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apwic
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nerugm-base-3

Token Classification
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Safetensors
Indonesian
bert
Generated from Trainer
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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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+
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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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+
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+ # nerugm-base-3
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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