Bump sentence-transformers from 2.2.2 to 4.0.2

[dependabot]

Bumps sentence-transformers from 2.2.2 to 4.0.2.

Release notes

Sourced from sentence-transformers's releases.

v4.0.2 - Safer reranker max sequence length logic, typing issues, FSDP & device placement

This patch release updates some logic for maximum sequence lengths, typing issues, FSDP training, and distributed training device placement.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==4.0.2
Inference only, use one of:
pip install sentence-transformers==4.0.2
pip install sentence-transformers[onnx-gpu]==4.0.2
pip install sentence-transformers[onnx]==4.0.2
pip install sentence-transformers[openvino]==4.0.2

Safer CrossEncoder (reranker) maximum sequence length

When loading CrossEncoder models, we now rely on the minimum of the tokenizer model_max_length and the config max_position_embeddings (if they exist), rather than only relying on the latter if it exists. This previously resulted in the maximum sequence length of BAAI/bge-reranker-base being 514, whereas it can only handle sequences up to 512 tokens.

from sentence_transformers import CrossEncoder
model = CrossEncoder("BAAI/bge-reranker-base")
print(model.max_length)
=> 512
The texts for which to predict similarity scores
query = "How many people live in Berlin?"
passages = [
"Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.",
"In 2013 around 600,000 Berliners were registered in one of the more than 2,300 sport and fitness clubs.",
]
scores = model.predict([(query, passage) for passage in passages])
print(scores)
=> [0.99953485 0.01062613]
Or test really long inputs to ensure that there's no crash:
score = model.predict([["one " * 1000, "two " * 1000]])
print(score)
=> [0.95482624]

Note that you can use the activation_fn option with torch.nn.Identity() to avoid the default Sigmoid that maps everything to [0, 1]:

from sentence_transformers import CrossEncoder
import torch
model = CrossEncoder("BAAI/bge-reranker-base", activation_fn=torch.nn.Identity())
The texts for which to predict similarity scores
query = "How many people live in Berlin?"
</tr></table>

... (truncated)

Commits

• 6d48aee Release v4.0.2
• c9b87ac Merge branch 'master' into v4.0-release
• 8d73d4f Specify that exported ONNX/OpenVINO models don't include pooling/normalizatio...
• a51d9c9 Add missing docstring for push_to_hub (#3306)
• 07f53c5 Update default device assignment in get_device_name for distributed trainin...
• 9b9856e Attempt to fix CI (#3305)
• 5aa7f93 [cross-encoder] Set the tokenizer model_max_length to the min. of model_max...
• b9e3257 Avoid "Only if model is wrapped" check which is faulty for FSDP (#3295)
• b3512bc Move encode typings back to .py from .pyi (#3297)
• e33f278 Fix some broken docs redirects (#3296)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #64.