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⬆️ Update sentence-transformers requirement from <2.4.0,>=2.3.1 to >=2.3.1,<2.7.0 #339

Closed dependabot[bot] closed 4 months ago

dependabot[bot] commented 5 months ago

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

Release notes

Sourced from sentence-transformers's releases.

v2.6.0 - Embedding Quantization, GISTEmbedLoss

This release brings embedding quantization: a way to heavily speed up retrieval & other tasks, and a new powerful loss function: GISTEmbedLoss.

Install this version with

pip install sentence-transformers==2.6.0

Embedding Quantization

Embeddings may be challenging to scale up, which leads to expensive solutions and high latencies. However, there is a new approach to counter this problem; it entails reducing the size of each of the individual values in the embedding: Quantization. Experiments on quantization have shown that we can maintain a large amount of performance while significantly speeding up computation and saving on memory, storage, and costs.

To be specific, using binary quantization may result in retaining 96% of the retrieval performance, while speeding up retrieval by 25x and saving on memory & disk space with 32x. Do not underestimate this approach! Read more about Embedding Quantization in our extensive blogpost.

Binary and Scalar Quantization

Two forms of quantization exist at this time: binary and scalar (int8). These quantize embedding values from float32 into binary and int8, respectively. For Binary quantization, you can use the following snippet:

from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings

1. Load an embedding model


model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")


2a. Encode some text using "binary" quantization


binary_embeddings = model.encode(
["I am driving to the lake.", "It is a beautiful day."],
precision="binary",
)


2b. or, encode some text without quantization & apply quantization afterwards


embeddings = model.encode(["I am driving to the lake.", "It is a beautiful day."])
binary_embeddings = quantize_embeddings(embeddings, precision="binary")

References:

GISTEmbedLoss

GISTEmbedLoss, as introduced in Solatorio (2024), is a guided variant of the more standard in-batch negatives (MultipleNegativesRankingLoss) loss. Both loss functions are provided with a list of (anchor, positive) pairs, but while MultipleNegativesRankingLoss uses anchor_i and positive_i as positive pair and all positive_j with i != j as negative pairs, GISTEmbedLoss uses a second model to guide the in-batch negative sample selection.

This can be very useful, because it is plausible that anchor_i and positive_j are actually quite semantically similar. In this case, GISTEmbedLoss would not consider them a negative pair, while MultipleNegativesRankingLoss would. When finetuning MPNet-base on the AllNLI dataset, these are the Spearman correlation based on cosine similarity using the STS Benchmark dev set (higher is better):

312039399-ef5d4042-a739-41f6-a6ca-eddc7f901411 The blue line is MultipleNegativesRankingLoss, whereas the grey line is GISTEmbedLoss with the small all-MiniLM-L6-v2 as the guide model. Note that all-MiniLM-L6-v2 by itself does not reach 88 Spearman correlation on this dataset, so this is really the effect of two models (mpnet-base and all-MiniLM-L6-v2) reaching a performance that they could not reach separately.

All changes

... (truncated)

Commits
  • a5f7749 Release v2.6.0
  • 13a9f3f [feat] Add binary & scalar embedding quantization support to Sentence Trans...
  • e6af66f Also update return docstring of encode_multi_process (#2548)
  • caaa28d Fix SentenceTransformer encode documentation return type default (numpy vecto...
  • 87f4180 [deprecation] Deprecate save_to_hub in favor of push_to_hub; add safe_s...
  • fc2a2d8 Enable saving modules as pytorch_model.bin (#2542)
  • b9255d9 Add 'get_config_dict' method to GISTEmbedLoss for better model cards (#2543)
  • 465d4f0 Add GISTEmbedLoss (#2535)
  • 66e0ee3 Fix CrossEncoder.rank default value for top_k (#2518)
  • aad0642 Don't always normalize the embeddings in clustering example (#2520)
  • Additional commits viewable in compare view


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dependabot[bot] commented 4 months ago

Superseded by #347.