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
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license: apache-2.0
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pipeline_tag: sentence-similarity
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# Lychee Embed
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`Lychee-embed` is the latest generalist text embedding model developed based on the `Qwen2.5` basic model. It is suitable for text retrieval (semantic correlation), text similarity and other downstream tasks, and supports multiple languages of `Qwen2.5`.
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license: apache-2.0
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pipeline_tag: sentence-similarity
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base_model:
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- Qwen/Qwen2.5-1.5B
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tags:
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- transformers
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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---
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# Lychee Embed
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`Lychee-embed` is the latest generalist text embedding model developed based on the `Qwen2.5` basic model. It is suitable for text retrieval (semantic correlation), text similarity and other downstream tasks, and supports multiple languages of `Qwen2.5`.
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`Lychee-embed` is jointly developed by the NLP Team of Harbin Institute of Technology, Shenzhen and is built based on an innovative multi-stage training framework (warm-up, task-learning, model merging, annealing).
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The first batch of open source is 1.5B parameter version.
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