Instructions to use FiveC/VieBahnar-DeleteOriginal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/VieBahnar-DeleteOriginal with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/VieBahnar-DeleteOriginal") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/VieBahnar-DeleteOriginal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: mit
base_model: IAmSkyDra/BARTBana_v5
tags:
- generated_from_trainer
metrics:
- sacrebleu
model-index:
- name: VieBahnar-DeleteOriginal
results: []
VieBahnar-DeleteOriginal
This model is a fine-tuned version of IAmSkyDra/BARTBana_v5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0218
- Sacrebleu: 1.9395
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Sacrebleu |
|---|---|---|---|---|
| 0.4697 | 1.0 | 13874 | 1.7581 | 1.5718 |
| 0.2864 | 2.0 | 27748 | 1.9640 | 1.8953 |
| 0.2377 | 3.0 | 41622 | 2.0218 | 1.9395 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1