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
Update README.md
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README.md
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@@ -68,6 +68,29 @@ computed by summing, over query tokens, the maximum similarity to a document tok
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| [LateOn](https://huggingface.co/lightonai/LateOn) | 57.22 | 149 | 128 | 50.52 | 47.36 | **39.67** | 45.99 | 92.02 | **53.12** | 79.98 | 45.67 | 37.79 | 63.91 | 89.67 | **21.90** | 76.61 | 83.60 | 30.52 |
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| GLInt | **57.43** | 149 | 128 | 52.38 | 46.49 | 34.17 | **47.68** | **92.45** | 50.85 | **82.54** | 46.38 | 37.51 | **68.03** | **90.08** | 20.65 | **77.13** | 84.78 | 30.26 |
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## Training data and reproducibility
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The corresponding private training artifacts are in
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| [LateOn](https://huggingface.co/lightonai/LateOn) | 57.22 | 149 | 128 | 50.52 | 47.36 | **39.67** | 45.99 | 92.02 | **53.12** | 79.98 | 45.67 | 37.79 | 63.91 | 89.67 | **21.90** | 76.61 | 83.60 | 30.52 |
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| GLInt | **57.43** | 149 | 128 | 52.38 | 46.49 | 34.17 | **47.68** | **92.45** | 50.85 | **82.54** | 46.38 | 37.51 | **68.03** | **90.08** | 20.65 | **77.13** | 84.78 | 30.26 |
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### BEIR-Decontaminated (14 datasets, NDCG@10)
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| Model | Average | ArguAna | ClimateFEVER | DBPedia | FEVER | FiQA2018 | HotpotQA | MS MARCO | NFCorpus | Natural Questions | Quora | SciDocs | SciFact | TREC-COVID | Touché-2020 |
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|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
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| **GLINT-base (ours)** | **62.50** | 51.67 | 36.35 | 42.50 | 92.89 | 56.88 | **81.16** | 72.70 | 26.21 | **94.97** | **92.06** | **22.02** | **89.07** | 81.51 | 34.97 |
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| LateOn | 61.4 | 52.2 | 42.1 | 31.7 | 92.7 | **57.9** | 78.9 | 70.3 | 27.0 | 93.1 | 91.5 | 15.1 | 88.9 | 80.9 | 36.8 |
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| DenseOn | 58.8 | 40.0 | 39.5 | 28.8 | 91.2 | 55.9 | 73.7 | 68.9 | 28.5 | 92.1 | 91.1 | 14.7 | 85.4 | 82.5 | 31.0 |
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| pplx-embed-v1-0.6b | 59.7 | 43.7 | 42.4 | 28.4 | 91.1 | 55.2 | 73.5 | 71.9 | 28.0 | 91.6 | 91.5 | 15.4 | 89.0 | 83.7 | 30.0 |
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| jina-v5-text-nano | 58.8 | 47.2 | 41.6 | 30.2 | 90.0 | 51.5 | 67.5 | 68.6 | 29.4 | 92.3 | 91.3 | 14.9 | 89.4 | 76.8 | 33.2 |
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| harrier-oss-v1-0.6b | 58.0 | 47.4 | 25.7 | 31.3 | 80.7 | 50.1 | 71.4 | **73.4** | 27.9 | 90.0 | 90.9 | 17.1 | **90.7** | 81.8 | 33.3 |
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| arctic-embed-l-v2 | 57.9 | 43.1 | **45.7** | **45.7** | 92.2 | 50.4 | 63.1 | 71.0 | 26.0 | 90.7 | 91.3 | 13.9 | 87.4 | 81.4 | 26.8 |
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| bge-large-en-v1.5 | 57.3 | 46.0 | 39.0 | 28.9 | 87.6 | 49.3 | 75.2 | 68.9 | **29.8** | 85.9 | 91.3 | 14.0 | 86.5 | 72.7 | 26.9 |
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| Qwen3-Embedding-0.6B | 57.0 | 48.4 | 38.0 | 25.3 | 86.4 | 49.1 | 62.2 | 63.6 | 25.8 | 88.3 | 90.0 | 15.3 | 85.5 | **87.9** | 31.8 |
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| GTE-ModernBERT | 56.6 | 52.5 | 47.5 | 25.9 | **94.1** | 55.5 | 65.5 | 64.8 | 26.1 | 84.5 | 90.8 | 11.6 | 88.6 | 62.4 | 23.1 |
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| bge-base-en-v1.5 | 56.2 | 45.6 | 32.9 | 26.7 | 86.8 | 44.5 | 72.7 | 66.8 | 27.4 | 85.6 | 91.1 | 13.8 | 87.6 | 76.6 | 28.1 |
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| Nomic v1.5 | 55.9 | 35.8 | 43.5 | 28.8 | 86.8 | 44.7 | 72.7 | 67.4 | 24.4 | 85.1 | 87.2 | 12.7 | 83.3 | 80.7 | 29.4 |
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| modernbert-embed-base | 55.6 | 36.5 | 37.8 | 24.7 | 87.8 | 46.0 | 62.7 | 65.3 | 24.3 | 89.3 | 89.9 | 12.9 | 85.5 | 82.7 | 33.1 |
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| ColBERT-Zero | 60.0 | 54.5 | 36.8 | 33.0 | 90.5 | 46.6 | 77.8 | **74.2** | 26.6 | 91.1 | 88.3 | 14.2 | 89.5 | 75.3 | **40.9** |
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| pplx-embed-v1-late-0.6b | 59.8 | **60.9** | 36.4 | 29.9 | 89.7 | 50.9 | 78.6 | 69.2 | 27.9 | 92.8 | 83.8 | 13.5 | 89.3 | 80.2 | 34.7 |
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| GTE-ModernColBERT | 59.3 | 48.8 | 33.5 | 33.2 | 88.1 | 50.2 | 77.3 | 71.6 | 27.3 | 93.1 | 89.1 | 13.6 | 87.7 | 81.4 | 35.3 |
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| colbert-small | 58.1 | 47.7 | 35.7 | 31.7 | 89.3 | 45.6 | 77.1 | 71.4 | 25.0 | 86.2 | 90.1 | 13.1 | 89.2 | 81.5 | 29.0 |
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## Training data and reproducibility
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The corresponding private training artifacts are in
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