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
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
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