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
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license: apache-2.0
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license: apache-2.0
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language:
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- en
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base_model: BAAI/bge-small-en
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pipeline_tag: feature-extraction
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library_name: transformers
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tags:
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- embeddings
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- finetune
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