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--- |
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license: mit |
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base_model: LazarusNLP/NusaBERT-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indonlu |
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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: NusaBERT-base-NERP |
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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 |
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type: indonlu |
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config: nerp |
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split: validation |
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args: nerp |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8060507833603457 |
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- name: Recall |
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type: recall |
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value: 0.8405633802816901 |
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- name: F1 |
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type: f1 |
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value: 0.8229453943739657 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9634085213032582 |
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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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# NusaBERT-base-NERP |
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This model is a fine-tuned version of [LazarusNLP/NusaBERT-base](https://huggingface.co/LazarusNLP/NusaBERT-base) on the indonlu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1254 |
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- Precision: 0.8061 |
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- Recall: 0.8406 |
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- F1: 0.8229 |
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- Accuracy: 0.9634 |
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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: 2e-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: 10 |
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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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| No log | 1.0 | 420 | 0.1444 | 0.7415 | 0.8272 | 0.7820 | 0.9543 | |
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| 0.2385 | 2.0 | 840 | 0.1276 | 0.7879 | 0.8187 | 0.8030 | 0.9586 | |
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| 0.1143 | 3.0 | 1260 | 0.1260 | 0.7815 | 0.8510 | 0.8148 | 0.9597 | |
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| 0.0903 | 4.0 | 1680 | 0.1305 | 0.7836 | 0.8516 | 0.8162 | 0.9596 | |
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| 0.07 | 5.0 | 2100 | 0.1342 | 0.8158 | 0.8255 | 0.8206 | 0.9605 | |
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| 0.0582 | 6.0 | 2520 | 0.1343 | 0.8172 | 0.8408 | 0.8288 | 0.9606 | |
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| 0.0582 | 7.0 | 2940 | 0.1440 | 0.7936 | 0.8476 | 0.8197 | 0.9594 | |
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| 0.0521 | 8.0 | 3360 | 0.1447 | 0.8069 | 0.8453 | 0.8257 | 0.9605 | |
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| 0.0446 | 9.0 | 3780 | 0.1512 | 0.7996 | 0.8453 | 0.8218 | 0.9599 | |
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| 0.0417 | 10.0 | 4200 | 0.1524 | 0.8078 | 0.8453 | 0.8261 | 0.9606 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu118 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.1 |
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