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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-r2a1d0.15 |
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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-r2a1d0.15 |
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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: 0.1346 |
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- Precision: 0.7342 |
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- Recall: 0.8652 |
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- F1: 0.7943 |
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- Accuracy: 0.9555 |
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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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| 0.79 | 1.0 | 528 | 0.4638 | 0.3302 | 0.0813 | 0.1305 | 0.8595 | |
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| 0.3919 | 2.0 | 1056 | 0.2519 | 0.5954 | 0.6729 | 0.6318 | 0.9275 | |
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| 0.2386 | 3.0 | 1584 | 0.1927 | 0.6540 | 0.7908 | 0.7159 | 0.9382 | |
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| 0.193 | 4.0 | 2112 | 0.1677 | 0.6826 | 0.8234 | 0.7464 | 0.9448 | |
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| 0.1712 | 5.0 | 2640 | 0.1594 | 0.6959 | 0.8443 | 0.7629 | 0.9476 | |
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| 0.1596 | 6.0 | 3168 | 0.1544 | 0.7082 | 0.8559 | 0.7751 | 0.9498 | |
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| 0.1524 | 7.0 | 3696 | 0.1519 | 0.7012 | 0.8605 | 0.7728 | 0.9506 | |
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| 0.1452 | 8.0 | 4224 | 0.1461 | 0.7203 | 0.8605 | 0.7842 | 0.9522 | |
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| 0.1397 | 9.0 | 4752 | 0.1432 | 0.7263 | 0.8559 | 0.7858 | 0.9535 | |
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| 0.1369 | 10.0 | 5280 | 0.1394 | 0.7258 | 0.8536 | 0.7845 | 0.9539 | |
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| 0.1336 | 11.0 | 5808 | 0.1375 | 0.7321 | 0.8512 | 0.7872 | 0.9543 | |
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| 0.1305 | 12.0 | 6336 | 0.1375 | 0.7345 | 0.8536 | 0.7896 | 0.9547 | |
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| 0.1281 | 13.0 | 6864 | 0.1351 | 0.7330 | 0.8536 | 0.7887 | 0.9547 | |
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| 0.1252 | 14.0 | 7392 | 0.1360 | 0.7342 | 0.8652 | 0.7943 | 0.9553 | |
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| 0.124 | 15.0 | 7920 | 0.1364 | 0.7292 | 0.8559 | 0.7875 | 0.9541 | |
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| 0.1234 | 16.0 | 8448 | 0.1351 | 0.7260 | 0.8605 | 0.7876 | 0.9549 | |
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| 0.1224 | 17.0 | 8976 | 0.1357 | 0.7299 | 0.8652 | 0.7918 | 0.9549 | |
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| 0.1208 | 18.0 | 9504 | 0.1360 | 0.7333 | 0.8675 | 0.7948 | 0.9553 | |
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| 0.1201 | 19.0 | 10032 | 0.1350 | 0.7347 | 0.8675 | 0.7956 | 0.9555 | |
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| 0.1205 | 20.0 | 10560 | 0.1346 | 0.7342 | 0.8652 | 0.7943 | 0.9555 | |
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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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