| language: si | |
| tags: | |
| - SinhalaBERTo | |
| - Sinhala | |
| - roberta | |
| datasets: | |
| - oscar | |
| ### Overview | |
| This is a slightly smaller model trained on [OSCAR](https://oscar-corpus.com/) Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks. | |
| ## Model Specification | |
| The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications: | |
| 1. vocab_size=52000 | |
| 2. max_position_embeddings=514 | |
| 3. num_attention_heads=12 | |
| 4. num_hidden_layers=6 | |
| 5. type_vocab_size=1 | |
| ## How to Use | |
| You can use this model directly with a pipeline for masked language modeling: | |
| ```py | |
| from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline | |
| model = BertForMaskedLM.from_pretrained("keshan/SinhalaBERTo") | |
| tokenizer = BertTokenizer.from_pretrained("keshan/SinhalaBERTo") | |
| fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer) | |
| fill_mask("මම ගෙදර <mask>.") | |
| ``` | |