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Update dependency sentence-transformers to v3.4.1#160

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@renovate renovate Bot commented Apr 23, 2025

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
sentence-transformers ==3.0.1 -> ==3.4.1 age adoption passing confidence

Release Notes

UKPLab/sentence-transformers (sentence-transformers)

v3.4.1: - Model2Vec compatibility & offline model fix

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This release introduces a convenient compatibility with Model2Vec models, and fixes a bug that caused an outgoing request even when using a local model.

Install this version with

##### Training + Inference
pip install sentence-transformers[train]==3.4.1

##### Inference only, use one of:
pip install sentence-transformers==3.4.1
pip install sentence-transformers[onnx-gpu]==3.4.1
pip install sentence-transformers[onnx]==3.4.1
pip install sentence-transformers[openvino]==3.4.1
Full Model2Vec integration

This release introduces support to load an efficient Model2Vec embedding model directly in Sentence Transformers:

from sentence_transformers import SentenceTransformer

##### Download from the 🤗 Hub
model = SentenceTransformer(
    "minishlab/potion-base-8M",
    device="cpu",
)

##### Run inference
sentences = [
    'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?',
    'To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.',
    'In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)

##### [3, 1024]
##### Get the similarity scores for the embeddings
similarities = model.similarity(embeddings[0], embeddings[1:])
print(similarities)

##### tensor([[0.8085, 0.4884]])
Previously, loading a Model2Vec model required you to load a `StaticEmbedding` module. 
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

##### Download from the 🤗 Hub
module = StaticEmbedding.from_model2vec("minishlab/potion-base-8M")
model = SentenceTransformer(modules=[module], device="cpu")

##### Run inference
sentences = [
    'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?',
    'To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.',
    'In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)

##### [3, 1024]
##### Get the similarity scores for the embeddings
similarities = model.similarity(embeddings[0], embeddings[1:])
print(similarities)

##### tensor([[0.8085, 0.4884]])

Model2Vec was the inspiration of the recent Static Embedding work; all of these models can be used to approach the performance of normal transformer-based embedding models at a fraction of the latency. For example, both Model2Vec and Static Embedding models are ~25x faster than tiny embedding models on a GPU and ~400x faster than those models on a CPU.

Bug Fix
  • Using local_files_only=True still triggered a request to Hugging Face for the model card metadata; this has been resolved in (#​3202).
All Changes
New Contributors

Full Changelog: huggingface/sentence-transformers@v3.4.0...v3.4.1

v3.4.0: - Resolved memory leak when deleting a model & trainer; add Matryoshka & Cached loss compatibility; small features & bug fixes

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This release resolves a memory leak when deleting a model & trainer, adds compatibility between the Cached... losses and the Matryoshka loss modifier, resolves numerous bugs, and adds several small features.

Install this version with

### Training + Inference
pip install sentence-transformers[train]==3.4.0

### Inference only, use one of:
pip install sentence-transformers==3.4.0
pip install sentence-transformers[onnx-gpu]==3.4.0
pip install sentence-transformers[onnx]==3.4.0
pip install sentence-transformers[openvino]==3.4.0

Matryoshka & Cached loss compatibility (#​3068, #​3107)

It is now possible to combine the strong Cached losses (CachedMultipleNegativesRankingLoss, CachedGISTEmbedLoss, CachedMultipleNegativesSymmetricRankingLoss) with the Matryoshka loss modifier:

from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
from datasets import Dataset

model = SentenceTransformer("microsoft/mpnet-base")
train_dataset = Dataset.from_dict({
    "anchor": ["It's nice weather outside today.", "He drove to work."],
    "positive": ["It's so sunny.", "He took the car to the office."],
})
loss = losses.CachedMultipleNegativesRankingLoss(model, mini_batch_size=16)
loss = losses.MatryoshkaLoss(model, loss, [768, 512, 256, 128, 64])

trainer = SentenceTransformerTrainer(
    model=model,
    train_dataset=train_dataset,
    loss=loss,
)
trainer.train()

See for example tomaarsen/mpnet-base-gooaq-cmnrl-mrl which was trained with CachedMultipleNegativesRankingLoss (CMNRL) with the Matryoshka loss modifier (MRL).

