Instructions to use Bakhteyar/Balochi-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bakhteyar/Balochi-Model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Bakhteyar/Balochi-Model") model = AutoModelForSeq2SeqLM.from_pretrained("Bakhteyar/Balochi-Model") - Notebooks
- Google Colab
- Kaggle
en-bal-latin-v2
This model is a fine-tuned version of shehzadkhalid04/balochi-latani on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1554
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3135 | 1.0 | 899 | 0.1732 |
| 0.1672 | 2.0 | 1798 | 0.1649 |
| 0.1384 | 3.0 | 2697 | 0.1590 |
| 0.1192 | 4.0 | 3596 | 0.1560 |
| 0.1058 | 5.0 | 4495 | 0.1554 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
- Downloads last month
- 184
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Bakhteyar/Balochi-Model
Base model
shehzadkhalid04/balochi-latani