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NusaBert-ner-v1.3

Token Classification
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NusaBert-ner-v1.3
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---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# NusaBert-ner-v1.3

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.
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