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        "author": "Daniel Hershcovich",
        "author_email": "danielh@cs.huji.ac.il",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Text Processing :: Linguistic"
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        "description": "Transition-based UCCA Parser\n============================\n\nTUPA is a transition-based parser for `Universal Conceptual Cognitive\nAnnotation (UCCA) <http://github.com/huji-nlp/ucca>`__.\n\nRequirements\n~~~~~~~~~~~~\n\n-  Python 3.6\n\nInstall\n~~~~~~~\n\nCreate a Python virtual environment. For example, on Linux:\n\n::\n\n    virtualenv --python=/usr/bin/python3 venv\n    . venv/bin/activate              # on bash\n    source venv/bin/activate.csh     # on csh\n\nInstall the latest release:\n\n::\n\n    pip install tupa\n\nAlternatively, install the latest code from GitHub (may be unstable):\n\n::\n\n    git clone https://github.com/danielhers/tupa\n    cd tupa\n    pip install .\n\nTrain the parser\n~~~~~~~~~~~~~~~~\n\nHaving a directory with UCCA passage files (for example, `the English\nWiki\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-Wiki>`__),\nrun:\n\n::\n\n    python -m tupa -t <train_dir> -d <dev_dir> -c <model_type> -m <model_filename>\n\nThe possible model types are ``sparse``, ``mlp``, and ``bilstm``.\n\nParse a text file\n~~~~~~~~~~~~~~~~~\n\nRun the parser on a text file (here named ``example.txt``) using a\ntrained model:\n\n::\n\n    python -m tupa example.txt -m <model_filename>\n\nAn ``xml`` file will be created per passage (separate by blank lines in\nthe text file).\n\nPre-trained models\n~~~~~~~~~~~~~~~~~~\n\nTo download and extract `a model pre-trained on the Wiki\ncorpus <https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10.tar.gz>`__,\nrun:\n\n::\n\n    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10.tar.gz\n    tar xvzf ucca-bilstm-1.3.10.tar.gz\n\nRun the parser using the model:\n\n::\n\n    python -m tupa example.txt -m models/ucca-bilstm\n\nOther languages\n~~~~~~~~~~~~~~~\n\nTo get `a\nmodel <https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-fr.tar.gz>`__\npre-trained on the `French *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_French-20K>`__\nor `a\nmodel <https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-de.tar.gz>`__\npre-trained on the `German *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_German-20K>`__,\nrun:\n\n::\n\n    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-fr.tar.gz\n    tar xvzf ucca-bilstm-1.3.10-fr.tar.gz\n    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-de.tar.gz\n    tar xvzf ucca-bilstm-1.3.10-de.tar.gz\n\nRun the parser on a French/German text file (separate passages by blank\nlines):\n\n::\n\n    python -m tupa exemple.txt -m models/ucca-bilstm-fr --lang fr\n    python -m tupa beispiel.txt -m models/ucca-bilstm-de --lang de\n\nUsing BERT embeddings\n~~~~~~~~~~~~~~~~~~~~~\n\nIt's possible to use BERT embeddings instead of the standard\nnot-context-aware embeddings. To use them pass the ``--use-bert``\nargument in the relevant command and install the packages in\nrequirements.bert.txt:\n\n::\n\n    python -m pip install -r requirements.bert.txt\n\nSee the possible config options in ``config.py`` (relevant configs are\nwith the prefix ``bert``).\n\nUsing BERT embeddings: Multilingual training\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIt's possible, when using the BERT embeddings, to train a multilingual\nmodel which can leverage cross-lingual transfer and improve results on\nlow-resource languages. To train in the multilingual settings you need\nto: 1) Use BERT embeddings by passing the ``--use-bert`` argument. 2)\nUse the BERT multilingual model by passing the\nargument\\ ``--bert-model=bert-base-multilingual-cased`` 3) Pass the\n``--bert-multilingual=0`` argument. 4) Make sure the UCCA passages files\nhave the ``lang`` property. See the script 'set\\_lang' in the package\n``semstr``.