Resolve memory leak when Model and Trainer are reinitialized (#​3144)

Due to a circular dependency in the SentenceTransformerTrainer -> SentenceTransformer -> SentenceTransformerModelCardData -> SentenceTransformerTrainer, deleting the trainer and model still doesn't clear them up via garbage disposal. I've moved a lot of components around, and now SentenceTransformerModelCardData does not need to store the SentenceTransformerTrainer, breaking the cycle.

We ran the seed optimization script (which frequently creates and deletes models and trainers):

  • Before: Approximate highest recorded VRAM:
    16332MiB / 24576MiB
  • After: Approximate highest recorded VRAM:
    8222MiB / 24576MiB

Small Features

  • Add Matthews Correlation Coefficient to the BinaryClassificationEvaluator in #​3051.
  • Add a triplet margin parameter to the TripletEvaluator in #​2862.
  • Put dataset information in the automatically generated model card in "expanding sections" blocks if there are many datasets in #​3088.
  • Add multi-GPU (and CPU multi-process) support for mine_hard_negatives in #​2967.

Notable Bug Fixes

  • Subsequent batches were identical when using the no_duplicates Batch Sampler (#​3069). This has been resolved in #​3073
  • The old-style model.fit() training with write_csv on an evaluator would crash (#​3062). This has been resolved in #​3066.
  • The output types of some evaluators were np.float instead of float (#​3075). This has been resolved in #​3076 and #​3096.
  • It was not possible to specify a revision or cache_dir when loading a PEFT Adapter model (#​3061). This has been resolved in #​3079 and #​3174.
  • The CrossEncoder was lazily placed on the incorrect device, did not respond to model.to (#​3078). This has been resolved in #​3104.
  • If a model used a custom module with custom kwargs, those kwargs keys were not saved in modules.json correctly, e.g. relevant for jina-embeddings-v3 (#​3111). This has been resolved in #​3112.
  • HfArgumentParser(SentenceTransformerTrainingArguments) would crash due to prompts typing (#​3090). This has been resolved in #​3178.

Example Updates

  • Update the quantization script in #​3070.
  • Update the seed optimization script in #​3092.
  • Update the TSDAE scripts in #​3137.
  • Add PEFT Adapter script in #​3180.

Documentation Updates

All Changes

New Contributors

An explicit thanks to @​JINO-ROHIT who has made a large amount of contributions in this release.

Full Changelog: huggingface/sentence-transformers@v3.3.1...v3.4.0

v3.3.1: - Patch private model loading without environment variable

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This patch release fixes a small issue with loading private models from Hugging Face using the token argument.

Install this version with

### Training + Inference
pip install sentence-transformers[train]==3.3.1
### Inference only, use one of:
pip install sentence-transformers==3.3.1
pip install sentence-transformers[onnx-gpu]==3.3.1
pip install sentence-transformers[onnx]==3.3.1
pip install sentence-transformers[openvino]==3.3.1

Details

If you're loading model under this scenario:

  • Your model is hosted on Hugging Face.
  • Your model is private.
  • You haven't set the HF_TOKEN environment variable via huggingface-cli login or some other approach.
  • You're passing the token argument to SentenceTransformer to load the model.

Then you may have encountered a crash in v3.3.0. This should be resolved now.

All Changes

Full Changelog: huggingface/sentence-transformers@v3.3.0...v3.3.1

v3.3.0: - Massive CPU speedup with OpenVINO int8 quantization; Training with Prompts for stronger models; NanoBEIR IR evaluation; PEFT compatibility; Transformers v4.46.0 compatibility

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4x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free performance boost, convenient evaluation on NanoBEIR: a subset of a strong Information Retrieval benchmark, PEFT compatibility by easily adding/loading adapters, Transformers v4.46.0 compatibility, and Python 3.8 deprecation.

