Update some cases of UKPLab not yet being replaced by huggingface

tomaarsen · tomaarsen · commit 1dfbe7342a3f · 2025-11-21T17:24:08.000+01:00
diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 <!--- BADGES: START --->
 
 [![HF Models](https://img.shields.io/badge/%F0%9F%A4%97-models-yellow)](https://huggingface.co/models?library=sentence-transformers)
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+[![GitHub - License](https://img.shields.io/github/license/huggingface/sentence-transformers?logo=github&style=flat&color=green)][#github-license]
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diff --git a/docs/_static/js/custom.js b/docs/_static/js/custom.js
diff --git a/docs/conf.py b/docs/conf.py
@@ -105,7 +105,7 @@
 html_show_sourcelink = False
 html_context = {
     "display_github": True,
-    "github_user": "UKPLab",
+    "github_user": "huggingface",
     "github_repo": "sentence-transformers",
     "github_version": "main/",
 }
diff --git a/examples/sentence_transformer/applications/retrieve_rerank/README.md b/examples/sentence_transformer/applications/retrieve_rerank/README.md
@@ -32,7 +32,7 @@ The advantage of Cross-Encoders is the higher performance, as they perform atten
 
 ## Example Scripts
 
-- **[retrieve_rerank_simple_wikipedia.ipynb](retrieve_rerank_simple_wikipedia.ipynb)** [ [Colab Version](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/main/examples/sentence_transformer/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]: This script uses the smaller [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page) as document collection to provide answers to user questions / search queries. First, we split all Wikipedia articles into paragraphs and encode them with a bi-encoder. If a new query / question is entered, it is encoded by the same bi-encoder and the paragraphs with the highest cosine-similarity are retrieved (see [semantic search](../semantic-search/README.md)). Next, the retrieved candidates are scored by a Cross-Encoder re-ranker and the 5 passages with the highest score from the Cross-Encoder are presented to the user.
+- **[retrieve_rerank_simple_wikipedia.ipynb](retrieve_rerank_simple_wikipedia.ipynb)** [ [Colab Version](https://colab.research.google.com/github/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]: This script uses the smaller [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page) as document collection to provide answers to user questions / search queries. First, we split all Wikipedia articles into paragraphs and encode them with a bi-encoder. If a new query / question is entered, it is encoded by the same bi-encoder and the paragraphs with the highest cosine-similarity are retrieved (see [semantic search](../semantic-search/README.md)). Next, the retrieved candidates are scored by a Cross-Encoder re-ranker and the 5 passages with the highest score from the Cross-Encoder are presented to the user.
 
 * **[in_document_search_crossencoder.py](in_document_search_crossencoder.py):** If you only have a small set of paragraphs, we don't do the retrieval stage. This is for example the case if you want to perform search within a single document. In this example, we take the Wikipedia article about Europe and split it into paragraphs. Then, the search query / question and all paragraphs are scored using the Cross-Encoder re-ranker. The most relevant passages for the query are returned.
 
diff --git a/examples/sentence_transformer/applications/semantic-search/README.md b/examples/sentence_transformer/applications/semantic-search/README.md
@@ -158,4 +158,4 @@ We list a handful of common use cases:
 
 [semantic_search_wikipedia_qa.py](semantic_search_wikipedia_qa.py) [ [Colab Version](https://colab.research.google.com/drive/11GunvCqJuebfeTlgbJWkIMT0xJH6PWF1?usp=sharing) ]: This example uses a model that was trained on the [Natural Questions dataset](https://huggingface.co/datasets/sentence-transformers/natural-questions). It consists of about 100k real Google search queries, together with an annotated passage from Wikipedia that provides the answer. It is an example of an **asymmetric search task**. As corpus, we use the smaller [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page) so that it fits easily into memory.
 
