Instructions to use FiveC/BartTay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/BartTay with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/BartTay") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/BartTay", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,035 Bytes
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library_name: transformers
license: mit
base_model: vinai/bartpho-syllable
tags:
- generated_from_trainer
model-index:
- name: BartTay
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. -->
# BartTay
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: 4.7854
## 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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 46 | 5.9329 |
| No log | 2.0 | 92 | 5.7500 |
| No log | 3.0 | 138 | 5.6268 |
| No log | 4.0 | 184 | 5.4371 |
| No log | 5.0 | 230 | 5.1383 |
| No log | 6.0 | 276 | 5.0149 |
| No log | 7.0 | 322 | 4.9770 |
| No log | 8.0 | 368 | 4.9059 |
| No log | 9.0 | 414 | 4.7860 |
| No log | 10.0 | 460 | 4.6284 |
| 5.4791 | 11.0 | 506 | 4.6639 |
| 5.4791 | 12.0 | 552 | 4.6550 |
| 5.4791 | 13.0 | 598 | 4.7702 |
### Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 3.1.0
- Tokenizers 0.22.1
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