<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wohlwend-etal-2019-flambe">
    <titleInfo>
        <title>Flambé: A Customizable Framework for Machine Learning Experiments</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jeremy</namePart>
        <namePart type="family">Wohlwend</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nicholas</namePart>
        <namePart type="family">Matthews</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="family">Itzcovich</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Costa-jussà</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Enrique</namePart>
            <namePart type="family">Alfonseca</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambé achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.</abstract>
    <identifier type="citekey">wohlwend-etal-2019-flambe</identifier>
    <identifier type="doi">10.18653/v1/P19-3029</identifier>
    <location>
        <url>https://aclanthology.org/P19-3029</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>181</start>
            <end>188</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Flambé: A Customizable Framework for Machine Learning Experiments
%A Wohlwend, Jeremy
%A Matthews, Nicholas
%A Itzcovich, Ivan
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wohlwend-etal-2019-flambe
%X Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambé achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.
%R 10.18653/v1/P19-3029
%U https://aclanthology.org/P19-3029
%U https://doi.org/10.18653/v1/P19-3029
%P 181-188

Markdown (Informal)

[Flambé: A Customizable Framework for Machine Learning Experiments](https://aclanthology.org/P19-3029) (Wohlwend et al., ACL 2019)

• Flambé: A Customizable Framework for Machine Learning Experiments (Wohlwend et al., ACL 2019)

ACL

• Jeremy Wohlwend, Nicholas Matthews, and Ivan Itzcovich. 2019. Flambé: A Customizable Framework for Machine Learning Experiments. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 181–188, Florence, Italy. Association for Computational Linguistics.