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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-r8a0d0.1 |
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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-r8a0d0.1 |
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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: 0.1278 |
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- Precision: 0.7600 |
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- Recall: 0.8815 |
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- F1: 0.8162 |
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- Accuracy: 0.9593 |
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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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| 0.713 | 1.0 | 528 | 0.3558 | 0.4950 | 0.3736 | 0.4258 | 0.8990 | |
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| 0.2793 | 2.0 | 1056 | 0.1931 | 0.6472 | 0.8048 | 0.7174 | 0.9392 | |
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| 0.1876 | 3.0 | 1584 | 0.1619 | 0.6758 | 0.8466 | 0.7516 | 0.9462 | |
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| 0.1593 | 4.0 | 2112 | 0.1416 | 0.7366 | 0.8629 | 0.7948 | 0.9555 | |
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| 0.1412 | 5.0 | 2640 | 0.1350 | 0.7386 | 0.8652 | 0.7969 | 0.9559 | |
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| 0.1325 | 6.0 | 3168 | 0.1361 | 0.7324 | 0.8698 | 0.7952 | 0.9555 | |
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| 0.126 | 7.0 | 3696 | 0.1383 | 0.7310 | 0.8698 | 0.7944 | 0.9553 | |
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| 0.1194 | 8.0 | 4224 | 0.1349 | 0.7456 | 0.8838 | 0.8088 | 0.9583 | |
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| 0.1137 | 9.0 | 4752 | 0.1299 | 0.7495 | 0.8745 | 0.8072 | 0.9583 | |
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| 0.1112 | 10.0 | 5280 | 0.1285 | 0.7455 | 0.8698 | 0.8029 | 0.9579 | |
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| 0.1065 | 11.0 | 5808 | 0.1304 | 0.7525 | 0.8815 | 0.8119 | 0.9587 | |
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| 0.1044 | 12.0 | 6336 | 0.1329 | 0.7520 | 0.8791 | 0.8106 | 0.9577 | |
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| 0.1026 | 13.0 | 6864 | 0.1257 | 0.7520 | 0.8722 | 0.8076 | 0.9585 | |
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| 0.0989 | 14.0 | 7392 | 0.1265 | 0.7626 | 0.8791 | 0.8167 | 0.9599 | |
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| 0.0982 | 15.0 | 7920 | 0.1281 | 0.7631 | 0.8815 | 0.8180 | 0.9597 | |
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| 0.0974 | 16.0 | 8448 | 0.1264 | 0.7515 | 0.8768 | 0.8093 | 0.9597 | |
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| 0.0966 | 17.0 | 8976 | 0.1282 | 0.7545 | 0.8838 | 0.8140 | 0.9589 | |
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| 0.095 | 18.0 | 9504 | 0.1292 | 0.7570 | 0.8815 | 0.8145 | 0.9589 | |
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| 0.0941 | 19.0 | 10032 | 0.1268 | 0.7585 | 0.8815 | 0.8154 | 0.9595 | |
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| 0.0948 | 20.0 | 10560 | 0.1278 | 0.7600 | 0.8815 | 0.8162 | 0.9593 | |
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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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