sentence-transformers 3.2.0

v3.2.0 - ONNX and OpenVINO backends offering 2-3x speedup; Static Embeddings offering 50x-500x speedups at ~10-20% performance cost

on Python PyPI

This release introduces 2 new efficient computing backends for SentenceTransformer models: ONNX and OpenVINO + optimization & quantization, allowing for speedups up to 2x-3x; static embeddings via Model2Vec allowing for lightning-fast models (i.e., 50x-500x speedups) at a ~10%-20% performance cost; and various small improvements and fixes.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==3.2.0

# Inference only, use one of:
pip install sentence-transformers==3.2.0
pip install sentence-transformers[onnx-gpu]==3.2.0
pip install sentence-transformers[onnx]==3.2.0
pip install sentence-transformers[openvino]==3.2.0

Faster ONNX and OpenVINO Backends for SentenceTransformer (#2712)

Introducing a new backend keyword argument to the SentenceTransformer initialization, allowing values of "torch" (default), "onnx", and "openvino".
These come with new installations:

pip install sentence-transformers[onnx-gpu]
# or ONNX for CPU only:
pip install sentence-transformers[onnx]
# or
pip install sentence-transformers[openvino]

It's as simple as:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2", backend="onnx")

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)

If you specify a backend and your model repository or directory contains an ONNX/OpenVINO model file, it will automatically be used! And if your model repository or directory doesn't have one already, an ONNX/OpenVINO model will be automatically exported. Just remember to model.push_to_hub or model.save_pretrained into the same model repository or directory to avoid having to re-export the model every time.

All keyword arguments passed via model_kwargs will be passed on to ORTModel.from_pretrained or OVBaseModel.from_pretrained. The most useful arguments are:

• provider: (Only if backend="onnx") ONNX Runtime provider to use for loading the model, e.g. "CPUExecutionProvider" . See https://onnxruntime.ai/docs/execution-providers/ for possible providers. If not specified, the strongest provider (E.g. "CUDAExecutionProvider") will be used.
• file_name: The name of the ONNX file to load. If not specified, will default to "model.onnx" or otherwise "onnx/model.onnx" for ONNX, and "openvino_model.xml" and "openvino/openvino_model.xml" for OpenVINO. This argument is useful for specifying optimized or quantized models.
• export: A boolean flag specifying whether the model will be exported. If not provided, export will be set to True if the model repository or directory does not already contain an ONNX or OpenVINO model.

For example:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "all-MiniLM-L6-v2",
	backend="onnx",
	model_kwargs={
		"file_name": "model_O3.onnx",
		"provider": "CPUExecutionProvider",
	}
)

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)

Benchmarks

We ran benchmarks for CPU and GPU, averaging findings across 4 models of various sizes, 3 datasets, and numerous batch sizes. Here are the findings:

These findings resulted in these recommendations:

For GPU, you can expect 2x speedup with fp16 at no cost, and for CPU you can expect ~2.5x speedup at a cost of 0.4% accuracy.

from sentence_transformers import SentenceTransformer, export_optimized_onnx_model

onnx_model = SentenceTransformer("BAAI/bge-large-en-v1.5", backend="onnx")
export_optimized_onnx_model(
	model=onnx_model,
	optimization_config="O4",
	model_name_or_path="BAAI/bge-large-en-v1.5",
	push_to_hub=True,
	create_pr=True,
)

from sentence_transformers import SentenceTransformer

pull_request_nr = 2 # TODO: Update this to the number of your pull request
model = SentenceTransformer(
   "BAAI/bge-large-en-v1.5",
   backend="onnx",
   model_kwargs={"file_name": "onnx/model_O4.onnx"},
   revision=f"refs/pr/{pull_request_nr}"
)

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
   "BAAI/bge-large-en-v1.5",
   backend="onnx",
   model_kwargs={"file_name": "onnx/model_O4.onnx"},
)

from sentence_transformers import SentenceTransformer, export_quantized_onnx_model

onnx_model = SentenceTransformer("BAAI/bge-large-en-v1.5", backend="onnx")
export_quantized_onnx_model(
	model=onnx_model,
	quantization_config="avx512",
	model_name_or_path="BAAI/bge-large-en-v1.5",
	push_to_hub=True,
	create_pr=True,
)

from sentence_transformers import SentenceTransformer

pull_request_nr = 2 # TODO: Update this to the number of your pull request
model = SentenceTransformer(
   "BAAI/bge-large-en-v1.5",
   backend="onnx",
   model_kwargs={"file_name": "onnx/model_qint8_avx512.onnx"},
   revision=f"refs/pr/{pull_request_nr}"
)

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
   "BAAI/bge-large-en-v1.5",
   backend="onnx",
   model_kwargs={"file_name": "onnx/model_qint8_avx512.onnx"},
)

Lightning-Fast Static Embeddings via Model2Vec (#2961)

If ONNX or OpenVINO isn't fast enough for you yet, then perhaps you'll enjoy Static Embeddings. These embeddings are a bit akin to GLoVe or Word2vec, i.e. they're bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks.