Install this version with:

### Training + Inference
pip install sentence-transformers[train]==3.3.0

### Inference only, use one of:
pip install sentence-transformers==3.3.0
pip install sentence-transformers[onnx-gpu]==3.3.0
pip install sentence-transformers[onnx]==3.3.0
pip install sentence-transformers[openvino]==3.3.0

OpenVINO int8 static quantization (https://github.com/UKPLab/sentence-transformers/pull/3025)

We introduce int8 static quantization using OpenVINO, a highly performant solution that outperforms all other current backends by a mile, at a minimal loss in performance. Here are the updated benchmarks:

Quantizing directly to the Hugging Face Hub
from sentence_transformers import SentenceTransformer, export_static_quantized_openvino_model

### 1. Load a model with the OpenVINO backend
model = SentenceTransformer("all-MiniLM-L6-v2", backend="openvino")

### 2. Quantize the model to int8, push the model to https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
### as a pull request:
export_static_quantized_openvino_model(
    model,
    quantization_config=None,
    model_name_or_path="sentence-transformers/all-MiniLM-L6-v2",
    push_to_hub=True,
    create_pr=True,
)

You can immediately use the model, even before it's merged, by using the revision argument:

from sentence_transformers import SentenceTransformer

pull_request_nr = 2 # TODO: Update this to the number of your pull request
model = SentenceTransformer(
    "all-MiniLM-L6-v2",
    backend="openvino",
    model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"},
    revision=f"refs/pr/{pull_request_nr}"
)

And once it's merged:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "all-MiniLM-L6-v2",
    backend="openvino",
    model_kwargs={"file_name": "openvino/openvino_model_qint8_quantized.xml"},
)
Quantizing locally

You can also quantize a model and save it locally:

from sentence_transformers import SentenceTransformer, export_static_quantized_openvino_model
from optimum.intel import OVQuantizationConfig

model = SentenceTransformer("all-mpnet-base-v2", backend="openvino")
model.save_pretrained("path/to/all-mpnet-base-v2-local")
quantization_config = OVQuantizationConfig() # <- You can update settings here
export_static_quantized_openvino_model(model, quantization_config, "path/to/all-mpnet-base-v2-local")

And after quantizing, you can load it like so:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "path/to/all-mpnet-base-v2-local",
    backend="openvino",
    model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"},
)

All original Sentence Transformer models already have these new openvino_model_qint8_quantized.xml files, so you can load them without exporting directly! I would recommend making pull requests for other models on Hugging Face that you'd like to see quantized.

Learn more about how to Speed up Inference in the documentation: https://sbert.net/docs/sentence_transformer/usage/efficiency.html

Training with Prompts (https://github.com/UKPLab/sentence-transformers/pull/2964)

Many modern embedding models are trained with “instructions” or “prompts” following the INSTRUCTOR paper. These prompts are strings, prefixed to each text to be embedded, allowing the model to distinguish between different types of text.

For example, the mixedbread-ai/mxbai-embed-large-v1 model was trained with Represent this sentence for searching relevant passages: as the prompt for all queries. This prompt is stored in the model configuration under the prompt name "query", so users can specify that prompt_name in model.encode:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
query_embedding = model.encode("What are Pandas?", prompt_name="query")

### or
### query_embedding = model.encode("What are Pandas?", prompt="Represent this sentence for searching relevant passages: ")
document_embeddings = model.encode([
    "Pandas is a software library written for the Python programming language for data manipulation and analysis.",
    "Pandas are a species of bear native to South Central China. They are also known as the giant panda or simply panda.",
    "Koala bears are not actually bears, they are marsupials native to Australia.",
])
similarity = model.similarity(query_embedding, document_embeddings)
print(similarity)

### => tensor([[0.7594, 0.7560, 0.4674]])

Various papers (INSTRUCTOR, BGE) show that including prompts or instructions both during training and inference results in stronger performance. As of this release, it's now possible to easily train with prompts in Sentence Transformers with just one extra training argument: prompts. There are 4 accepted formats for it:

  1. str: A single prompt to use for all columns in all datasets. For example: 
    args = SentenceTransformerTrainingArguments(
    ...,
    prompts="text: ",
    ...,
)
  2. Dict[str, str]: A dictionary mapping column names to prompts, applied to all datasets. For example: 
    args = SentenceTransformerTrainingArguments(
    ...,
    prompts={
        "query": "query: ",
        "answer": "document: ",
    },
    ...,
)
  3. Dict[str, str]: A dictionary mapping dataset names to prompts. This should only be used if your training/evaluation/test datasets are a DatasetDict or a dictionary of Dataset. For example: 
    args = SentenceTransformerTrainingArguments(
    ...,
    prompts={
        "stsb": "Represent this text for semantic similarity search: ",
        "nq": "Represent this text for retrieval: ",
    },
    ...,
)
  4. Dict[str, Dict[str, str]]: A dictionary mapping dataset names to dictionaries mapping column names to prompts. This should only be used if your training/evaluation/test datasets are a DatasetDict or a dictionary of Dataset. For example: 
    args = SentenceTransformerTrainingArguments(
    ...,
    prompts={
        "stsb": {
            "sentence1": "sts: ",
            "sentence2": "sts: ",
        },
        "nq": {
            "query": "query: ",
            "document": "document: ",
        },
    },
    ...,
)

