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--- |
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license: mit |
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base_model: flax-community/indonesian-roberta-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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language: |
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- ind |
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model-index: |
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- name: indonesian-roberta-base-nerp-tagger |
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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: test |
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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: 0.8102477477477478 |
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- name: Recall |
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type: recall |
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value: 0.8107042253521127 |
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- name: F1 |
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type: f1 |
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value: 0.8104759222754154 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9615076182838813 |
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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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# indonesian-roberta-base-nerp-tagger |
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This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the indonlu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1180 |
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- Precision: 0.8102 |
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- Recall: 0.8107 |
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- F1: 0.8105 |
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- Accuracy: 0.9615 |
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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: 16 |
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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 | 0.1419 | 0.7491 | 0.8034 | 0.7753 | 0.9551 | |
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| 0.2261 | 2.0 | 840 | 0.1317 | 0.7889 | 0.7983 | 0.7936 | 0.9569 | |
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| 0.1081 | 3.0 | 1260 | 0.1430 | 0.7587 | 0.8300 | 0.7927 | 0.9546 | |
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| 0.0777 | 4.0 | 1680 | 0.1459 | 0.7848 | 0.8266 | 0.8052 | 0.9577 | |
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| 0.0563 | 5.0 | 2100 | 0.1525 | 0.7923 | 0.8125 | 0.8022 | 0.9579 | |
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| 0.0441 | 6.0 | 2520 | 0.1552 | 0.7986 | 0.8176 | 0.8080 | 0.9584 | |
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| 0.0441 | 7.0 | 2940 | 0.1692 | 0.7910 | 0.8232 | 0.8068 | 0.9584 | |
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| 0.0387 | 8.0 | 3360 | 0.1677 | 0.7894 | 0.8306 | 0.8095 | 0.9588 | |
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| 0.032 | 9.0 | 3780 | 0.1784 | 0.7939 | 0.8249 | 0.8091 | 0.9586 | |
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| 0.0284 | 10.0 | 4200 | 0.1817 | 0.7950 | 0.8261 | 0.8102 | 0.9588 | |
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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 2.16.1 |
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- Tokenizers 0.15.1 |
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