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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-r8 |
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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-r8 |
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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: 0.2786 |
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- Accuracy: 0.8847 |
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- Precision: 0.8648 |
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- Recall: 0.8534 |
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- F1: 0.8588 |
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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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| 0.5623 | 1.0 | 122 | 0.5217 | 0.7268 | 0.6604 | 0.6217 | 0.6301 | |
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| 0.5061 | 2.0 | 244 | 0.4898 | 0.7569 | 0.7074 | 0.7105 | 0.7089 | |
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| 0.4443 | 3.0 | 366 | 0.4085 | 0.8120 | 0.7751 | 0.7620 | 0.7679 | |
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| 0.3805 | 4.0 | 488 | 0.3672 | 0.8246 | 0.7980 | 0.7609 | 0.7752 | |
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| 0.3488 | 5.0 | 610 | 0.3535 | 0.8521 | 0.8207 | 0.8254 | 0.8229 | |
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| 0.3156 | 6.0 | 732 | 0.3337 | 0.8571 | 0.8299 | 0.8214 | 0.8255 | |
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| 0.3055 | 7.0 | 854 | 0.3217 | 0.8622 | 0.8385 | 0.8225 | 0.8298 | |
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| 0.2995 | 8.0 | 976 | 0.3145 | 0.8596 | 0.8347 | 0.8207 | 0.8272 | |
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| 0.2825 | 9.0 | 1098 | 0.3090 | 0.8672 | 0.8402 | 0.8385 | 0.8394 | |
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| 0.272 | 10.0 | 1220 | 0.2992 | 0.8722 | 0.8453 | 0.8471 | 0.8462 | |
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| 0.2626 | 11.0 | 1342 | 0.3008 | 0.8747 | 0.8568 | 0.8338 | 0.8440 | |
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| 0.2641 | 12.0 | 1464 | 0.2949 | 0.8747 | 0.8488 | 0.8488 | 0.8488 | |
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| 0.257 | 13.0 | 1586 | 0.2885 | 0.8772 | 0.8592 | 0.8381 | 0.8475 | |
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| 0.2473 | 14.0 | 1708 | 0.2826 | 0.8822 | 0.8596 | 0.8542 | 0.8568 | |
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| 0.2456 | 15.0 | 1830 | 0.2826 | 0.8847 | 0.8609 | 0.8609 | 0.8609 | |
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| 0.2477 | 16.0 | 1952 | 0.2795 | 0.8847 | 0.8621 | 0.8584 | 0.8602 | |
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| 0.2426 | 17.0 | 2074 | 0.2794 | 0.8797 | 0.8585 | 0.8474 | 0.8526 | |
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| 0.2359 | 18.0 | 2196 | 0.2796 | 0.8872 | 0.8658 | 0.8602 | 0.8629 | |
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| 0.2417 | 19.0 | 2318 | 0.2787 | 0.8847 | 0.8648 | 0.8534 | 0.8588 | |
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| 0.2319 | 20.0 | 2440 | 0.2786 | 0.8847 | 0.8648 | 0.8534 | 0.8588 | |
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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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