w11wo_indonesian-roberta-base-posp-tagger

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license: mit base_model: flax-community/indonesian-roberta-base tags:

• generated_from_trainer datasets:
• indonlu language:
• ind metrics:
• precision
• recall
• f1
• accuracy model-index:
• name: indonesian-roberta-base-posp-tagger results:
  • task: name: Token Classification type: token-classification dataset: name: indonlu type: indonlu config: posp split: test args: posp metrics:
    • name: Precision type: precision value: 0.9625100240577386
    • name: Recall type: recall value: 0.9625100240577386
    • name: F1 type: f1 value: 0.9625100240577386
    • name: Accuracy type: accuracy value: 0.9625100240577386

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indonesian-roberta-base-posp-tagger

This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset. It achieves the following results on the evaluation set:

• Loss: 0.1395
• Precision: 0.9625
• Recall: 0.9625
• F1: 0.9625
• Accuracy: 0.9625

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	420	0.2254	0.9313	0.9313	0.9313	0.9313
0.4398	2.0	840	0.1617	0.9499	0.9499	0.9499	0.9499
0.1566	3.0	1260	0.1431	0.9569	0.9569	0.9569	0.9569
0.103	4.0	1680	0.1412	0.9605	0.9605	0.9605	0.9605
0.0723	5.0	2100	0.1408	0.9635	0.9635	0.9635	0.9635
0.051	6.0	2520	0.1408	0.9642	0.9642	0.9642	0.9642
0.051	7.0	2940	0.1510	0.9635	0.9635	0.9635	0.9635
0.0368	8.0	3360	0.1653	0.9645	0.9645	0.9645	0.9645
0.0277	9.0	3780	0.1664	0.9644	0.9644	0.9644	0.9644
0.0231	10.0	4200	0.1668	0.9646	0.9646	0.9646	0.9646

Framework versions

• Transformers 4.37.2
• Pytorch 2.2.0+cu118
• Datasets 2.16.1
• Tokenizers 0.15.1