Sentence Similarity
sentence-transformers
Safetensors
Transformers
Vietnamese
gte
feature-extraction
phobert
vietnamese
sentence-embedding
custom_code
Instructions to use longsteel/embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use longsteel/embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("longsteel/embedding", trust_remote_code=True) 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] - Transformers
How to use longsteel/embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("longsteel/embedding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c61862a1d9f43a3f76eae5cb18b43fddddc1277bec41bbc0aa8df16676ee449d
- Size of remote file:
- 17.1 MB
- SHA256:
- aa7a6ad87a7ce8fe196787355f6af7d03aee94d19c54a5eb1392ed18c8ef451a
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