Feature Extraction
sentence-transformers
ONNX
Sanskrit
English
gemma3_text
embedding
sanskrit
rigveda
multilingual
text-embeddings-inference
Instructions to use bsbarkur/rigveda-onnx-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bsbarkur/rigveda-onnx-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bsbarkur/rigveda-onnx-model") 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
Download .gitattributes from bsbarkur/rigveda-onnx-model: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/bsbarkur/rigveda-onnx-model/resolve/main/.gitattributes
- Command line
-
hf download hf://bsbarkur/rigveda-onnx-model/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/bsbarkur/rigveda-onnx-model/resolve/main/.gitattributes
275 Bytes
| *.onnx filter=lfs diff=lfs merge=lfs -text | |
| *.tflite filter=lfs diff=lfs merge=lfs -text | |
| *.bin filter=lfs diff=lfs merge=lfs -text | |
| *.safetensors filter=lfs diff=lfs merge=lfs -text | |
| *.model filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |