Instructions to use Ransaka/SinhalaRoberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ransaka/SinhalaRoberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ransaka/SinhalaRoberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Ransaka/SinhalaRoberta") model = AutoModelForMaskedLM.from_pretrained("Ransaka/SinhalaRoberta") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - MLM | |
| model-index: | |
| - name: RobertaSin | |
| results: [] | |
| widget: | |
| - text: අපි තමයි [MASK] කරේ. | |
| - text: මට හෙට එන්න වෙන්නේ [MASK]. | |
| - text: අපි ගෙදර [MASK]. | |
| - text: සිංහල සහ [MASK] අලුත් අවුරුද්ද. | |
| license: apache-2.0 | |
| language: | |
| - si | |
| # SinhalaRoberta - Pretrained Roberta for Sinhala MLM tasks. | |
| This model is trained on various Sinhala corpus extracted from News and articles. | |
| ## Model description | |
| Trained on MLM tasks, Please use [MASK] token to indicate masked token. The model comprises a total of 68 million parameters | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - Pytorch 1.13.0 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.13.2 |