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--- |
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library_name: transformers |
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base_model: cahya/NusaBert-v1.3 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- grit-id/id_nergrit_corpus |
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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_nergrit_1.3 |
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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: grit-id/id_nergrit_corpus ner |
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type: grit-id/id_nergrit_corpus |
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config: ner |
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split: validation |
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args: ner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8010483135824977 |
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- name: Recall |
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type: recall |
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value: 0.8338275412169375 |
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- name: F1 |
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type: f1 |
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value: 0.8171093159760562 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9476653696498054 |
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pipeline_tag: token-classification |
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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-ner-v1.3 |
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This model is a fine-tuned version of [cahya/NusaBert-v1.3](https://huggingface.co/cahya/NusaBert-v1.3) on the grit-id/id_nergrit_corpus ner dataset. |
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It supports a context length of 8192, the same as the model *cahya/NusaBert-v1.3* which was pre-trained from scratch using ModernBERT architecture. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2174 |
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- Precision: 0.8010 |
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- Recall: 0.8338 |
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- F1: 0.8171 |
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- Accuracy: 0.9477 |
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## Model description |
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The dataset contains 19 following entities |
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``` |
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'CRD': Cardinal |
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'DAT': Date |
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'EVT': Event |
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'FAC': Facility |
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'GPE': Geopolitical Entity |
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'LAW': Law Entity (such as Undang-Undang) |
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'LOC': Location |
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'MON': Money |
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'NOR': Political Organization |
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'ORD': Ordinal |
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'ORG': Organization |
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'PER': Person |
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'PRC': Percent |
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'PRD': Product |
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'QTY': Quantity |
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'REG': Religion |
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'TIM': Time |
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'WOA': Work of Art |
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'LAN': Language |
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``` |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 2.19.2 |
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- Tokenizers 0.21.0 |
