chungimungi
/

GLInt

@@ -13,14 +13,14 @@ license: apache-2.0
 pipeline_tag: sentence-similarity
 ---
-# GLINT
 GLINT is a 149M-parameter English late-interaction retriever built from
 [LateOn-unsupervised](https://huggingface.co/lightonai/LateOn-unsupervised). It retains
 128-dimensional token embeddings and uses MaxSim retrieval with 32 query tokens and 300
 document tokens.
-## What is new in GLINT?
 GLINT is designed around the mismatch between ordinary dense hard-negative mining and a
 late-interaction retriever. Dense mining selects documents that are difficult under one pooled
@@ -42,7 +42,7 @@ objective, while a false-negative-masked InfoNCE term preserves a direct retriev
 ```python
 from pylate import models
-model = models.ColBERT("chungimungi/GLINT-base")
 query_embeddings = model.encode(["what causes a lunar eclipse?"], is_query=True)
 document_embeddings = model.encode(
     ["A lunar eclipse happens when Earth passes between the Sun and the Moon."],
@@ -66,7 +66,7 @@ computed by summing, over query tokens, the maximum similarity to a document tok
 | [ColBERT-Zero](https://huggingface.co/lightonai/ColBERT-Zero) | 55.39 | 149 | 128 | **52.82** | 41.41 | 35.90 | 47.43 | 90.52 | 42.50 | 79.45 | 45.95 | 37.21 | 61.82 | 85.19 | 19.84 | 76.33 | 78.27 | **36.24** |
 | [LateOn-unsupervised](https://huggingface.co/lightonai/LateOn-unsupervised) | 50.11 | 149 | 128 | 43.12 | **47.71** | 18.76 | 43.36 | 65.74 | 51.94 | 68.17 | 37.51 | 37.15 | 58.41 | 89.48 | 21.13 | 76.89 | 69.81 | 22.53 |
 | [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 |
-| GLINT-base | **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 |
 ## Training data and reproducibility

 pipeline_tag: sentence-similarity
 ---
+# GLInt
 GLINT is a 149M-parameter English late-interaction retriever built from
 [LateOn-unsupervised](https://huggingface.co/lightonai/LateOn-unsupervised). It retains
 128-dimensional token embeddings and uses MaxSim retrieval with 32 query tokens and 300
 document tokens.
+## What is new in GLInt?
 GLINT is designed around the mismatch between ordinary dense hard-negative mining and a
 late-interaction retriever. Dense mining selects documents that are difficult under one pooled
 ```python
 from pylate import models
+model = models.ColBERT("chungimungi/GLInt")
 query_embeddings = model.encode(["what causes a lunar eclipse?"], is_query=True)
 document_embeddings = model.encode(
     ["A lunar eclipse happens when Earth passes between the Sun and the Moon."],
 | [ColBERT-Zero](https://huggingface.co/lightonai/ColBERT-Zero) | 55.39 | 149 | 128 | **52.82** | 41.41 | 35.90 | 47.43 | 90.52 | 42.50 | 79.45 | 45.95 | 37.21 | 61.82 | 85.19 | 19.84 | 76.33 | 78.27 | **36.24** |
 | [LateOn-unsupervised](https://huggingface.co/lightonai/LateOn-unsupervised) | 50.11 | 149 | 128 | 43.12 | **47.71** | 18.76 | 43.36 | 65.74 | 51.94 | 68.17 | 37.51 | 37.15 | 58.41 | 89.48 | 21.13 | 76.89 | 69.81 | 22.53 |
 | [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 |
+| 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 |
 ## Training data and reproducibility