Sentence Similarity
ONNX
sentence-transformers
light-embed
bert
feature-extraction
text-embeddings-inference
Instructions to use LightEmbed/baai-llm-embedder-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LightEmbed/baai-llm-embedder-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LightEmbed/baai-llm-embedder-onnx") 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] - Notebooks
- Google Colab
- Kaggle
nguyenthaibinh commited on
Commit ·
14f5784
1
Parent(s): 4b3784e
add base model name
Browse files- model_info.json +1 -0
model_info.json
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{
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"description": "BAI embeeing model, BAAI/llm-embedder",
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"dim": 768,
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"max_seq_length": 512
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}
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{
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"description": "BAI embeeing model, BAAI/llm-embedder",
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"base_model": "BAAI/llm-embedder",
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"dim": 768,
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"max_seq_length": 512
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}
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