Merge branch 'master' into v3.3-release, increment to v3.3.1

Author: tomaarsen

examples/training/prompts/README.md

@@ -96,7 +96,7 @@ Additionally, some research papers (`INSTRUCTOR <https://arxiv.org/abs/2212.0974
 ```eval_rst
 See the following script as an example of how to train with prompts in practice:
 
-* `training_nq_prompts.py <training_nq_prompts.py>`_: This script finetunes `mpnet-base <https://huggingface.co/microsoft/mpnet-base>`_ on 100k query-answer pairs from the `natural-questions <https://huggingface.co/datasets/sentence-transformers/natural-questions>`_ dataset using the :class:`~sentence_transformers.losses.CachedMultipleNegativesRankingLoss` loss. The model is evaluated during training using the :class:`~sentence_transformers.evaluation.NanoBEIREvaluator`.
+* `training_nq_prompts.py <https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/prompts/training_nq_prompts.py>`_: This script finetunes `mpnet-base <https://huggingface.co/microsoft/mpnet-base>`_ on 100k query-answer pairs from the `natural-questions <https://huggingface.co/datasets/sentence-transformers/natural-questions>`_ dataset using the :class:`~sentence_transformers.losses.CachedMultipleNegativesRankingLoss` loss. The model is evaluated during training using the :class:`~sentence_transformers.evaluation.NanoBEIREvaluator`.
 
 This script has two variables that affect 1) whether prompts are used and 2) whether prompts are included in the pooling. I have finetuned both ``mpnet-base`` and ``bert-base-uncased`` under the various different settings, resulting in a 0.66% and 0.90% relative improvements on ``NDCG@10`` at no extra cost.
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "sentence-transformers"
-version = "3.3.0"
+version = "3.3.1"
 description = "State-of-the-Art Text Embeddings"
 license = { text = "Apache 2.0" }
 readme = "README.md"

sentence_transformers/__init__.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-__version__ = "3.3.0"
+__version__ = "3.3.1"
 __MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
 
 import importlib

sentence_transformers/models/Transformer.py

@@ -101,7 +101,15 @@ def __init__(
 
     def _load_config(self, model_name_or_path: str, cache_dir: str | None, backend: str, config_args: dict[str, Any]):
         """Loads the configuration of a model"""
-        if find_adapter_config_file(model_name_or_path) is not None:
+        if (
+            find_adapter_config_file(
+                model_name_or_path,
+                token=config_args.get("token"),
+                revision=config_args.get("revision"),
+                local_files_only=config_args.get("local_files_only", False),
+            )
+            is not None
+        ):
             if not is_peft_available():
                 raise Exception(
                     "Loading a PEFT model requires installing the `peft` package. You can install it via `pip install peft`."