Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
feature-extraction
text-embeddings-inference
Instructions to use vec-ai/lychee-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vec-ai/lychee-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vec-ai/lychee-embed") 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 vec-ai/lychee-embed with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vec-ai/lychee-embed") model = AutoModel.from_pretrained("vec-ai/lychee-embed") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- a14ccbb8ca8f4eb9545596606d20655c9b2a17cd53335f944c1da14a77c3f0bb
- Size of remote file:
- 225 kB
- SHA256:
- eade35b0f8eca610087da421fef85075547a7cd0e5636a4b364d61fa3e092341
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