Instructions to use ts-goonj/bhasha-embed-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ts-goonj/bhasha-embed-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ts-goonj/bhasha-embed-v0") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
bhasha-embed-v0 (int8 OpenVINO)
Fork of AkshitaS/bhasha-embed-v0 (Apache 2.0, developed by Akshita Sukhlecha), with a pre-converted int8-quantized OpenVINO artifact added for faster inference.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"ts-goonj/bhasha-embed-v0",
revision="<pin-a-commit-sha>",
backend="openvino",
model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"},
)
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