Instructions to use pqthinh232/HCMUS-vietnamese-correction-project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pqthinh232/HCMUS-vietnamese-correction-project with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pqthinh232/HCMUS-vietnamese-correction-project") model = AutoModelForSeq2SeqLM.from_pretrained("pqthinh232/HCMUS-vietnamese-correction-project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
HCMUS-vietnamese-correction-project
This model is a fine-tuned version of vinai/bartpho-syllable on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0213
- Bleu: 89.3574
- Cer: 0.0196
- Wer: 0.0563
- F1: 0.9826
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Cer | Wer | F1 |
|---|---|---|---|---|---|---|---|
| 0.3907 | 1.0 | 782 | 0.0338 | 83.8387 | 0.0343 | 0.0892 | 0.9626 |
| 0.0355 | 2.0 | 1564 | 0.0240 | 87.6565 | 0.0244 | 0.0669 | 0.9763 |
| 0.0278 | 3.0 | 2346 | 0.0225 | 88.6311 | 0.0216 | 0.0605 | 0.9804 |
| 0.021 | 4.0 | 3128 | 0.0220 | 88.8898 | 0.0200 | 0.0580 | 0.9821 |
| 0.0187 | 5.0 | 3910 | 0.0213 | 89.3574 | 0.0196 | 0.0563 | 0.9826 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
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