Python: Update sentence-transformers requirement from <5.0,>=2.2 to >=2.2,<6.0 in /python (#12679)

Updates the requirements on
[sentence-transformers](https://github.com/UKPLab/sentence-transformers)
to permit the latest version.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/UKPLab/sentence-transformers/releases">sentence-transformers's
releases</a>.</em></p>
<blockquote>
<h2>v5.0.0 - SparseEncoder support; encode_query &amp; encode_document;
multi-processing in encode; Router; and more</h2>
<p>This release consists of significant updates including the
introduction of Sparse Encoder models, new methods
<code>encode_query</code> and <code>encode_document</code>,
multi-processing support in <code>encode</code>, the <code>Router</code>
module for asymmetric models, custom learning rates for parameter
groups, composite loss logging, and various small improvements and bug
fixes.</p>
<p>Install this version with</p>
<pre lang="bash"><code># Training + Inference
pip install sentence-transformers[train]==5.0.0
<h1>Inference only, use one of:</h1>
<p>pip install sentence-transformers==5.0.0
pip install sentence-transformers[onnx-gpu]==5.0.0
pip install sentence-transformers[onnx]==5.0.0
pip install sentence-transformers[openvino]==5.0.0
</code></pre></p>
<blockquote>
<p>[!TIP]
Our <a href="https://huggingface.co/blog/train-sparse-encoder">Training
and Finetuning Sparse Embedding Models with Sentence Transformers v5
blogpost</a> is an excellent place to learn about finetuning sparse
embedding models!</p>
</blockquote>
<blockquote>
<p>[!NOTE]
This release is designed to be fully backwards compatible, meaning that
you should be able to upgrade from older versions to v5.x without any
issues. If you are running into issues when upgrading, feel free to open
<a href="https://github.com/uKPLab/sentence-transformers/issues/new">an
issue</a>. Also see the <a
href="https://sbert.net/docs/migration_guide.html">Migration Guide</a>
for changes that we would recommend.</p>
</blockquote>
<h2>Sparse Encoder models</h2>
<p>The Sentence Transformers v5.0 release introduces Sparse Embedding
models, also known as Sparse Encoders. These models generate
high-dimensional embeddings, often with 30,000+ dimensions, where often
only &lt;1% of dimensions are non-zero. This is in contrast to the
standard dense embedding models, which produce low-dimensional
embeddings (e.g., 384, 768, or 1024 dimensions) where all values are
non-zero.</p>
<p>Usually, each active dimension (i.e. the dimension with a non-zero
value) in a sparse embedding corresponds to a specific token in the
model's vocabulary, allowing for interpretability. This means that you
can e.g. see exactly which words/tokens are important in an embedding,
and that you can inspect exactly because of which words/tokens two texts
are deemed similar.</p>
<p>Let's have a look at <a
href="https://huggingface.co/naver/splade-v3">naver/splade-v3</a>, a
strong sparse embedding model, as an example:</p>
<pre lang="python"><code>from sentence_transformers import SparseEncoder
<h1>Download from the 🤗 Hub</h1>
<p>model = SparseEncoder(&quot;naver/splade-v3&quot;)</p>
<h1>Run inference</h1>
<p>sentences = [
&quot;The weather is lovely today.&quot;,
&quot;It's so sunny outside!&quot;,
&quot;He drove to the stadium.&quot;,
]
embeddings = model.encode(sentences)
print(embeddings.shape)</p>
<h1>(3, 30522)</h1>
<h1>Get the similarity scores for the embeddings</h1>
<p>similarities = model.similarity(embeddings, embeddings)
print(similarities)</p>
<h1>tensor([[   32.4323,     5.8528,     0.0258],</h1>
<p>&lt;/tr&gt;&lt;/table&gt;
</code></pre></p>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/8dc0fca767afd74208764fd28f89dd83e8743241"><code>8dc0fca</code></a>
Release v5.0.0</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/e91af6ac3e160aa0d610ea32aeb295f3ff648e67"><code>e91af6a</code></a>
Update links for SPLADE and Inference-Free SPLADE models collections in
docum...</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/4c00aea59d4b25061ba03761804a4266c3e8fd33"><code>4c00aea</code></a>
[<code>fix</code>] Remove hub_kwargs in SparseStaticEmbedding.from_json
in favor of more...</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/28685bb1d771b8609c5e4d4115a8b862267358ce"><code>28685bb</code></a>
Clean up gitignore (<a
href="https://redirect.github.com/UKPLab/sentence-transformers/issues/3409">#3409</a>)</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/85dd17518f15a8466ec131a4f82e1e93e4ada631"><code>85dd175</code></a>
Fix formatting of docstring arguments in
SpladeRegularizerWeightSchedulerCall...</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/14afc4b6681f0b83bded05fe91a8fd3320d453f9"><code>14afc4b</code></a>
Merge PR <a
href="https://redirect.github.com/UKPLab/sentence-transformers/issues/3401">#3401</a>:
[<code>v5</code>] Add support for Sparse Embedding models</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/2d248419eb47c3710d50fa560d41e7fd17459846"><code>2d24841</code></a>
Update tip phrasing and fix links</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/ed043c56b21d4a6c8f600e9d2a74523e522d6823"><code>ed043c5</code></a>
typo</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/b2679d12294f9e0e5a2250cbcb400e9b4111f856"><code>b2679d1</code></a>
fix broken link</li>
<li><a
href="https://github.com/UKPLab/sentence-transformers/commit/d30341ec2f3a8fa50eb41f924273e414bb7c0db7"><code>d30341e</code></a>
Update tips to prepared for v5.0</li>
<li>Additional commits viewable in <a
href="https://github.com/UKPLab/sentence-transformers/compare/v2.2.0...v5.0.0">compare
view</a></li>
</ul>
</details>
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python/pyproject.toml

@@ -93,7 +93,7 @@ google = [
 ]
 hugging_face = [
     "transformers[torch] ~= 4.28",
-    "sentence-transformers >= 2.2,< 5.0",
+    "sentence-transformers >= 2.2,< 6.0",
     "torch == 2.7.1"
 ]
 mcp = [