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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-r4a2d0.1-1 |
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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-r4a2d0.1-1 |
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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: 0.3239 |
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- Accuracy: 0.8622 |
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- Precision: 0.8373 |
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- Recall: 0.8250 |
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- F1: 0.8307 |
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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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| 0.5658 | 1.0 | 122 | 0.5195 | 0.7268 | 0.6646 | 0.6492 | 0.6550 | |
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| 0.5125 | 2.0 | 244 | 0.5060 | 0.7293 | 0.6805 | 0.6935 | 0.6855 | |
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| 0.4809 | 3.0 | 366 | 0.4686 | 0.7669 | 0.7184 | 0.7151 | 0.7167 | |
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| 0.4353 | 4.0 | 488 | 0.4295 | 0.7920 | 0.7500 | 0.7353 | 0.7417 | |
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| 0.4116 | 5.0 | 610 | 0.4171 | 0.8020 | 0.7628 | 0.7849 | 0.7714 | |
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| 0.3809 | 6.0 | 732 | 0.3865 | 0.8446 | 0.8148 | 0.8051 | 0.8096 | |
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| 0.3681 | 7.0 | 854 | 0.3697 | 0.8496 | 0.8193 | 0.8161 | 0.8177 | |
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| 0.3469 | 8.0 | 976 | 0.3554 | 0.8471 | 0.8206 | 0.8018 | 0.8102 | |
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| 0.3455 | 9.0 | 1098 | 0.3494 | 0.8496 | 0.8211 | 0.8111 | 0.8158 | |
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| 0.3284 | 10.0 | 1220 | 0.3437 | 0.8496 | 0.8289 | 0.7961 | 0.8096 | |
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| 0.3132 | 11.0 | 1342 | 0.3371 | 0.8596 | 0.8389 | 0.8132 | 0.8243 | |
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| 0.3042 | 12.0 | 1464 | 0.3371 | 0.8546 | 0.8254 | 0.8221 | 0.8238 | |
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| 0.3063 | 13.0 | 1586 | 0.3317 | 0.8596 | 0.8406 | 0.8107 | 0.8233 | |
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| 0.3013 | 14.0 | 1708 | 0.3304 | 0.8622 | 0.8373 | 0.8250 | 0.8307 | |
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| 0.2928 | 15.0 | 1830 | 0.3295 | 0.8596 | 0.8325 | 0.8257 | 0.8290 | |
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| 0.2864 | 16.0 | 1952 | 0.3284 | 0.8622 | 0.8351 | 0.8300 | 0.8325 | |
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| 0.2819 | 17.0 | 2074 | 0.3254 | 0.8596 | 0.8347 | 0.8207 | 0.8272 | |
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| 0.2877 | 18.0 | 2196 | 0.3249 | 0.8596 | 0.8336 | 0.8232 | 0.8281 | |
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| 0.2819 | 19.0 | 2318 | 0.3241 | 0.8647 | 0.8410 | 0.8267 | 0.8333 | |
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| 0.2803 | 20.0 | 2440 | 0.3239 | 0.8622 | 0.8373 | 0.8250 | 0.8307 | |
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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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