Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
colbert
late-interaction
retrieval
pylate
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
Fix serialization so the model loads with public PyLate 1.6.0 (metadata only; weights unchanged)
Browse files- 2_Dense/config.json +0 -2
2_Dense/config.json
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@@ -3,7 +3,5 @@
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"out_features": 768,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity",
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"module_input_name": "token_embeddings",
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"module_output_name": "token_embeddings",
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"use_residual": true
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}
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"out_features": 768,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity",
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"use_residual": true
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}
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