\n\nBERT Performance\n~~~~~~~~~~~~~~~~\n\nHere are the average results over 3 Bert multilingual models trained on\nthe `German *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_German-20K>`__,\n`English\nWiki\\_corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-Wiki>`__\nand only on 15 sentences from the `French *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_French-20K>`__,\nwith the following settings:\n\n::\n\n    bert-model=bert-base-multilingual-cased\n    bert-layers= -1 -2 -3 -4\n    bert-layers-pooling=weighted\n    bert-token-align-by=sum\n\nThe results:\n\n+------------------------+-------------------+------------------+----------------+\n| description            | test primary F1   | test remote F1   | test average   |\n+========================+===================+==================+================+\n| German\\_20K Leagues    | 0.828             | 0.6723           | 0.824          |\n+------------------------+-------------------+------------------+----------------+\n| English\\_20K Leagues   | 0.763             | 0.359            | 0.755          |\n+------------------------+-------------------+------------------+----------------+\n| French\\_20K Leagues    | 0.739             | 0.46             | 0.732          |\n+------------------------+-------------------+------------------+----------------+\n| English\\_Wiki          | 0.789             | 0.581            | 0.784          |\n+------------------------+-------------------+------------------+----------------+\n\n\\*\\ `English *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-20K>`__\nis used as out of domain test.\n\nBERT Pre-trained models\n~~~~~~~~~~~~~~~~~~~~~~~\n\nTo download and extract `a multilingual\nmodel <https://github.com/huji-nlp/tupa/releases/download/v1.4.0/bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz>`__,\nrun:\n\n::\n\n    curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.4.0/bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz\n    tar xvzf bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz\n\nTo run the parser using the mode, use the following command. Pay\nattention that you need to replace ``[example lang]`` with the language\nsymbol of the sentence in ``example.txt`` (fr, en, de, etc.):\n\n::\n\n    python -m tupa example.txt --lang [example lang] -m bert_multilingual_layers_4_layers_pooling_weighed_align_sum\n\nThe model was trained on the `German *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_German-20K>`__,\n`English\nWiki\\_corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-Wiki>`__\nand only on 15 sentences from the `French *20K Leagues*\ncorpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_French-20K>`__.\n\nSee the expected performance at `BERT\nPerformance <#bert-performance>`__.\n\nAuthor\n------\n\n-  Daniel Hershcovich: daniel.hershcovich@gmail.com\n\nContributors\n------------\n\n-  Ofir Arviv: ofir.arviv@mail.huji.ac.il\n\nCitation\n--------\n\nIf you make use of this software, please cite `the following\npaper <http://aclweb.org/anthology/P17-1104>`__:\n\n::\n\n    @InProceedings{hershcovich2017a,\n      author    = {Hershcovich, Daniel  and  Abend, Omri  and  Rappoport, Ari},\n      title     = {A Transition-Based Directed Acyclic Graph Parser for {UCCA}},\n      booktitle = {Proc. of ACL},\n      year      = {2017},\n      pages     = {1127--1138},\n      url       = {http://aclweb.org/anthology/P17-1104}\n    }\n\nThe version of the parser used in the paper is\n`v1.0 <https://github.com/huji-nlp/tupa/releases/tag/v1.0>`__. To\nreproduce the experiments, run:\n\n::\n\n    curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2017.sh | bash\n\nIf you use the French, German or multitask models, please cite `the\nfollowing paper <http://aclweb.org/anthology/P18-1035>`__:\n\n::\n\n    @InProceedings{hershcovich2018multitask,\n      author    = {Hershcovich, Daniel  and  Abend, Omri  and  Rappoport, Ari},\n      title     = {Multitask Parsing Across Semantic Representations},\n      booktitle = {Proc. of ACL},\n      year      = {2018},\n      pages     = {373--385},\n      url       = {http://aclweb.org/anthology/P18-1035}\n    }\n\nThe version of the parser used in the paper is\n`v1.3.3 <https://github.com/huji-nlp/tupa/releases/tag/v1.3.3>`__. To\nreproduce the experiments, run:\n\n::\n\n    curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2018.sh | bash\n\nLicense\n-------\n\nThis package is licensed under the GPLv3 or later license (see\n```LICENSE.txt`` <LICENSE.txt>`__).\n\n|Build Status (Travis CI)| |Build Status (AppVeyor)| |Build Status\n(Docs)| |PyPI version|\n\n.. |Build Status (Travis CI)| image:: https://travis-ci.org/danielhers/tupa.svg?branch=master\n   :target: https://travis-ci.org/danielhers/tupa\n.. |Build Status (AppVeyor)| image:: https://ci.appveyor.com/api/projects/status/github/danielhers/tupa?svg=true\n   :target: https://ci.appveyor.com/project/danielh/tupa\n.. |Build Status (Docs)| image:: https://readthedocs.org/projects/tupa/badge/?version=latest\n   :target: http://tupa.readthedocs.io/en/latest/\n.. |PyPI version| image:: https://badge.fury.io/py/TUPA.svg\n   :target: https://badge.fury.io/py/TUPA\n\n\n",
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