.5057 | 0.7218 | 0.6593 | 0.6482 | 0.6527 | |
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| 0.5012 | 2.0 | 244 | 0.4792 | 0.7519 | 0.7117 | 0.7370 | 0.7193 | |
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| 0.4628 | 3.0 | 366 | 0.4281 | 0.7694 | 0.7256 | 0.7419 | 0.7321 | |
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| 0.4045 | 4.0 | 488 | 0.3951 | 0.8170 | 0.7803 | 0.7730 | 0.7765 | |
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| 0.3701 | 5.0 | 610 | 0.4239 | 0.7995 | 0.7633 | 0.7956 | 0.7736 | |
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| 0.3362 | 6.0 | 732 | 0.3721 | 0.8296 | 0.7934 | 0.8019 | 0.7974 | |
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| 0.3285 | 7.0 | 854 | 0.3725 | 0.8346 | 0.7989 | 0.8205 | 0.8078 | |
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| 0.3061 | 8.0 | 976 | 0.3537 | 0.8421 | 0.8087 | 0.8133 | 0.8109 | |
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| 0.3017 | 9.0 | 1098 | 0.3504 | 0.8421 | 0.8087 | 0.8133 | 0.8109 | |
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| 0.2942 | 10.0 | 1220 | 0.3391 | 0.8496 | 0.8186 | 0.8186 | 0.8186 | |
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| 0.2715 | 11.0 | 1342 | 0.3456 | 0.8496 | 0.8169 | 0.8261 | 0.8212 | |
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| 0.2703 | 12.0 | 1464 | 0.3534 | 0.8521 | 0.8190 | 0.8354 | 0.8262 | |
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| 0.2759 | 13.0 | 1586 | 0.3326 | 0.8521 | 0.8228 | 0.8179 | 0.8203 | |
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| 0.2705 | 14.0 | 1708 | 0.3360 | 0.8571 | 0.8266 | 0.8314 | 0.8289 | |
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| 0.2576 | 15.0 | 1830 | 0.3423 | 0.8647 | 0.8340 | 0.8467 | 0.8399 | |
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| 0.2513 | 16.0 | 1952 | 0.3394 | 0.8571 | 0.8251 | 0.8389 | 0.8314 | |
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| 0.2481 | 17.0 | 2074 | 0.3261 | 0.8571 | 0.8273 | 0.8289 | 0.8281 | |
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| 0.2561 | 18.0 | 2196 | 0.3320 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2478 | 19.0 | 2318 | 0.3269 | 0.8622 | 0.8326 | 0.8375 | 0.8349 | |
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| 0.2451 | 20.0 | 2440 | 0.3274 | 0.8622 | 0.8319 | 0.8400 | 0.8357 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
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