I've trained models with and without prompts for 2 base models: mpnet-base and bert-base-uncased:

For both base models, the model with prompts consistently outperformed the baseline model. After training, the models with prompts resulted in a 0.66% and 0.90% relative improvement on NDCG@10 at no extra cost.

mpnet-base tests bert-base-uncased tests
mpnet_base_nq_nanobeir bert_base_nq_nanobeir

NanoBEIR Evaluator integration (https://github.com/UKPLab/sentence-transformers/pull/2966)

This update introduced a new simple NanoBEIREvaluator, evaluating your model against NanoBEIR: a collection of subsets of the 13 BEIR datasets. BEIR corresponds to the retrieval tab of MTEB, and is commonly seen as a valuable indicator of general-purpose information retrieval performance.

With the NanoBEIREvaluator, you can easily evaluate your models on a much faster benchmark that should give similar insights in performance as BEIR. You can use it like so:

from sentence_transformers.evaluation import NanoBEIREvaluator
from sentence_transformers import SentenceTransformer
import logging

### Optional, but nice to get human-readable results in the terminal
logging.basicConfig(
    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO
)

### 1. Load a model
model = SentenceTransformer("all-mpnet-base-v2", backend="onnx")

### 2. Initialize the evaluator
evaluator = NanoBEIREvaluator()

### 3. Call the evaluator to get a dictionary of metric names to values
results = evaluator(model)
"""
NanoBEIR Evaluation of the model on ['climatefever', 'dbpedia', 'fever', 'fiqa2018', 'hotpotqa', 'msmarco', 'nfcorpus', 'nq', 'quoraretrieval', 'scidocs', 'arguana', 'scifact', 'touche2020'] dataset:
Evaluating NanoClimateFEVER
Information Retrieval Evaluation of the model on the NanoClimateFEVER dataset:
Queries: 50
Corpus: 3408

Score-Function: cosine
Accuracy@1: 24.00%
Accuracy@3: 36.00%
Accuracy@5: 44.00%
Accuracy@10: 66.00%
Precision@1: 24.00%
Precision@3: 14.00%
Precision@5: 10.40%
Precision@10: 9.00%
Recall@1: 9.50%
Recall@3: 17.33%
Recall@5: 22.90%
Recall@10: 36.07%
MRR@10: 0.3311
NDCG@10: 0.2618
MAP@100: 0.1982
Evaluating NanoDBPedia
Information Retrieval Evaluation of the model on the NanoDBPedia dataset:
Queries: 50
Corpus: 6045

Score-Function: cosine
Accuracy@1: 66.00%
Accuracy@3: 88.00%
Accuracy@5: 88.00%
Accuracy@10: 88.00%
Precision@1: 66.00%
Precision@3: 58.00%
Precision@5: 52.00%
Precision@10: 43.60%
Recall@1: 6.87%
Recall@3: 14.70%
Recall@5: 20.30%
Recall@10: 27.62%
MRR@10: 0.7533
NDCG@10: 0.5384
MAP@100: 0.3796
Evaluating NanoFEVER
Information Retrieval Evaluation of the model on the NanoFEVER dataset:
Queries: 50
Corpus: 4996

... (truncated for brevity)

Aggregated for Score Function: cosine
Accuracy@1: 52.87%
Accuracy@3: 71.35%
Accuracy@5: 78.45%
Accuracy@10: 85.07%
Precision@1: 52.87%
Recall@1: 30.28%
Precision@3: 33.78%
Recall@3: 47.93%
Precision@5: 26.23%
Recall@5: 55.04%
Precision@10: 18.07%
Recall@10: 62.54%
MRR@10: 0.6334
NDCG@10: 0.5758
"""

