chore(deps-dev): update sentence-transformers requirement from ~=2.6 to >=2.6,<7.0

[dependabot]

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

Release notes

Sourced from sentence-transformers's releases.

v6.0.0 - MultiVectorEncoder for ColBERT & late interaction models, transformers v5, float32 scoring, faster training & encoding

This major release introduces Multi-Vector Embedding models, also known as late interaction or ColBERT-style models, as a fourth model type alongside SentenceTransformer, CrossEncoder, and SparseEncoder. Going forward, you'll be able to use Sentence Transformers for training, inferencing, and interpreting Multi-Vector Embedding models.

It also modernizes the dependency floors to transformers v5, fixes a class of silent scoring bugs caused by half precision, and speeds up both training and encoding.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==6.0.0
Inference only, use one of:
pip install sentence-transformers==6.0.0
pip install sentence-transformers[onnx-gpu]==6.0.0
pip install sentence-transformers[onnx]==6.0.0
pip install sentence-transformers[openvino]==6.0.0
Multimodal dependencies (optional):
pip install sentence-transformers[image]==6.0.0
pip install sentence-transformers[audio]==6.0.0
pip install sentence-transformers[video]==6.0.0
Or combine as needed:
pip install sentence-transformers[train,onnx,image]==6.0.0

[!TIP] Our Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers blogpost is an excellent place to learn about multi-vector models: loading the various checkpoint formats, encoding and scoring, plugging them into a search stack, running them on page images, and keeping the index affordable.

[!WARNING] This is a major release with breaking changes. Upgrading from v5.x to v6.0 may require code updates. The changes marked 🚨 below are the ones most likely to affect you, and the Migration Guide has the full list. If you run into issues when upgrading, feel free to open an issue.

MultiVectorEncoder: ColBERT-style late interaction models (#3794)

Sentence Transformers v6.0 introduces MultiVectorEncoder, for ColBERT-style late interaction retrieval. Where a regular embedding model compresses a whole text into one vector, a multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away, which usually means stronger retrieval at the cost of a bigger index. It is also the state of the art for visual document retrieval, where a text query is matched against page images directly, with no OCR step in between.

Any PyLate checkpoint and any Stanford-NLP ColBERT checkpoint loads straight into it, and colpali-engine models for visual document retrieval work too, through the same familiar API you already use for dense, sparse, and reranker models.

from sentence_transformers import MultiVectorEncoder
Download from the 🤗 Hub
model = MultiVectorEncoder("lightonai/LateOn")
query_embeddings = model.encode_query(["Which planet is known as the Red Planet?"])
document_embeddings = model.encode_document([
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
</tr></table>

... (truncated)

Commits

• 56c9548 [docs] Remove revision from EVIE now that it's integrated (#3951)
• aa791eb Release v6.0.0
• 8b332d1 tests: Fix the pretrained MultiVectorEncoder tests for device tensors and Hub...
• a03cfd6 docs: Point cross-references at their documented targets (#3947)
• 310ed7b docs: Rank the pretrained MultiVectorEncoder models by NanoBEIR and NanoViDoR...
• 345c538 Speed up multi-vector padding and numpy to tensor conversion (#3942)
• 8d62fa2 [v6] Return tensors on the model device from MultiVectorEncoder encoding (#...
• 8a73e35 [v6] Keep the prompt-excluded mask out of the feature dicts (#3944)
• c8d2c0c [v6] Extend the merged column forward to the SentenceTransformer losses (#3...
• adc09fc [v6] Fix XTR input validation, padding masks, and integer embedding support...
• Additional commits viewable in compare view

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