chore(deps): Bump sentence-transformers from 3.0.1 to 3.1.0 in /backend/python/sentencetransformers

[dependabot]

Bumps sentence-transformers from 3.0.1 to 3.1.0.

Release notes

Sourced from sentence-transformers's releases.

v3.1.0 - Hard Negatives Mining utility; new loss function for symmetric tasks; streaming datasets; custom modules

This release introduces a hard negatives mining utility to get better models out of your data, a new strong loss function for symmetric tasks, training with streaming datasets to avoid having to store datasets fully on disk, custom modules to allow for more creativity from model authors, and many bug fixes, small additions and documentation improvements.

Install this version with

# Full installation:
pip install sentence-transformers[train]==3.1.0
Inference only:
pip install sentence-transformers==3.1.0

[!WARNING] Due to incompatibilities with Windows, we have set numpy<2 in the Sentence Transformers requirements. If you're not on Windows, you can still install numpy>=2 and everything should work as expected.

Hard Negatives Mining utility (#2768, #2848)

Hard negatives are texts that are rather similar to some anchor text (e.g. a question), but are not the correct match. For example:

• Anchor: "are red pandas actually pandas?"
• Positive: "Red pandas, like giant pandas, are bamboo eaters native to Asia's high forests. Despite these similarities and their shared name, the two species are not closely related. Red pandas are much smaller than giant pandas and are the only living member of their taxonomic family."
• Hard negative: "The giant panda (Ailuropoda melanoleuca; Chinese: 大熊猫; pinyin: dàxióngmāo), also known as the panda bear or simply the panda, is a bear native to south central China."

These negatives are more difficult for a model to distinguish from the correct answer, leading to a stronger training signal and a stronger overall model when used with one of the Loss Functions that accepts (anchor, positive, negative) pairs such as the one above.

This release introduces a utility function called mine_hard_negatives that allows you to mine for these hard negatives given a (anchor, positive) dataset (and optionally a corpus of negative candidate texts).

It boasts the following features to give you fine-grained control over the similarity of the mined negatives relative to the anchor:

• CrossEncoder rescoring for higher quality negative selection.
• Skip the top $n$ negative candidates as these might be true positives.
• Consider only the top $n$ negative candidates.
• Skip negative candidates that are within some margin of the true similarity between anchor and positive.
• Skip negative candidates whose similarity is larger than some max_score.
• Two sampling strategies: pick the top negative candidates that satisfy the requirements, or pick them randomly.
• FAISS index for searching for negative candidates.
• Option to return data as triplets only, or as 2 + num_negatives-tuples.

from sentence_transformers.util import mine_hard_negatives
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
Load a Sentence Transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")
Load a dataset to mine hard negatives from
dataset = load_dataset("sentence-transformers/natural-questions", split="train")
print(dataset)
"""
</tr></table>

... (truncated)

Commits

• 845dd54 Release v3.1.0
• a3f2236 [feat] Update mine_hard_negatives to using a full corpus and multiple posit...
• 8af7c5d [feat] Add column order warnings to the data collator (#2928)
• bc9a666 [docs] Move losses up in the package reference; they're more important (#2929)
• 597d5ed [fix] Add dtype cast for modules other than Transformer (#2889)
• 6257cb0 [feat] Allow loading custom modules; encode kwargs passthrough to modules (...
• ede5804 [security] Load weights only with torch.load & pytorch_model.bin (#2927)
• 9e3da5b [deprecation] Push deprecation cycle for use_auth_token to v4 (#2926)
• 1cd91ab [feat] Add cache_dir & config_args support to CrossEncoder (#2784)
• 02fb5f8 [fix] Change eval dataloader to use eval_batch_size (#2847)
• Additional commits viewable in compare view

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