Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use Ransaka/sinhala-roberta-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ransaka/sinhala-roberta-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ransaka/sinhala-roberta-sentence-transformer") sentences = [ "වී අස්වැන්න අඩුවී තිබෙනවා", "මෙවර වී අස්වැන්න සාර්ථක නැහැ", "පොහොර ලැබී ඇති නිසා වී වගාව සාර්ථක විය හැකිය", "මෙවර වී වගාව එතරම් සාර්ථක නැති බව ගොවි මහතා පැවසීය", "වී වගාව තරමක් සාර්ථක බව පේනවා" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Transformers
How to use Ransaka/sinhala-roberta-sentence-transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-roberta-sentence-transformer") model = AutoModel.from_pretrained("Ransaka/sinhala-roberta-sentence-transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Pushing sample model
Browse filesModel trained on crafter triplets
- tokenizer.json +0 -0
tokenizer.json
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