Podcast appearances and mentions of ines montani

What trends and technologies should you be paying attention to today? Are there hot new database servers you should check out? Or will that just be a flash in the pan? I love these forward looking episodes and this one is super fun. I've put together an amazing panel: Gina Häußge, Ines Montani, Richard Campbell, and Calvin Hendryx-Parker. We dive into the recent Stack Overflow Developer survey results as a sounding board for our thoughts on rising and falling trends in the Python and broader developer space. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show The Stack Overflow Survey Results: survey.stackoverflow.co/2024 Panelists Gina Häußge: chaos.social/@foosel Ines Montani: ines.io Richard Campbell: about.me/richard.campbell Calvin Hendryx-Parker: github.com/calvinhp Explosion: explosion.ai spaCy: spacy.io OctoPrint: octoprint.org .NET Rocks: dotnetrocks.com Six Feet Up: sixfeetup.com Stack Overflow: stackoverflow.com Python.org: python.org GitHub Copilot: github.com OpenAI ChatGPT: chat.openai.com Claude: anthropic.com LM Studio: lmstudio.ai Hetzner: hetzner.com Docker: docker.com Aider Chat: github.com Goose AI: goose.ai IndyPy: indypy.org OctoPrint Community Forum: community.octoprint.org spaCy GitHub: github.com Hugging Face: huggingface.co Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike. In this wide-ranging discussion, we dive into: • The evolution of applied NLP and its role in industry • The balance between large language models and smaller, specialized models • Human-in-the-loop distillation for creating faster, more data-private AI systems • The challenges and opportunities in NLP, including modularity, transparency, and privacy • The future of AI and software development • The potential impact of AI regulation on innovation and competition We also touch on their recent transition back to a smaller, more independent-minded company structure and the lessons learned from their journey in the AI startup world. Ines and Matt offer invaluable insights for data scientists, machine learning practitioners, and anyone interested in the practical applications of AI. They share their thoughts on how to approach NLP projects, the importance of data quality, and the role of open-source in advancing the field. Whether you're a seasoned NLP practitioner or just getting started with AI, this episode offers a wealth of knowledge from two of the field's most respected figures. Join us for a discussion that explores the current landscape of AI development, with insights that bridge the gap between cutting-edge research and real-world applications. LINKS The livestream on YouTube (https://youtube.com/live/-6o5-3cP0ik?feature=share) How S&P Global is making markets more transparent with NLP, spaCy and Prodigy (https://explosion.ai/blog/sp-global-commodities) A practical guide to human-in-the-loop distillation (https://explosion.ai/blog/human-in-the-loop-distillation) Laws of Tech: Commoditize Your Complement (https://gwern.net/complement) spaCy: Industrial-Strength Natural Language Processing (https://spacy.io/) LLMs with spaCy (https://spacy.io/usage/large-language-models) Explosion, building developer tools for AI, Machine Learning and Natural Language Processing (https://explosion.ai/) Back to our roots: Company update and future plans, by Matt and Ines (https://explosion.ai/blog/back-to-our-roots-company-update) Matt's detailed blog post: back to our roots (https://honnibal.dev/blog/back-to-our-roots) Ines on twitter (https://x.com/_inesmontani) Matt on twitter (https://x.com/honnibal) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

There hasn't been a boom like the AI boom since the .com days. And it may look like a space destined to be controlled by a couple of tech giants. But Ines Montani thinks open source will play an important role in the future of AI. I hope you join us for this excellent conversation about the future of AI and open source. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Porkbun Talk Python Courses Links from the show Ines Montani on Twitter: @_inesmontani spaCy: spacy.io Prodigy App: prodi.gy Ines' presentation at PyCon Lithuania: youtube.com LM Studio: lmstudio.ai Little Bobby Tables: xkcd.com spaCy and NLP course: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Ines Montani, co-founder and CEO of Explosion, speaks with host Jeremy Jung about solving problems using natural language processing (NLP). They cover generative vs predictive tasks, creating a pipeline and breaking down problems, labeling examples for training, fine-tuning models, using LLMs to label data and build prototypes, and the spaCy NLP library.

