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phobert_eda_segmented_results
BonTori/phobert_eda_segmented_results
phobert_eda_segmented_results is a text classification model from BonTori. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Aug 29, 2026
- Updated
- Aug 29, 2026
- SHA
- d8370adf
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Base models
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. -->
phobert_eda_segmented_results
This model is a fine-tuned version of vinai/phobert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5795
- Accuracy: 0.8192
- F1: 0.6181
- Precision: 0.6102
- Recall: 0.6411
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4854 | 1.0 | 8402 | 0.4685 | 0.8125 | 0.6015 | 0.5892 | 0.6251 |
| 0.3286 | 2.0 | 16804 | 0.5038 | 0.8083 | 0.6078 | 0.6074 | 0.6380 |
| 0.2231 | 3.0 | 25206 | 0.5795 | 0.8192 | 0.6181 | 0.6102 | 0.6411 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Aug 29, 2026
- Updated
- Aug 29, 2026