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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-r4a2d0.1 |
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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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# nerugm-lora-r4a2d0.1 |
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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: 0.1302 |
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- Precision: 0.7375 |
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- Recall: 0.8605 |
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- F1: 0.7943 |
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- Accuracy: 0.9573 |
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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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| 0.7665 | 1.0 | 528 | 0.4290 | 0.3803 | 0.1255 | 0.1887 | 0.8711 | |
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| 0.336 | 2.0 | 1056 | 0.2177 | 0.6187 | 0.7751 | 0.6882 | 0.9335 | |
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| 0.2067 | 3.0 | 1584 | 0.1743 | 0.6523 | 0.8187 | 0.7261 | 0.9410 | |
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| 0.1734 | 4.0 | 2112 | 0.1525 | 0.7026 | 0.8443 | 0.7670 | 0.9500 | |
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| 0.1557 | 5.0 | 2640 | 0.1442 | 0.7125 | 0.8512 | 0.7757 | 0.9524 | |
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| 0.146 | 6.0 | 3168 | 0.1445 | 0.7085 | 0.8629 | 0.7781 | 0.9520 | |
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| 0.1397 | 7.0 | 3696 | 0.1444 | 0.7145 | 0.8768 | 0.7874 | 0.9525 | |
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| 0.1338 | 8.0 | 4224 | 0.1386 | 0.7262 | 0.8675 | 0.7906 | 0.9545 | |
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| 0.1277 | 9.0 | 4752 | 0.1365 | 0.7395 | 0.8629 | 0.7965 | 0.9561 | |
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| 0.1255 | 10.0 | 5280 | 0.1332 | 0.7348 | 0.8629 | 0.7937 | 0.9563 | |
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| 0.1215 | 11.0 | 5808 | 0.1330 | 0.7242 | 0.8652 | 0.7885 | 0.9557 | |
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| 0.1189 | 12.0 | 6336 | 0.1340 | 0.7342 | 0.8652 | 0.7943 | 0.9561 | |
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| 0.1179 | 13.0 | 6864 | 0.1295 | 0.7445 | 0.8582 | 0.7973 | 0.9571 | |
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| 0.114 | 14.0 | 7392 | 0.1295 | 0.7446 | 0.8675 | 0.8014 | 0.9579 | |
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| 0.1128 | 15.0 | 7920 | 0.1317 | 0.7371 | 0.8652 | 0.7960 | 0.9571 | |
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| 0.1115 | 16.0 | 8448 | 0.1300 | 0.7376 | 0.8675 | 0.7973 | 0.9575 | |
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| 0.1109 | 17.0 | 8976 | 0.1307 | 0.7357 | 0.8652 | 0.7952 | 0.9577 | |
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| 0.1097 | 18.0 | 9504 | 0.1319 | 0.7386 | 0.8652 | 0.7969 | 0.9575 | |
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| 0.1086 | 19.0 | 10032 | 0.1296 | 0.7375 | 0.8605 | 0.7943 | 0.9573 | |
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| 0.1094 | 20.0 | 10560 | 0.1302 | 0.7375 | 0.8605 | 0.7943 | 0.9573 | |
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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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