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misc/py-sentence-transformers: New port: PyTorch: Ready to use implementations of generative models
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@@ -546,6 +546,7 @@
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SUBDIR += py-schedulefree
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SUBDIR += py-scikit-fusion
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SUBDIR += py-scikit-quant
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SUBDIR += py-sentence-transformers
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SUBDIR += py-serverfiles
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SUBDIR += py-shap2
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SUBDIR += py-shell-gpt
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@@ -0,0 +1,38 @@
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PORTNAME= sentence-transformers
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DISTVERSION= 5.1.0
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CATEGORIES= misc # machine-learning
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MASTER_SITES= PYPI
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PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
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DISTNAME= ${PORTNAME:S/-/_/}-${PORTVERSION}
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MAINTAINER= yuri@FreeBSD.org
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COMMENT= PyTorch: Ready to use implementations of generative models
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WWW= https://www.sbert.net/ \
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https://github.com/UKPLab/sentence-transformers/
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LICENSE= APACHE20
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LICENSE_FILE= ${WRKSRC}/LICENSE
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BUILD_DEPENDS= ${PY_SETUPTOOLS} \
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${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR}
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RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}huggingface-hub>=0.20.0:misc/py-huggingface-hub@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}pillow>0:graphics/py-pillow@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}pytorch>=1.11.0:misc/py-pytorch@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}scikit-learn>0:science/py-scikit-learn@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}scipy>0:science/py-scipy@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}tqdm>0:misc/py-tqdm@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}transformers>=4.41.0<5.0.0:misc/py-transformers@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}typing-extensions>=4.5.0:devel/py-typing-extensions@${PY_FLAVOR}
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RUN_DEPENDS+= ${PYTHON_PKGNAMEPREFIX}tokenizers>0:textproc/py-tokenizers@${PY_FLAVOR}
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USES= python
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USE_PYTHON= pep517 autoplist pytest
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TEST_ENV= ${MAKE_ENV} PYTHONPATH=${STAGEDIR}${PYTHONPREFIX_SITELIBDIR}
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TEST_WRKSRC= ${WRKSRC}/tests
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NO_ARCH= yes
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# tests fail to run, see https://github.com/UKPLab/sentence-transformers/issues/3520
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.include <bsd.port.mk>
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@@ -0,0 +1,3 @@
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TIMESTAMP = 1758260582
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SHA256 (sentence_transformers-5.1.0.tar.gz) = 70c7630697cc1c64ffca328d6e8688430ebd134b3c2df03dc07cb3a016b04739
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SIZE (sentence_transformers-5.1.0.tar.gz) = 370745
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@@ -0,0 +1,8 @@
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This framework provides an easy method to compute embeddings for accessing,
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using, and training state-of-the-art embedding and reranker models. It can be
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used to compute embeddings using Sentence Transformer models (quickstart), to
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calculate similarity scores using Cross-Encoder (a.k.a. reranker) models
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(quickstart) or to generate sparse embeddings using Sparse Encoder models
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(quickstart).
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This unlocks a wide range of applications, including semantic search, semantic
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textual similarity, and paraphrase mining.
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