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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SproutsAI/embedding-model") model = AutoModel.from_pretrained("SproutsAI/embedding-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SproutsAI/embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/SproutsAI/embedding-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SproutsAI/embedding-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SproutsAI/embedding-model/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- 5133a79f3c0de4f875b3d1aab125feadf91403c4f1563a21491db80cd397b8f7
- Size of remote file:
- 134 MB
- SHA256:
- 2a1795b421ebb3d1d2be825b21e4fa836e1bbec02efc94a5583d94d5861d8135
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