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--- |
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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-base-3 |
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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-base-3 |
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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: 0.2918 |
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- Precision: 0.7974 |
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- Recall: 0.8847 |
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- F1: 0.8388 |
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- Accuracy: 0.9619 |
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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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| 0.3817 | 1.0 | 106 | 0.1442 | 0.7266 | 0.8732 | 0.7932 | 0.9515 | |
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| 0.1266 | 2.0 | 212 | 0.1385 | 0.7381 | 0.8934 | 0.8083 | 0.9551 | |
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| 0.087 | 3.0 | 318 | 0.1367 | 0.7512 | 0.8790 | 0.8101 | 0.9568 | |
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| 0.0528 | 4.0 | 424 | 0.1468 | 0.7732 | 0.8646 | 0.8163 | 0.9595 | |
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| 0.0424 | 5.0 | 530 | 0.1664 | 0.7899 | 0.8559 | 0.8216 | 0.9607 | |
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| 0.0275 | 6.0 | 636 | 0.2044 | 0.7714 | 0.8847 | 0.8242 | 0.9583 | |
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| 0.019 | 7.0 | 742 | 0.2377 | 0.7410 | 0.8905 | 0.8089 | 0.9554 | |
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| 0.0145 | 8.0 | 848 | 0.2432 | 0.7758 | 0.8876 | 0.8280 | 0.9588 | |
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| 0.0102 | 9.0 | 954 | 0.2287 | 0.8109 | 0.9020 | 0.8540 | 0.9641 | |
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| 0.0067 | 10.0 | 1060 | 0.2430 | 0.8026 | 0.8905 | 0.8443 | 0.9617 | |
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| 0.0064 | 11.0 | 1166 | 0.2675 | 0.7943 | 0.8905 | 0.8397 | 0.9602 | |
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| 0.0046 | 12.0 | 1272 | 0.2743 | 0.7828 | 0.8934 | 0.8345 | 0.9619 | |
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| 0.0034 | 13.0 | 1378 | 0.2666 | 0.7995 | 0.8963 | 0.8451 | 0.9619 | |
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| 0.0036 | 14.0 | 1484 | 0.2606 | 0.8117 | 0.8818 | 0.8453 | 0.9634 | |
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| 0.0027 | 15.0 | 1590 | 0.2862 | 0.7913 | 0.8963 | 0.8405 | 0.9627 | |
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| 0.0016 | 16.0 | 1696 | 0.2793 | 0.8021 | 0.8876 | 0.8427 | 0.9629 | |
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| 0.0012 | 17.0 | 1802 | 0.2951 | 0.7949 | 0.8934 | 0.8412 | 0.9622 | |
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| 0.0012 | 18.0 | 1908 | 0.2930 | 0.7938 | 0.8876 | 0.8381 | 0.9617 | |
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| 0.0014 | 19.0 | 2014 | 0.2953 | 0.7912 | 0.8847 | 0.8354 | 0.9612 | |
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| 0.0007 | 20.0 | 2120 | 0.2918 | 0.7974 | 0.8847 | 0.8388 | 0.9619 | |
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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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