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🤗 Sentence Transformers v2.4.0 for embedding models is now out! It introduces a lot of powerful features, such as:
🤗 Sentence Transformers v2.4.0 for embedding models is now out! It introduces a lot of powerful features, such as:
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1. Matryoshka Loss function - you can now train & perform inference on 🪆 Matryoshka Embedding models. See also our blogpost: 
1. Matryoshka Loss function - you can now train & perform inference on 🪆 Matryoshka Embedding models. See also our blogpost: 
link
https://huggingface.co/blog/matryoshka
https://huggingface.co/blog/matryoshka
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2. CoSENTLoss & AnglELoss: State of the art loss functions. These are quite interesting, they outperform CosineSimilarityLoss on nearly all benchmarks as a drop-in replacement! See also the docs: 
2. CoSENTLoss & AnglELoss: State of the art loss functions. These are quite interesting, they outperform CosineSimilarityLoss on nearly all benchmarks as a drop-in replacement! See also the docs: 
link
https://sbert.net/docs/package_reference/losses.html#cosentloss
https://sbert.net/docs/package_reference/losses.html#cosentloss
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3. Prompt templates: Many popular models such as 
3. Prompt templates: Many popular models such as 
resource
https://huggingface.co/intfloat/multilingual-e5-large
model
intfloat/multilingual-e5-large
https://huggingface.co/intfloat/multilingual-e5-large
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 and 
 and 
resource
https://huggingface.co/BAAI/bge-large-en-v1.5
model
BAAI/bge-large-en-v1.5
https://huggingface.co/BAAI/bge-large-en-v1.5
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 prefix their texts with prompts, so this adds configuration options to automatically include prompts using 
 prefix their texts with prompts, so this adds configuration options to automatically include prompts using 
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`model.encode(..., prompt_name="query")`
model.encode(..., prompt_name="query")
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 which will include a prompt with the name "query". More info in the docs: 
 which will include a prompt with the name "query". More info in the docs: 
link
https://sbert.net/examples/applications/computing-embeddings/README.html#prompt-templates
https://sbert.net/examples/applications/computing-embeddings/README.html#prompt-templates
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4. Instructor support: Support for the INSTRUCTOR line of models, such as 
4. Instructor support: Support for the INSTRUCTOR line of models, such as 
resource
https://huggingface.co/hkunlp/instructor-large
model
hkunlp/instructor-large
https://huggingface.co/hkunlp/instructor-large
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. Learn how to use them here: 
. Learn how to use them here: 
link
https://sbert.net/docs/pretrained_models.html#instructor-models
https://sbert.net/docs/pretrained_models.html#instructor-models
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5. Removed NLTK & sentencepiece dependencies: Should allow for a smaller installation & a slightly faster import!
5. Removed NLTK & sentencepiece dependencies: Should allow for a smaller installation & a slightly faster import!
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6. Updated documentation: a new Loss Overview section: 
6. Updated documentation: a new Loss Overview section: 
link
https://sbert.net/docs/training/loss_overview.html
https://sbert.net/docs/training/loss_overview.html
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 and more detailed loss functions: 
 and more detailed loss functions: 
link
https://sbert.net/docs/package_reference/losses.html
https://sbert.net/docs/package_reference/losses.html
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And much more! See the full release notes here: 
And much more! See the full release notes here: 
link
https://github.com/UKPLab/sentence-transformers/releases/tag/v2.4.0
https://github.com/UKPLab/sentence-transformers/releases/tag/v2.4.0
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Some more very exciting updates are still on the horizon!
Some more very exciting updates are still on the horizon!
🤗 Sentence Transformers v2.4.0 for embedding models is now out! It introduces a lot of powerful features, such as:

1. Matryoshka Loss function - you can now train & perform inference on 🪆 Matryoshka Embedding models. See also our blogpost: https://huggingface.co/blog/matryoshka

2. CoSENTLoss & AnglELoss: State of the art loss functions. These are quite interesting, they outperform CosineSimilarityLoss on nearly all benchmarks as a drop-in replacement! See also the docs: https://sbert.net/docs/package_reference/losses.html#cosentloss

3. Prompt templates: Many popular models such as https://huggingface.co/intfloat/multilingual-e5-large and https://huggingface.co/BAAI/bge-large-en-v1.5 prefix their texts with prompts, so this adds configuration options to automatically include prompts using `model.encode(..., prompt_name="query")` which will include a prompt with the name "query". More info in the docs: https://sbert.net/examples/applications/computing-embeddings/README.html#prompt-templates

4. Instructor support: Support for the INSTRUCTOR line of models, such as https://huggingface.co/hkunlp/instructor-large. Learn how to use them here: https://sbert.net/docs/pretrained_models.html#instructor-models

5. Removed NLTK & sentencepiece dependencies: Should allow for a smaller installation & a slightly faster import!

6. Updated documentation: a new Loss Overview section: https://sbert.net/docs/training/loss_overview.html and more detailed loss functions: https://sbert.net/docs/package_reference/losses.html

And much more! See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v2.4.0

Some more very exciting updates are still on the horizon!
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