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phobert-large-vsmec-5class
qdovan03/phobert-large-vsmec-5class
phobert-large-vsmec-5class is a text classification model from qdovan03. 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
- Jun 23, 2026
- Updated
- Jun 23, 2026
- SHA
- be47d307
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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-large-vsmec-5class
This model is a fine-tuned version of vinai/phobert-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3865
- Accuracy: 0.6735
- F1 Weighted: 0.6684
- F1 Macro: 0.6619
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Macro |
|---|---|---|---|---|---|---|
| 1.6386 | 1.0 | 359 | 1.6093 | 0.2726 | 0.2867 | 0.2451 |
| 1.3278 | 2.0 | 718 | 1.2181 | 0.5394 | 0.5385 | 0.5015 |
| 1.1186 | 3.0 | 1077 | 1.0814 | 0.5816 | 0.5855 | 0.5645 |
| 1.0724 | 4.0 | 1436 | 1.0846 | 0.6210 | 0.6168 | 0.5964 |
| 0.8941 | 5.0 | 1795 | 1.1514 | 0.6370 | 0.6305 | 0.6197 |
| 0.8185 | 6.0 | 2154 | 1.1291 | 0.6706 | 0.6736 | 0.6518 |
| 0.7954 | 7.0 | 2513 | 1.1931 | 0.6458 | 0.6448 | 0.6198 |
| 0.7019 | 8.0 | 2872 | 1.2620 | 0.6560 | 0.6583 | 0.6475 |
| 0.6637 | 9.0 | 3231 | 1.3124 | 0.6574 | 0.6591 | 0.6320 |
| 0.6326 | 10.0 | 3590 | 1.3566 | 0.6691 | 0.6657 | 0.6534 |
| 0.6074 | 11.0 | 3949 | 1.3491 | 0.6603 | 0.6643 | 0.6454 |
| 0.6072 | 12.0 | 4308 | 1.3870 | 0.6735 | 0.6684 | 0.6619 |
| 0.5517 | 13.0 | 4667 | 1.4099 | 0.6560 | 0.6514 | 0.6366 |
| 0.6018 | 14.0 | 5026 | 1.4134 | 0.6633 | 0.6653 | 0.6545 |
| 0.5688 | 15.0 | 5385 | 1.4302 | 0.6676 | 0.6678 | 0.6507 |
| 0.5826 | 16.0 | 5744 | 1.4399 | 0.6560 | 0.6562 | 0.6301 |
| 0.5909 | 17.0 | 6103 | 1.4440 | 0.6487 | 0.6504 | 0.6183 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jun 23, 2026
- Updated
- Jun 23, 2026