Update sentence-transformers requirement from <3.5.0,>=3.4.0 to >=3.4.0,<5.1.0

[dependabot]

Updates the requirements on sentence-transformers to permit the latest version.

Release notes

Sourced from sentence-transformers's releases.

v5.0.0 - SparseEncoder support; encode_query & encode_document; multi-processing in encode; Router; and more

This release consists of significant updates including the introduction of Sparse Encoder models, new methods encode_query and encode_document, multi-processing support in encode, the Router module for asymmetric models, custom learning rates for parameter groups, composite loss logging, and various small improvements and bug fixes.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.0.0
Inference only, use one of:
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

[!TIP] Our Training and Finetuning Sparse Embedding Models with Sentence Transformers v5 blogpost is an excellent place to learn about finetuning sparse embedding models!

[!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 an issue. Also see the Migration Guide for changes that we would recommend.

Sparse Encoder models

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 <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.

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.

Let's have a look at naver/splade-v3, a strong sparse embedding model, as an example:

from sentence_transformers import SparseEncoder
Download from the 🤗 Hub
model = SparseEncoder("naver/splade-v3")
Run inference
sentences = [
"The weather is lovely today.",
"It's so sunny outside!",
"He drove to the stadium.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
(3, 30522)
Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
tensor([[   32.4323,     5.8528,     0.0258],
</tr></table>

... (truncated)

Commits

• 8dc0fca Release v5.0.0
• e91af6a Update links for SPLADE and Inference-Free SPLADE models collections in docum...
• 4c00aea [fix] Remove hub_kwargs in SparseStaticEmbedding.from_json in favor of more...
• 28685bb Clean up gitignore (#3409)
• 85dd175 Fix formatting of docstring arguments in SpladeRegularizerWeightSchedulerCall...
• 14afc4b Merge PR #3401: [v5] Add support for Sparse Embedding models
• 2d24841 Update tip phrasing and fix links
• ed043c5 typo
• b2679d1 fix broken link
• d30341e Update tips to prepared for v5.0
• Additional commits viewable in compare view

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[dependabot]

Superseded by #433.