Sentence Similarity
sentence-transformers
Safetensors
Transformers
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Instructions to use Lajavaness/bilingual-embedding-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lajavaness/bilingual-embedding-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lajavaness/bilingual-embedding-base", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Lajavaness/bilingual-embedding-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lajavaness/bilingual-embedding-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Lajavaness/bilingual-embedding-base: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Lajavaness/bilingual-embedding-base/resolve/main/model.safetensors
- Command line
-
hf download hf://Lajavaness/bilingual-embedding-base/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Lajavaness/bilingual-embedding-base/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- 8691c4a3fbda68e08588cc73567170f88a3fe7c0ecd68209fdb74661a4780c03
- Size of remote file:
- 1.11 GB
- SHA256:
- 4945b6ad436c8df3dc4be3d64f4b0fa62470eb432e5c35f68e6fdd9dc4c3c594
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