# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4

PortSystem          1.0
PortGroup           github 1.0
PortGroup           python 1.0

github.setup        UKPLab sentence-transformers 2.0.0 v
revision            0
name                py-${github.project}

categories-append   textproc
license             Apache-2
maintainers         nomaintainer
platforms           {darwin any}
supported_archs     noarch

description         Sentence Embeddings using BERT / RoBERTa / XLM-R

long_description    This framework provides an easy method to compute \
                    dense vector representations for sentences, \
                    paragraphs, and images. The models are based on \
                    transformer networks like BERT / RoBERTa / \
                    XLM-RoBERTa etc. and achieve state-of-the-art \
                    performance in various task. Text is embedding in \
                    vector space such that similar text is close and \
                    can efficiently be found using cosine similarity. \
                    We provide an increasing number of \
                    state-of-the-art pretrained models for more than \
                    100 languages, fine-tuned for various use-cases. \
                    Further, this framework allows an easy fine-tuning \
                    of custom embeddings models, to achieve maximal \
                    performance on your specific task.

checksums           rmd160  ccbf8233ed8ac93e0d1dff3e8bdcf34746b22ca6 \
                    sha256  d316491c05560e578a6722d917e66b93ca2ad2266b77a8bfd27c300b379d7ac7 \
                    size    14667470

python.versions     39

if {${name} ne ${subport}} {
    depends_build-append \
                    port:py${python.version}-setuptools

    depends_run-append \
                    port:py${python.version}-nltk \
                    port:py${python.version}-numpy \
                    port:py${python.version}-pytorch \
                    port:py${python.version}-scikit-learn \
                    port:py${python.version}-scipy \
                    port:py${python.version}-sentencepiece \
                    port:py${python.version}-tqdm \
                    port:py${python.version}-transformers

    post-destroot {
        set docdir ${prefix}/share/doc/${subport}
        xinstall -d ${destroot}${docdir}
        xinstall -m 0644 -W ${worksrcpath} LICENSE README.md \
            ${destroot}${docdir}
    }

    test.run        yes
}