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--- |
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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-r4a0d0.05-1 |
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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-r4a0d0.05-1 |
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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: 0.3356 |
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- Accuracy: 0.8622 |
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- Precision: 0.8399 |
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- Recall: 0.8200 |
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- F1: 0.8289 |
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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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| 0.5657 | 1.0 | 122 | 0.5182 | 0.7243 | 0.6604 | 0.6424 | 0.6488 | |
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| 0.5109 | 2.0 | 244 | 0.5051 | 0.7243 | 0.6748 | 0.6874 | 0.6796 | |
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| 0.48 | 3.0 | 366 | 0.4643 | 0.7569 | 0.7047 | 0.6880 | 0.6948 | |
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| 0.434 | 4.0 | 488 | 0.4281 | 0.7920 | 0.7497 | 0.7378 | 0.7431 | |
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| 0.4106 | 5.0 | 610 | 0.4194 | 0.7920 | 0.7528 | 0.7778 | 0.7618 | |
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| 0.3812 | 6.0 | 732 | 0.3936 | 0.8296 | 0.8008 | 0.7744 | 0.7854 | |
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| 0.3689 | 7.0 | 854 | 0.3700 | 0.8521 | 0.8220 | 0.8204 | 0.8212 | |
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| 0.3489 | 8.0 | 976 | 0.3656 | 0.8346 | 0.8088 | 0.7780 | 0.7905 | |
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| 0.3502 | 9.0 | 1098 | 0.3640 | 0.8371 | 0.8101 | 0.7847 | 0.7955 | |
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| 0.3349 | 10.0 | 1220 | 0.3608 | 0.8346 | 0.8074 | 0.7805 | 0.7917 | |
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| 0.3189 | 11.0 | 1342 | 0.3574 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | |
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| 0.3121 | 12.0 | 1464 | 0.3547 | 0.8471 | 0.8175 | 0.8093 | 0.8132 | |
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| 0.3181 | 13.0 | 1586 | 0.3478 | 0.8521 | 0.8332 | 0.7979 | 0.8122 | |
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| 0.3092 | 14.0 | 1708 | 0.3435 | 0.8596 | 0.8374 | 0.8157 | 0.8253 | |
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| 0.3018 | 15.0 | 1830 | 0.3466 | 0.8546 | 0.8296 | 0.8121 | 0.8200 | |
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| 0.2955 | 16.0 | 1952 | 0.3365 | 0.8596 | 0.8347 | 0.8207 | 0.8272 | |
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| 0.2917 | 17.0 | 2074 | 0.3353 | 0.8596 | 0.8374 | 0.8157 | 0.8253 | |
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| 0.2956 | 18.0 | 2196 | 0.3379 | 0.8596 | 0.8360 | 0.8182 | 0.8262 | |
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| 0.2899 | 19.0 | 2318 | 0.3353 | 0.8647 | 0.8455 | 0.8192 | 0.8306 | |
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| 0.2885 | 20.0 | 2440 | 0.3356 | 0.8622 | 0.8399 | 0.8200 | 0.8289 | |
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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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