Feature Extraction
Model2Vec
Safetensors
sentence-transformers
SentenceTransformer
embeddings
static-embeddings
multilingual
Instructions to use amgix/static-retrieval-multilingual-69m-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use amgix/static-retrieval-multilingual-69m-v1 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("amgix/static-retrieval-multilingual-69m-v1") - sentence-transformers
How to use amgix/static-retrieval-multilingual-69m-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("amgix/static-retrieval-multilingual-69m-v1") 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
- Xet hash:
- 2632dfa60662e16f9c9c661e5318cdb4f1405d75ac50f734abcc612b2fbf4a3f
- Size of remote file:
- 10.6 MB
- SHA256:
- 33b9cb8d53183b4c33076e91d1f6578f4d9990ef6657bb68ccb3386a4aa7349f
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