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"text": "With great scientific breakthrough comes solid engineering and open communities. The Natural Language Processing (NLP) community has benefited greatly from the open culture in sharing knowledge, data, and software. The primary objective of this workshop is to further the sharing of insights on the engineering and community aspects of creating, developing, and maintaining NLP open source software (OSS), which we seldom talk about in scientific publications. Our secondary goal is to promote synergies between different open source projects and encourage cross-software collaborations and comparisons.", |
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"text": "We refer to Natural Language Processing OSS as an umbrella term that not only covers traditional syntactic, semantic, phonetic, and pragmatic applications; we extend the definition to include taskspecific applications (e.g., machine translation, information retrieval, question-answering systems), low-level string processing that contains valid linguistic information (e.g. Unicode creation for new languages, language-based character set definitions) and machine learning/artificial intelligence frameworks with functionalities focusing on text applications.", |
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"text": "In the earlier days of NLP, linguistic software was often monolithic and the learning curve to install, use, and extend the tools was steep and frustrating. More often than not, NLP OSS developers/users interact in siloed communities within the ecologies of their respective projects. In addition to the engineering aspects of NLP software, the open source movement has brought a community aspect that we often overlook in building impactful NLP technologies.", |
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"text": "An example of precious OSS knowledge comes from SpaCy developer Montani (2017), who shared her thoughts and challenges of maintaining commercial NLP-OSS, such as handling open issues on the issue tracker, model release and packaging strategy and monetizing NLP OSS for sustainability. 1", |
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"text": "More recently, the Transformers library created by Hugging Face, has gathered much interest from the community by open sourcing implementations to use pretrained weights of BERT-like models, in a clean and well-organized structure. The interoperability of various pretrained models trained with different tools in one library enables quick benchmarking across the models, as well as developing best practices for reading/saving serialized interoperable. 2 We hope that the NLP-OSS workshop becomes the intellectual forum to collate various open source knowledge beyond the scientific contribution, announce new software/features, promote the open source culture and best practices that go beyond the conferences. ", |
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"section": "Introduction", |
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"text": "This talk covers what it means to operationalize Machine Learning (ML) models. It starts by analyzing the difference between ML in research vs. in production, ML systems vs. traditional software, as well as myths about ML production. It then goes over the principles of good ML systems design and introduces an iterative framework for ML systems design, from scoping the project, data management, model development, deployment, maintenance, to business analysis. It covers the differences between DataOps, ML Engineering, MLOps, and data science, and where each fits into the framework. The talk ends with a survey of the ML production ecosystem, the economics of open source, and open-core businesses.", |
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"text": "Chip Huyen is an engineer who develops tools and best practices for machine learning production. She's currently with Snorkel A and she'll be teaching Machine Learning Systems Design at Stanford. Previously, she was with Netflix, NVIDIA, Primer. She's also the author of four bestselling Vietnamese books.", |
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"text": "Interactions in Word-processing Spencer Kelly Freelance Developer", |
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"section": "On Typing: Historical and Potential", |
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"text": "People love typing, in a surprising and universal way. In this talk we look at the development of word-processing, and the design-decisions in this historic interface. Can NLP contribute to word-processing, without making it worse? What would a text-centered computer really look like?", |
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"text": "We look at the history of punctuation, keyboards, and markup languages. We look at Wikipedia, text-editors, and data structures -with the goal of authoring usable data in text. ", |
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"text": "Principles of Good Machine Learning Systems Design Chip Huyen, Stanford & Snorkel AI On Typing: Historical and Potential Interactions in Word-processing Spencer Kelly, Freelance Developer An Introduction to Transfer Learning in NLP and HuggingFace Thomas Wolf, Huggingface vii", |
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"content": "<table><tr><td colspan=\"2\">An Introduction to Transfer Learning in NLP and</td></tr><tr><td colspan=\"2\">HuggingFace</td></tr><tr><td colspan=\"2\">Thomas Wolf Huggingface</td></tr><tr><td>Abstract</td><td/></tr><tr><td>In this talk I'll start by introducing the re-cent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures. The second part of the talk will be dedi-cated to an introduction of the open-source tools released by HuggingFace, in par-ticular our Transformers, Tokenizers and Datasets libraries and our models.</td><td>Spencer is the author of compromise -a small natural language processing library for the browser. He is a web developer, and maintainer of open-source libraries. His background is in the semantic web and Wikipedia. Today his work focuses on cre-ating infographics. His open-source work is funded by freelance web development. He is from Toronto, Canada.</td></tr></table>", |
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"text": "BiographyThomas Wolf is co-founder and Chief Science Officer of HuggingFace. His team is on a mission to catalyze and democratize NLP research. Prior to HuggingFace, Thomas gained a Ph.D. in physics, and later a law degree. He worked as a physics researcher and a European Patent Attorney.", |
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"type_str": "table" |
|
} |
|
} |
|
} |
|
} |