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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`jinaai/jina-reranker-v3.5` scores, temperature sharpening, false-negative masking, and an
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InfoNCE anchor.
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This is a private research release. It is a single checkpoint, not an ensemble or a re-ranker.
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## Usage
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```python
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`jinaai/jina-reranker-v3.5` scores, temperature sharpening, false-negative masking, and an
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InfoNCE anchor.
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## Usage
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```python
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