This episode features co-founder and CEO of Explosion, Ines Montani. Listen in as we discuss the evolution of the web and machine learning, the development of SpaCy, Natural Language Processing vs. Natural Language Understanding, the misconceptions of starting a software company, and so much more! Ines is a software developer working on Artificial Intelligence and Natural Language Processing technologies.She's the co-founder and CEO of Explosion, the company behind SpaCy, one of the leading open-source libraries for NLP in Python and Prodigy, an annotation tool to help create training data for Machine Learning Models. Ines has an academic background in Communication Science, Media Studies and Linguistics and has been coding and designing websites since she was 11. She's been the keynote speaker at Python and Data Science conferences around the world.Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.Listen on YouTube: https://youtu.be/XNFqFT-DZwo?si=Aj75TmsCyBQTyWqqListen on your favorite podcast platform:https://rss.com/podcasts/learning-from-machine-learning/1190862/References in the Episodehttps://explosion.ai/https://spacy.io/https://ines.io/Applied NLP ThinkingInes Montani - How to Ignore Most Startup Advice and Build a Decent Software Business Ines Montani: Incorporating LLMs into practical NLP workflowsInes Montani (spaCy) - Large Language Models from Prototype to Production [PyData Südwest] Confectionhttps://github.com/explosion/confectionResources to learn more about Learning from Machine Learninghttps://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

This episode dives into the multifaceted realm of Natural Language Processing (NLP) with a guest expert, Ines Montani (#). The discussion revolves around the use of Python in the context of NLP, the complexities of language, the design of label schemes, and how educators and students can dive into this intriguing area. The conversation also touches on tools such as Prodigy (https://prodi.gy/) and Spacy (https://spacy.io/), as well as practical applications, including a humorous digression on the popular game, Fortnite (https://www.epicgames.com/fortnite/). Teachers are encouraged to explore NLP with their students, emphasizing the importance of hands-on experience and data annotation. There's also a mention of a fascinating project involving a "magic mirror (https://www.raspberrypi.com/tutorials/how-to-build-a-super-slim-smart-mirror/)" powered by Raspberry Pi (https://www.raspberrypi.org/). Special Guest: Ines Montani.

Newsrooms have been exploring how Natural Language Processing (NLP) and Information Extractions (IE) can modularise content as reusable elements to use for different storytelling formats and for meeting users' needs. But what bits and pieces are worth looking at and how can journalists do that? Marieta and Laura talk to Ines Montani, founder of ExplosionAI and Anna Vissens, lead data scientist at The Guardian, to find out more about modular journalism and the importance of transparency when using AI.

In this episode we speak to Ines Montani, co-founder and CEO of Explosion, a developer of Artificial Intelligence and Natural Language Processing technologies. We discuss how ML and NLP work behind the scenes, how developers should think about applied NLP, the common languages and frameworks used to build ML and NLP applications, and the challenges that come with running them at scale. About Ines MontaniInes Montani is co-founder and CEO of Explosion. A software developer working on Artificial Intelligence and Natural Language Processing technologies, her company Explosion are makers of spaCy, one of the leading open-source libraries for Natural Language Processing in Python, and Prodigy, a modern annotation tool for creating training data for machine learning models. In 2020, Montani became a Fellow of the Python Software Foundation.Other things mentioned:ExplosionspaCyProdigyTensorflowHugging Face PythonCypressHyperSlackLet us know what you think on Twitter:https://twitter.com/consoledotdevhttps://twitter.com/davidmyttonhttps://twitter.com/_inesmontani Or by email: hello@console.devAbout ConsoleConsole is the place developers go to find the best tools. Our weekly newsletter picks out the most interesting tools and new releases. We keep track of everything - dev tools, devops, cloud, and APIs - so you don't have to. Sign up for free at: https://console.devRecorded: 2022-04-06

The world of AI is changing fast. And the AI / ML space is a bit out of the ordinary for software developers. Typically in software, we can prove that given a certain situations, the code will always behave the same. We can point to where and why a decision is made. ML isn't like that. We set it up and then it takes on a life of its own. Regulators and governments are starting to step in and make rules over AI. The EU is one of the first to do so. That's why it's great to have Ines Montani and Katharine Jarmul, both awesome data scientists and EU residents, here to give us an overview of the coming regulations and other benefits and pitfalls of the AI / ML space. Links from the show Katharine Jarmul on Twitter: @kjam Katharine's site: kjamistan.com Ines Montani on Twitter: @_inesmontani Explosion AI: explosion.ai EU proposes new Artificial Intelligence Regulation: nortonrosefulbright.com The EU's leaked AI regulation is ambitious but disappointingly vague: techmonitor.ai EU ARTIFICIAL INTELLIGENCE ACT: eur-lex.europa.eu/legal-content Facial Recognition Technology Ban Passed by King County Council: kingcounty.gov On the Opportunities and Risks of Foundation Models paper: arxiv.org thoughtworks: thoughtworks.com I don't care about cookies extension: chrome.google.com Everybody hates “FLoC,” Google's tracking plan: arstechnica.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON SignalWire Talk Python Training

Our guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool. I've always found Ines to be personally inspiring in the work that she and her team produce as well as how they present themselves to the world, so it was a real pleasure to get to dive into the weeds as to exactly how that happens. We also discuss how NLP works in production, what reproducibility means for ML projects and much more. Special Guest: Ines Montani.

