Sentence Similarity
sentence-transformers
Safetensors
bert
embeddings
cross-lingual
multilingual
igbo
hausa
yoruba
information-retrieval
semantic-search
text-embeddings-inference
Instructions to use Modularcomputing/Native-Bird with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Modularcomputing/Native-Bird with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Modularcomputing/Native-Bird") 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 2_Dense/model.safetensors from Modularcomputing/Native-Bird: direct link, hf CLI and curl.
- Browser
- Download file 2.36 MB
-
https://huggingface.co/Modularcomputing/Native-Bird/resolve/main/2_Dense/model.safetensors
- Command line
-
hf download hf://Modularcomputing/Native-Bird/2_Dense/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Modularcomputing/Native-Bird/resolve/main/2_Dense/model.safetensors
2.36 MB
- Xet hash:
- b3cfeb6967ddd64350829df6c66472718393f44f7121e899552d154306407833
- Size of remote file:
- 2.36 MB
- SHA256:
- c7d0e218e49743fefa25884a33cdcdb52d2f119c693db1bd7e0d149fc5932c85
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