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Training complete

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README.md ADDED
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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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+ datasets:
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+ - indonlu_nergrit
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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: belajarner
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: indonlu_nergrit
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+ type: indonlu_nergrit
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+ config: indonlu_nergrit_source
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+ split: validation
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+ args: indonlu_nergrit_source
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8400335008375209
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+ - name: Recall
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+ type: recall
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+ value: 0.8631669535283993
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+ - name: F1
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+ type: f1
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+ value: 0.8514431239388794
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.949652118912081
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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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+ # belajarner
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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 the indonlu_nergrit dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2914
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+ - Precision: 0.8400
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+ - Recall: 0.8632
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+ - F1: 0.8514
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+ - Accuracy: 0.9497
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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: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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: 8
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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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+ | No log | 1.0 | 209 | 0.2655 | 0.8163 | 0.8718 | 0.8431 | 0.9424 |
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+ | No log | 2.0 | 418 | 0.2315 | 0.8146 | 0.8546 | 0.8341 | 0.9486 |
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+ | 0.04 | 3.0 | 627 | 0.2466 | 0.8291 | 0.8640 | 0.8462 | 0.9470 |
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+ | 0.04 | 4.0 | 836 | 0.2412 | 0.8322 | 0.8623 | 0.8470 | 0.9503 |
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+ | 0.03 | 5.0 | 1045 | 0.2636 | 0.8386 | 0.8898 | 0.8635 | 0.9521 |
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+ | 0.03 | 6.0 | 1254 | 0.2830 | 0.8399 | 0.8623 | 0.8510 | 0.9497 |
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+ | 0.03 | 7.0 | 1463 | 0.2848 | 0.8376 | 0.8657 | 0.8515 | 0.9500 |
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+ | 0.013 | 8.0 | 1672 | 0.2914 | 0.8400 | 0.8632 | 0.8514 | 0.9497 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.2
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