However, these Static Embeddings are created in different ways. For example:

1. Distillation via the Model2Vec technique. This projects allows you to distill any Sentence Transformer model into Static Embeddings. For example, distilling BAAI/bge-base-en-v1.5 resulted in a Static Embeddings Sentence Transformer model that reaches 87.5% of the performance of all-MiniLM-L6-v2 on MTEB (+ PEARL & WordSim) and 97.4% of the performance of all-MiniLM-L6-v2 on various classification benchmarks.
   You can initialize Static Embeddings via Model2Vec in two ways:

   • from_model2vec: You can load one of the pretrained Model2Vec models:

   # note: `pip install model2vec` is needed, but not for inference
   from sentence_transformers import SentenceTransformer
   from sentence_transformers.models import StaticEmbedding
   
   # Initialize a Sentence Transformer model with a static embedding from a pretrained model2vec model
   static_embedding = StaticEmbedding.from_model2vec("minishlab/M2V_multilingual_output")
   model = SentenceTransformer(modules=[static_embedding])
   
   # Encode some texts
   queries = ["What is the capital of France?", "How many people live in the Netherlands?"]
   documents = ["Paris is the capital of France", "The Netherlands has 17 million inhabitants"]
   query_embeddings = model.encode(queries)
   document_embeddings = model.encode(documents)
   
   # Compute similarities
   scores = model.similarity(query_embeddings, document_embeddings)
   print(scores)
   """
   tensor([[0.8170, 0.3843],
           [0.3929, 0.5818]])
   """

   • from_distillation: You can use the name of any Sentence Transformer model alongside some parameters (See this docs for more information) to perform the distillation yourself, without needing any dataset. On my device, this takes ~4s on a GPU and ~2 minutes on a CPU:

   # note: `pip install model2vec` is needed, but not for inference
   from sentence_transformers import SentenceTransformer
   from sentence_transformers.models import StaticEmbedding
   
   # Initialize a Sentence Transformer model with a static embedding by distilling via model2vec
   static_embedding = StaticEmbedding.from_distillation(
       "mixedbread-ai/mxbai-embed-large-v1",
       device="cuda",
       pca_dims=256,
       apply_zipf=True,
   )
   model = SentenceTransformer(modules=[static_embedding])
   
   # Encode some texts
   queries = ["What is the capital of France?", "How many people live in the Netherlands?"]
   documents = ["Paris is the capital of France", "The Netherlands has 17 million inhabitants"]
   query_embeddings = model.encode(queries)
   document_embeddings = model.encode(documents)
   
   # Compute similarities
   scores = model.similarity(query_embeddings, document_embeddings)
   print(scores)
   """
   tensor([[0.8430, 0.3271],
           [0.3213, 0.5861]])
   """

2. Random initialization: Although this initialization needs finetuning, finetuning a Sentence Transformers model backed by StaticEmbedding is extremely fast. For example, I was able to finetune tomaarsen/static-bert-uncased-gooaq with MatryoshkaLoss & MultipleNegativesRankingLoss on the entire (3 million pairs) gooaq dataset in just 7 minutes. This model reaches a NDCG@10 of 79.33 on a hold-out set of 10k samples from gooaq, whereas e.g. BAAI/bge-base-en-v1.5 reaches 85.01 NDCG@10. In short, only 6.6% less performance for a model that's about 500x faster.
   That's not a typo: I can compute embeddings for about 14000 stsb sentences from per second on CPU, compared to about ~24 with BAAI/bge-base-en-v1.5, a.k.a. 625x faster.

Small changes

• The InformationRetrievalEvaluator now accepts query_prompt, query_prompt_name, corpus_prompt, and corpus_prompt_name arguments, useful if your model requires specific prompts for queries and/or documents for the best performance. (#2951)
• The mine_hard_negatives function now accepts anchor_column_name and positive_column_name for specifying which dataset columns will be used. If not specified, the first two columns are used, respectively. Additionally, the min_score parameter is added, ensuring that all mined negatives have a similarity score of at least min_score according to the chosen SentenceTransformer or CrossEncoder model. (#2977)
• If you're using multiple evaluators during training via SequentialEvaluator, e.g. multiple evaluators for different Matryoshka dimensions, then the order is now preserved in the training logs in the model card. Previously, they were sorted by name, resulting in weird orderings (e.g. "gooaq-1024", "gooaq-128", "gooaq-256", "gooaq-32", "gooaq-512", "gooaq-64") (#2963)
• CachedGISTEmbedLoss has been improved to support multiple negatives per sample, i.e. the loss now accepts data in the (anchor, positive, negative_1, …, negative_n) format. It is the third loss to support this format (see docs):

All changes

• [fix] Only save first module in root if "save_in_root" is specified. by @tomaarsen in #2957
• [feat] Add query prompts to Information Retrieval Evaluator by @ArthurCamara in #2951
• [model cards] Keep evaluation order in training logs if there's multiple evaluators by @tomaarsen in #2963
• Add negatives in CachedGISTEmbedLoss by @daegonYu in #2946
• [ENH] -- CrossEncoder.rank by @it176131 in #2947
• [feat] Add lightning-fast StaticEmbedding module based on model2vec by @tomaarsen in #2961
• [feat] Add ONNX and OpenVINO backends by @helena-intel and @tomaarsen in #2712
• Refine mine_hard_negatives arguments by @bakrianoo in #2977

New Contributors

• @daegonYu made their first contribution in #2946
• @it176131 made their first contribution in #2947
• @helena-intel made their first contribution in #2712
• @bakrianoo made their first contribution in #2977

Special thanks to @echarlaix for making the new backends possible due to some last-minute changes in optimum and optimum-intel.

Full Changelog: v3.1.1...v3.2.0