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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: nerugm-lora-r8a1d0.15 |
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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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# nerugm-lora-r8a1d0.15 |
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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: 0.1281 |
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- Precision: 0.7470 |
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- Recall: 0.8629 |
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- F1: 0.8008 |
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- Accuracy: 0.9579 |
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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: 16 |
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- eval_batch_size: 64 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.7018 | 1.0 | 528 | 0.3353 | 0.5529 | 0.4800 | 0.5138 | 0.9115 | |
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| 0.2639 | 2.0 | 1056 | 0.1912 | 0.6494 | 0.8210 | 0.7252 | 0.9412 | |
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| 0.1862 | 3.0 | 1584 | 0.1672 | 0.6739 | 0.8536 | 0.7531 | 0.9466 | |
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| 0.1612 | 4.0 | 2112 | 0.1446 | 0.7238 | 0.8512 | 0.7824 | 0.9539 | |
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| 0.1439 | 5.0 | 2640 | 0.1390 | 0.7254 | 0.8582 | 0.7863 | 0.9545 | |
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| 0.1358 | 6.0 | 3168 | 0.1392 | 0.7256 | 0.8652 | 0.7893 | 0.9551 | |
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| 0.129 | 7.0 | 3696 | 0.1384 | 0.7267 | 0.8698 | 0.7919 | 0.9561 | |
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| 0.1228 | 8.0 | 4224 | 0.1339 | 0.7353 | 0.8698 | 0.7969 | 0.9575 | |
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| 0.1168 | 9.0 | 4752 | 0.1321 | 0.7439 | 0.8559 | 0.7960 | 0.9577 | |
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| 0.1146 | 10.0 | 5280 | 0.1300 | 0.7445 | 0.8582 | 0.7973 | 0.9581 | |
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| 0.1105 | 11.0 | 5808 | 0.1327 | 0.7333 | 0.8675 | 0.7948 | 0.9571 | |
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| 0.1083 | 12.0 | 6336 | 0.1333 | 0.7342 | 0.8652 | 0.7943 | 0.9569 | |
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| 0.106 | 13.0 | 6864 | 0.1265 | 0.7490 | 0.8582 | 0.7999 | 0.9591 | |
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| 0.1032 | 14.0 | 7392 | 0.1269 | 0.7445 | 0.8582 | 0.7973 | 0.9589 | |
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| 0.1023 | 15.0 | 7920 | 0.1291 | 0.7455 | 0.8629 | 0.7999 | 0.9585 | |
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| 0.1014 | 16.0 | 8448 | 0.1271 | 0.7400 | 0.8582 | 0.7947 | 0.9575 | |
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| 0.1002 | 17.0 | 8976 | 0.1281 | 0.7460 | 0.8722 | 0.8042 | 0.9589 | |
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| 0.0986 | 18.0 | 9504 | 0.1304 | 0.7416 | 0.8722 | 0.8016 | 0.9573 | |
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| 0.0978 | 19.0 | 10032 | 0.1271 | 0.7520 | 0.8652 | 0.8046 | 0.9589 | |
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| 0.0984 | 20.0 | 10560 | 0.1281 | 0.7470 | 0.8629 | 0.8008 | 0.9579 | |
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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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