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