metadata
language: si
tags:
- SinhalaBERTo
- Sinhala
- roberta
datasets:
- oscar
Overview
This is a slightly smaller model trained on OSCAR 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 with the following specifications:
- vocab_size=52000
- max_position_embeddings=514
- num_attention_heads=12
- num_hidden_layers=6
- type_vocab_size=1
How to Use
You can use this model directly with a pipeline for masked language modeling:
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>.")