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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-r4a0d0.15 |
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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-r4a0d0.15 |
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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: 0.1301 |
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- Precision: 0.7357 |
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- Recall: 0.8652 |
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- F1: 0.7952 |
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- Accuracy: 0.9577 |
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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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| 0.7663 | 1.0 | 528 | 0.4380 | 0.3934 | 0.1116 | 0.1738 | 0.8659 | |
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| 0.3481 | 2.0 | 1056 | 0.2220 | 0.6018 | 0.7403 | 0.6639 | 0.9339 | |
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| 0.2139 | 3.0 | 1584 | 0.1790 | 0.6561 | 0.8327 | 0.7339 | 0.9400 | |
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| 0.1777 | 4.0 | 2112 | 0.1535 | 0.7164 | 0.8559 | 0.7800 | 0.9512 | |
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| 0.1578 | 5.0 | 2640 | 0.1445 | 0.7367 | 0.8698 | 0.7978 | 0.9535 | |
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| 0.1469 | 6.0 | 3168 | 0.1441 | 0.7139 | 0.8745 | 0.7861 | 0.9535 | |
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| 0.1399 | 7.0 | 3696 | 0.1453 | 0.7175 | 0.8838 | 0.7920 | 0.9524 | |
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| 0.1333 | 8.0 | 4224 | 0.1403 | 0.7298 | 0.8838 | 0.7995 | 0.9547 | |
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| 0.1273 | 9.0 | 4752 | 0.1368 | 0.7387 | 0.8722 | 0.7999 | 0.9563 | |
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| 0.1246 | 10.0 | 5280 | 0.1342 | 0.7426 | 0.8768 | 0.8042 | 0.9569 | |
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| 0.1195 | 11.0 | 5808 | 0.1351 | 0.7359 | 0.8791 | 0.8012 | 0.9571 | |
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| 0.1172 | 12.0 | 6336 | 0.1349 | 0.7373 | 0.8791 | 0.8020 | 0.9573 | |
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| 0.1155 | 13.0 | 6864 | 0.1296 | 0.7441 | 0.8768 | 0.8050 | 0.9581 | |
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| 0.1118 | 14.0 | 7392 | 0.1302 | 0.7367 | 0.8698 | 0.7978 | 0.9577 | |
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| 0.1111 | 15.0 | 7920 | 0.1322 | 0.7426 | 0.8768 | 0.8042 | 0.9577 | |
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| 0.1097 | 16.0 | 8448 | 0.1303 | 0.7353 | 0.8698 | 0.7969 | 0.9577 | |
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| 0.1094 | 17.0 | 8976 | 0.1306 | 0.7343 | 0.8722 | 0.7973 | 0.9573 | |
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| 0.1077 | 18.0 | 9504 | 0.1319 | 0.7372 | 0.8722 | 0.7990 | 0.9577 | |
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| 0.1065 | 19.0 | 10032 | 0.1296 | 0.7376 | 0.8675 | 0.7973 | 0.9577 | |
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| 0.1078 | 20.0 | 10560 | 0.1301 | 0.7357 | 0.8652 | 0.7952 | 0.9577 | |
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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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