### 4. Print the results
print(evaluator.primary_metric)

### => "NanoBEIR_mean_cosine_ndcg@10"
print(results[evaluator.primary_metric])

### => 0.5758124378869705
Advanced Usage

You can also specify a subset of datasets, and you can specify query and/or corpus prompts, if your model uses them. For example:

import logging
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import NanoBEIREvaluator

### Optional, but nice to get human-readable results in the terminal
logging.basicConfig(
    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO
)

model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')

datasets = ["QuoraRetrieval", "MSMARCO"]
query_prompts = {
    "QuoraRetrieval": "Instruct: Given a question, retrieve questions that are semantically equivalent to the given question\\nQuery: ",
    "MSMARCO": "Instruct: Given a web search query, retrieve relevant passages that answer the query\\nQuery: "
}

evaluator = NanoBEIREvaluator(
    dataset_names=datasets,
    query_prompts=query_prompts,
)

results = evaluator(model)
'''
NanoBEIR Evaluation of the model on ['QuoraRetrieval', 'MSMARCO'] dataset:
Evaluating NanoQuoraRetrieval
Information Retrieval Evaluation of the model on the NanoQuoraRetrieval dataset:
Queries: 50
Corpus: 5046

Score-Function: cosine
Accuracy@1: 92.00%
Accuracy@3: 98.00%
Accuracy@5: 100.00%
Accuracy@10: 100.00%
Precision@1: 92.00%
Precision@3: 40.67%
Precision@5: 26.00%
Precision@10: 14.00%
Recall@1: 81.73%
Recall@3: 94.20%
Recall@5: 97.93%
Recall@10: 100.00%
MRR@10: 0.9540
NDCG@10: 0.9597
MAP@100: 0.9395

Evaluating NanoMSMARCO
Information Retrieval Evaluation of the model on the NanoMSMARCO dataset:
Queries: 50
Corpus: 5043

Score-Function: cosine
Accuracy@1: 40.00%
Accuracy@3: 74.00%
Accuracy@5: 78.00%
Accuracy@10: 88.00%
Precision@1: 40.00%
Precision@3: 24.67%
Precision@5: 15.60%
Precision@10: 8.80%
Recall@1: 40.00%
Recall@3: 74.00%
Recall@5: 78.00%
Recall@10: 88.00%
MRR@10: 0.5849
NDCG@10: 0.6572
MAP@100: 0.5892
Average Queries: 50.0
Average Corpus: 5044.5

Aggregated for Score Function: cosine
Accuracy@1: 66.00%
Accuracy@3: 86.00%
Accuracy@5: 89.00%
Accuracy@10: 94.00%
Precision@1: 66.00%
Recall@1: 60.87%
Precision@3: 32.67%
Recall@3: 84.10%
Precision@5: 20.80%
Recall@5: 87.97%
Precision@10: 11.40%
Recall@10: 94.00%
MRR@10: 0.7694
NDCG@10: 0.8085
'''
print(evaluator.primary_metric)

### => "NanoBEIR_mean_cosine_ndcg@10"
print(results[evaluator.primary_metric])

### => 0.8084508771660436

PEFT compatibility (https://github.com/UKPLab/sentence-transformers/pull/3000, https://github.com/UKPLab/sentence-transformers/pull/2980, https://github.com/UKPLab/sentence-transformers/pull/3046)

Sentence Transformers has been integrated much more closely with PEFT. Notably, we introduce new methods:

These methods allow you to add new PEFT adapters or load pretrained ones, for example:

Adding a adapter
from sentence_transformers import SentenceTransformer

### 1. Load a model to finetune with 2. (Optional) model card data
model = SentenceTransformer(
    "all-MiniLM-L6-v2",
    model_card_data=SentenceTransformerModelCardData(
        language="en",
        license="apache-2.0",
        model_name="all-MiniLM-L6-v2 adapter finetuned on GooAQ pairs",
    ),
)

### 2. Create a LoRA adapter for the model & add it
peft_config = LoraConfig(
    task_type=TaskType.FEATURE_EXTRACTION,
    inference_mode=False,
    r=8,
    lora_alpha=32,
    lora_dropout=0.1,
)
model.add_adapter(peft_config)

### Proceed as usual... See https://sbert.net/docs/sentence_transformer/training_overview.html
Loading a pretrained adapter