-[retrieve_rerank_simple_wikipedia.ipynb](../retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) [ [Colab Version](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/main/examples/sentence_transformer/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]: This script uses the [Retrieve & Re-rank](../retrieve_rerank/README.md) strategy and is an example for an **asymmetric search task**. We split all Wikipedia articles into paragraphs and encode them with a bi-encoder. If a new query / question is entered, it is encoded by the same bi-encoder and the paragraphs with the highest cosine-similarity are retrieved. Next, the retrieved candidates are scored by a Cross-Encoder re-ranker and the 5 passages with the highest score from the Cross-Encoder are presented to the user. We use models that were trained on the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset, a dataset with about 500k real queries from Bing search.
+[retrieve_rerank_simple_wikipedia.ipynb](../retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) [ [Colab Version](https://colab.research.google.com/github/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]: This script uses the [Retrieve & Re-rank](../retrieve_rerank/README.md) strategy and is an example for an **asymmetric search task**. We split all Wikipedia articles into paragraphs and encode them with a bi-encoder. If a new query / question is entered, it is encoded by the same bi-encoder and the paragraphs with the highest cosine-similarity are retrieved. Next, the retrieved candidates are scored by a Cross-Encoder re-ranker and the 5 passages with the highest score from the Cross-Encoder are presented to the user. We use models that were trained on the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset, a dataset with about 500k real queries from Bing search.
diff --git a/examples/sparse_encoder/applications/retrieve_rerank/README.md b/examples/sparse_encoder/applications/retrieve_rerank/README.md
@@ -13,7 +13,7 @@ This approach combines the efficiency of first-stage retrieval with the accuracy
 
 ## Interactive Demo: Simple Wikipedia Search
 
-**File**: [retrieve_rerank_simple_wikipedia.ipynb](retrieve_rerank_simple_wikipedia.ipynb) [ [Colab Version](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/main/examples/sparse_encoder/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]
+**File**: [retrieve_rerank_simple_wikipedia.ipynb](retrieve_rerank_simple_wikipedia.ipynb) [ [Colab Version](https://colab.research.google.com/github/huggingface/sentence-transformers/blob/main/examples/sparse_encoder/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]
 
 This Jupyter notebook provides an interactive demonstration of retrieve & re-rank over [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page) as corpus. The example allows you to:
 

Original file line number	Diff line number	Diff line change
`@@ -105,7 +105,7 @@`
`105`	`105`	`html_show_sourcelink = False`
`106`	`106`	`html_context = {`
`107`	`107`	`"display_github": True,`
`108`		`- "github_user": "UKPLab",`
	`108`	`+ "github_user": "huggingface",`
`109`	`109`	`"github_repo": "sentence-transformers",`
`110`	`110`	`"github_version": "main/",`
`111`	`111`	`}`
Original file line number	Diff line number	Diff line change
`@@ -158,4 +158,4 @@ We list a handful of common use cases:`
`158`	`158`
`159`	`159`	[semantic_search_wikipedia_qa.py](semantic_search_wikipedia_qa.py) [ [Colab Version](https://colab.research.google.com/drive/11GunvCqJuebfeTlgbJWkIMT0xJH6PWF1?usp=sharing) ]: This example uses a model that was trained on the [Natural Questions dataset](https://huggingface.co/datasets/sentence-transformers/natural-questions). It consists of about 100k real Google search queries, together with an annotated passage from Wikipedia that provides the answer. It is an example of an asymmetric search task. As corpus, we use the smaller [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page) so that it fits easily into memory.
`160`	`160`
`161`		-[retrieve_rerank_simple_wikipedia.ipynb](../retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) [ [Colab Version](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/main/examples/sentence_transformer/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]: This script uses the [Retrieve & Re-rank](../retrieve_rerank/README.md) strategy and is an example for an asymmetric search task. We split all Wikipedia articles into paragraphs and encode them with a bi-encoder. If a new query / question is entered, it is encoded by the same bi-encoder and the paragraphs with the highest cosine-similarity are retrieved. Next, the retrieved candidates are scored by a Cross-Encoder re-ranker and the 5 passages with the highest score from the Cross-Encoder are presented to the user. We use models that were trained on the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset, a dataset with about 500k real queries from Bing search.
	`161`	+[retrieve_rerank_simple_wikipedia.ipynb](../retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) [ [Colab Version](https://colab.research.google.com/github/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) ]: This script uses the [Retrieve & Re-rank](../retrieve_rerank/README.md) strategy and is an example for an asymmetric search task. We split all Wikipedia articles into paragraphs and encode them with a bi-encoder. If a new query / question is entered, it is encoded by the same bi-encoder and the paragraphs with the highest cosine-similarity are retrieved. Next, the retrieved candidates are scored by a Cross-Encoder re-ranker and the 5 passages with the highest score from the Cross-Encoder are presented to the user. We use models that were trained on the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset, a dataset with about 500k real queries from Bing search.