deps: update sentence-transformers requirement from >=3.3.0 to >=5.4.1

[dependabot]

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

Release notes

Sourced from sentence-transformers's releases.

v5.4.1 - Numpy string arrays

This patch release allows encode() and predict() to accept 1D numpy string arrays as inputs.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.4.1
Inference only, use one of:
pip install sentence-transformers==5.4.1
pip install sentence-transformers[onnx-gpu]==5.4.1
pip install sentence-transformers[onnx]==5.4.1
pip install sentence-transformers[openvino]==5.4.1
Multimodal dependencies (optional):
pip install sentence-transformers[image]==5.4.1
pip install sentence-transformers[audio]==5.4.1
pip install sentence-transformers[video]==5.4.1
Or combine as needed:
pip install sentence-transformers[train,onnx,image]==5.4.1

Numpy string/object arrays as batches (#3720)

encode() and predict() now correctly recognize 1D numpy string/object arrays as batches rather than singular inputs. Previously, something like model.encode(df["text"].to_numpy()) was silently treated as a single input and produced incorrect output. 1D numpy arrays with dtype.kind in ("U", "O") are now unpacked like lists, and 2D+ arrays are treated as batches of pairs (for CrossEncoder).

import numpy as np
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
Previously treated as one input; now correctly encoded as 3 separate texts
embeddings = model.encode(np.array(["first", "second", "third"]))
print(embeddings.shape)
(3, 384)

For CrossEncoder, a 1D numpy string array is still treated as a single [query, document] pair to match the existing list behavior, while a 2D array of shape (N, 2) is a batch of N pairs.

Safer activation function loading in Dense (#3714)

The Dense module stores its activation function as a dotted import path in its saved config (e.g. "torch.nn.modules.activation.Tanh"), which was then resolved via import_from_string whenever the module was loaded. Because any importable Python callable could be referenced, a maliciously crafted config.json on the Hub could trigger arbitrary imports at model load time.

The loader now only resolves activation functions whose import path starts with torch.. Anything else is skipped with a warning and replaced by the default activation (Tanh). To load a model with a custom (non-torch) activation function, opt in explicitly with trust_remote_code=True:

from sentence_transformers import SentenceTransformer
</tr></table>

... (truncated)

Commits

• 6dc2cb5 Release v5.4.1
• a6a371c Merge branch 'main' into v5.4-release
• c500af5 [fix] Treat numpy string/object arrays as batches in encode/predict (#3720)
• 25f0694 Only load activation functions starting with 'torch' in the Dense module (#3714)
• 9140444 Replace evaluation_strategy with eval_strategy in a few more places (#3713)
• cec9077 No revision needed anymore for nvidia nemotron (#3712)
• 5035ccd No revision needed anymore for nvidia nemotron (#3712)
• abca5aa Increment dev version after v5.4 release (#3711)
• d36232b [tests] Fix test_trainer_prompts for SE and ST after prompt handling moved ...
• fe93612 Merge branch 'main' into v5.4-release
• Additional commits viewable in compare view

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