Instructions to use NIRVLab/bartede with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NIRVLab/bartede with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NIRVLab/bartede") model = AutoModelForSeq2SeqLM.from_pretrained("NIRVLab/bartede") - Notebooks
- Google Colab
- Kaggle
File size: 1,877 Bytes
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library_name: transformers
license: mit
base_model: vinai/bartpho-syllable
tags:
- generated_from_trainer
model-index:
- name: bartede
results: []
---
<!-- 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. -->
# bartede
This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5850
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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
- lr_scheduler_warmup_steps: 0.06
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.9411 | 1.0 | 158 | 0.8584 |
| 5.7966 | 2.0 | 316 | 0.6884 |
| 4.6457 | 3.0 | 474 | 0.6328 |
| 4.1820 | 4.0 | 632 | 0.6210 |
| 3.8281 | 5.0 | 790 | 0.5837 |
| 2.7451 | 6.0 | 948 | 0.5861 |
| 2.3142 | 7.0 | 1106 | 0.6141 |
| 1.8199 | 8.0 | 1264 | 0.6588 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
|