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 model.safetensors from Modularcomputing/Native-Bird: direct link, hf CLI and curl.
- Browser
- Download file 1.88 GB
-
https://huggingface.co/Modularcomputing/Native-Bird/resolve/main/model.safetensors
- Command line
-
hf download hf://Modularcomputing/Native-Bird/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Modularcomputing/Native-Bird/resolve/main/model.safetensors
1.88 GB
- Xet hash:
- 658866c167b1893b4ca6452378602ec39b02635ca1235303897f8a41d03da68e
- Size of remote file:
- 1.88 GB
- SHA256:
- cb68b8f24b63156c6faa4dde3c178f31e0ab942a09a361c73bc6cb9ade8eb935
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