Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
Chinese
qwen3
feature-extraction
text-embeddings-inference
binary code
binary
Instructions to use XingTuLab/BinSeek-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use XingTuLab/BinSeek-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("XingTuLab/BinSeek-Embedding") 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 XingTuLab/BinSeek-Embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("XingTuLab/BinSeek-Embedding") model = AutoModel.from_pretrained("XingTuLab/BinSeek-Embedding") - Notebooks
- Google Colab
- Kaggle
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license: gpl-3.0
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license: gpl-3.0
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language:
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- en
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- zh
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pipeline_tag: sentence-similarity
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tags:
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- transformers
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- sentence-transformers
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- text-embeddings-inference
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- binary code
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- binary
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- sentence-similarity
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- feature-extraction
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