Instructions to use Kashif786/mbert-base-sindhi-cpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kashif786/mbert-base-sindhi-cpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kashif786/mbert-base-sindhi-cpt")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kashif786/mbert-base-sindhi-cpt") model = AutoModelForMaskedLM.from_pretrained("Kashif786/mbert-base-sindhi-cpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mbert-base-sindhi-cpt
This model is a fine-tuned version of Kashif786/mbert-base-sindhi-cpt on an unknown dataset.
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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- 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: cosine
- num_epochs: 1
Framework versions
- Transformers 5.16.1
- Pytorch 2.8.0+cu129
- Datasets 5.0.1
- Tokenizers 0.23.2
- Downloads last month
- 33
Model tree for Kashif786/mbert-base-sindhi-cpt
Unable to build the model tree, the base model loops to the model itself. Learn more.