Instructions to use SproutsAI/embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SproutsAI/embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SproutsAI/embedding-model") 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 SproutsAI/embedding-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SproutsAI/embedding-model") model = AutoModel.from_pretrained("SproutsAI/embedding-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11d73011b73d7bacf824efb0fd31e1e6ba53ef1fd80fcec98b780c79bdd0c985
- Size of remote file:
- 133 MB
- SHA256:
- 978641a1877cddfbed88b1d9470ec702c578a92a1ac0257c0609a77e8482ef91
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