Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
colbert
late-interaction
retrieval
pylate
multi-vector
text-embeddings-inference
Instructions to use chungimungi/GLInt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use chungimungi/GLInt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chungimungi/GLInt") 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
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README.md
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library_name: sentence-transformers
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tags:
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# GLINT-base
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## Limitations
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This is an English retrieval model. It is intended for research and offline evaluation; assess
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domain fit, bias, and source-data licensing before using it in a production search system.
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library_name: sentence-transformers
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tags:
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- colbert
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- late-interaction
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- retrieval
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- pylate
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datasets:
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- bisectgroup/hard-negatives-traversal
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- lightonai/embeddings-fine-tuning
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language:
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- en
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base_model:
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- lightonai/LateOn-unsupervised
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# GLINT-base
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## Limitations
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This is an English retrieval model. It is intended for research and offline evaluation; assess
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domain fit, bias, and source-data licensing before using it in a production search system.
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