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xlmr-indonesian-ner-v1
AzrilFahmiardi/xlmr-indonesian-ner-v1
xlmr-indonesian-ner-v1 is a token classification model from AzrilFahmiardi. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 24, 2025
- Updated
- Dec 2, 2025
- SHA
- df389481
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Model card
From the Hugging Face model README
<!-- 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. -->
xlmr_finetuned_ner_600
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0724
- Precision: 0.6876
- Recall: 0.7559
- F1: 0.7201
- Accuracy: 0.9769
Model description
More information needed
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0945 | 1.0 | 120 | 0.0678 | 0.6698 | 0.7752 | 0.7187 | 0.9776 |
| 0.0656 | 2.0 | 240 | 0.0700 | 0.6687 | 0.7683 | 0.7150 | 0.9773 |
| 0.0584 | 3.0 | 360 | 0.0724 | 0.6876 | 0.7559 | 0.7201 | 0.9769 |
| 0.0322 | 4.0 | 480 | 0.0750 | 0.6785 | 0.7628 | 0.7182 | 0.9771 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Token Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Nov 24, 2025
- Updated
- Dec 2, 2025