Instructions to use IAmSkyDra/BARTBana_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IAmSkyDra/BARTBana_v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("IAmSkyDra/BARTBana_v2") model = AutoModelForSeq2SeqLM.from_pretrained("IAmSkyDra/BARTBana_v2") - Notebooks
- Google Colab
- Kaggle
BARTBana
This model is a fine-tuned version of vinai/bartpho-syllable on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6184
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1847 | 1.0 | 13319 | 1.0153 |
| 0.8204 | 2.0 | 26638 | 0.7092 |
| 0.7208 | 3.0 | 39957 | 0.6224 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
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