README.md

metadata

library_name: transformers
base_model: cahya/NusaBert-v1.3
tags:
  - generated_from_trainer
datasets:
  - grit-id/id_nergrit_corpus
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: nusabert_nergrit_1.3
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: grit-id/id_nergrit_corpus ner
          type: grit-id/id_nergrit_corpus
          config: ner
          split: validation
          args: ner
        metrics:
          - name: Precision
            type: precision
            value: 0.8010483135824977
          - name: Recall
            type: recall
            value: 0.8338275412169375
          - name: F1
            type: f1
            value: 0.8171093159760562
          - name: Accuracy
            type: accuracy
            value: 0.9476653696498054
pipeline_tag: token-classification

NusaBert-ner-v1.3

This model is a fine-tuned version of cahya/NusaBert-v1.3 on the grit-id/id_nergrit_corpus ner dataset. 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. It achieves the following results on the evaluation set:

• Loss: 0.2174
• Precision: 0.8010
• Recall: 0.8338
• F1: 0.8171
• Accuracy: 0.9477

Model description

The dataset contains 19 following entities

    'CRD': Cardinal
    'DAT': Date
    'EVT': Event
    'FAC': Facility
    'GPE': Geopolitical Entity
    'LAW': Law Entity (such as Undang-Undang)
    'LOC': Location
    'MON': Money
    'NOR': Political Organization
    'ORD': Ordinal
    'ORG': Organization
    'PER': Person
    'PRC': Percent
    'PRD': Product
    'QTY': Quantity
    'REG': Religion
    'TIM': Time
    'WOA': Work of Art
    'LAN': Language

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 64
• total_eval_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 2.19.2
• Tokenizers 0.21.0