Given sentence-transformers-testing/stsb-bert-tiny-lora as a small adapter model (the adapter_model.safetensors file is only 33.8kB!) on top of sentence-transformers-testing/stsb-bert-tiny-safetensors, you can either load this adapter directly:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-lora")
embeddings = model.encode(["This is an example sentence", "Each sentence is converted"])
print(embeddings.shape)

### (2, 128)

Or you can load the original model and load the adapter into it:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors")
model.load_adapter("sentence-transformers-testing/stsb-bert-tiny-lora")
embeddings = model.encode(["This is an example sentence", "Each sentence is converted"])
print(embeddings.shape)

### (2, 128)

Transformers v4.46.0 compatibility (https://github.com/UKPLab/sentence-transformers/pull/3026, https://github.com/UKPLab/sentence-transformers/pull/3035, https://github.com/UKPLab/sentence-transformers/pull/3037, https://github.com/UKPLab/sentence-transformers/pull/3038)

The recent transformers v4.46.0 update introduced a few changes that were incompatible with Sentence Transformers. For example:

  • Use "processing_class" argument instead of "tokenizers"
  • Add a num_items_in_batch argument to the compute_loss method in the Trainer
  • Adding a ValueError if eval_dataset is None while eval_strategy is not "no" (this should be possible in Sentence Transformers, as we accept evaluating with just an evaluator as well)

These issues and deprecation warnings have been resolved.

Drop Python 3.8 support (https://github.com/UKPLab/sentence-transformers/pull/3033)

Given that Python 3.8 has now reached it's end of life, Sentence Transformers will no longer support it.

All Changes

New Contributors

Special Thanks

Big thanks to @​ArthurCamara for leading the work on both 1) training with prompts and 2) NanoBEIR.

Full Changelog: huggingface/sentence-transformers@v3.2.1...v3.3.0

v3.2.1: - Patch CLIP loading, small ONNX fix, compatibility with other libraries

Compare Source

This patch release fixes some small bugs, such as related to loading CLIP models, automatic model card generation issues, and ensuring compatibility with third party libraries.

Install this version with

### Training + Inference
pip install sentence-transformers[train]==3.2.1

### Inference only, use one of:
pip install sentence-transformers==3.2.1
pip install sentence-transformers[onnx-gpu]==3.2.1
pip install sentence-transformers[onnx]==3.2.1
pip install sentence-transformers[openvino]==3.2.1

Fixing Loading non-Transformer models

In v3.2.0, a non-Transformer based model (e.g. CLIP) would not load correctly if the model was saved in the root of the model repository/directory. This has been resolved in #​3007.

Throw error if StaticEmbedding-based model is finetuned with incompatible losses

The following losses are not compatible with StaticEmbedding-based models:

  • CachedGISTEmbedLoss
  • CachedMultipleNegativesRankingLoss
  • CachedMultipleNegativesSymmetricRankingLoss
  • DenoisingAutoEncoderLoss
  • GISTEmbedLoss

An error is now thrown when one of these are used with a StaticEmbedding-based model. I recommend using MultipleNegativesRankingLoss to finetune these models, e.g. as in https://huggingface.co/tomaarsen/static-bert-uncased-gooaq.
Note: to get good performance, you must use much higher learning rates than otherwise. In my experiments, 2e-1 worked well.

Patch ONNX model when the model uses output_hidden_states

For example, this script used to fail, but passes now:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "distiluse-base-multi

</details>

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@renovate renovate Bot requested a review from porteron as a code owner April 23, 2025 00:19
github-actions[bot]
github-actions Bot reviewed Apr 23, 2025
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thypon previously approved these changes Jun 6, 2025
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@renovate renovate Bot force-pushed the renovate/sentence-transformers-3-x branch from fc090de to abea65e Compare June 12, 2025 13:34
@mihaiplesa mihaiplesa closed this Jun 12, 2025
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renovate Bot commented Jun 12, 2025

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Because you closed this PR without merging, Renovate will ignore this update (==3.4.1). You will get a PR once a newer version is released. To ignore this dependency forever, add it to the ignoreDeps array of your Renovate config.

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@renovate renovate Bot deleted the renovate/sentence-transformers-3-x branch June 12, 2025 13:34
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