The team explore the many flaws present in machine learning and AI with Monash University Department of Neuroscience's Dr Jarrel Seah and Ines Montani from Explosion AI & Prodigy. Featuring the latest news from the intersection of science and tech from presenters Laura, Jo and Lily.Website: https://www.rrr.org.au/explore/programs/byte-into-itFacebook: https://www.facebook.com/3RRRFMByteIntoIT/Twitter: https://twitter.com/byteintoit

You've heard that software developers and startups go hand-in-hand. But what about data scientists? Of course they! But how do you turn your data science skill set into a data science business skill set? What are some of the areas ripe for launching such a business into? On this episode, I welcome back 4 prior guests who have all walked their own version of this path and are currently running successful Python-based Data Science startups: * Ines Montani from Explosion AI * Matthew Rocklin from Coiled * Jonathon Morgan from Yonder AI * William Stein from Cocalc Links from the show Ines Montani Twitter: @_inesmontani Explosion AI: explosion.ai Matthew Rocklin Twitter: @mrocklin Coiled: coiled.io Jobs @ Coiled: jobs.lever.co/coiled Jonathon Morgan Twitter: @jonathonmorgan Yonder AI: yonder-ai.com William Stein Twitter: @wstein389 CoCalc: cocalc.com Talk Python Live Streams: talkpython.fm/youtube Sentry Promo Code: TALKPYTHON2021 Sponsors Sentry Error Monitoring, Code TALKPYTHON Linode Talk Python Training

2020 will be one for the history books, won't it? I've put together a great group to look back on 2020 - from the Python perspective. Join Brian and Michael along with Cecil Phillip, Ines Montani, Jay Miller, Paul Everitt, Reuven Lerner, and Matt Harrison for a light-hearted and fun look back on the major Python events of 2020. Video version of this episode: Watch on YouTube Guests Cecil Phillip Ines Montani Jay Miller Paul Everitt Reuven Lerner Matt Harrison

2020 will be one for the history books, won't it? I've put together a great group to look back on 2020 - from the Python perspective. Join me along with Cecil Phillip, Ines Montani, Jay Miller, Paul Everitt, Reuven Lerner, Matt Harrison, and Brian Okken for a light-hearted and fun look back on the major Python events of 2020. Links from the show Video version of this episode: youtube.com Guests Cecil Phillip: @cecilphillip Ines Montani: @_inesmontani Jay Miller: @kjaymiller Paul Everitt: @paulweveritt Reuven Lerner: @reuvenmlerner Matt Harrison: @__mharrison__ Brian Okken: @brianokken Sponsors Talk Python Training

In today's episode, senior technical evangelist Mady Mantha is joined by Ines Montani and Matthew Honnibal, co-founders of Explosion, a software company that specializes in developer tools for Natural Language Processing (NLP) applications, and core contributors to spaCy, an open-source software library for advanced NLP. Join in to learn about how to build resilient NLP applications, some challenges and pitfalls developers typically experience, and some ways to overcome them. --- Send in a voice message: https://anchor.fm/rasachats/message

Sofie and Ines walk us through how the new spaCy library helps build end to end SOTA natural language processing workflows. Ines Montani is the co-founder of Explosion AI, a digital studio specializing in tools for AI technology. She's a core developer of spaCy, one of the leading open-source libraries for Natural Language Processing in Python and Prodigy, a new data annotation tool powered by active learning. Before founding Explosion AI, she was a freelance front-end developer and strategist. https://twitter.com/_inesmontani Sofie Van Landeghem is a Natural Language Processing and Machine Learning engineer at Explosion.ai. She is a Software Engineer at heart, with an absurd love for quality assurance and testing, introducing proper levels of abstraction, and ensuring code robustness and modularity. She has more than 12 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. https://twitter.com/oxykodit https://spacy.io/ https://prodi.gy/ https://thinc.ai/ https://explosion.ai/ Topics covered: 0:00 Sneak peek 0:35 intro 2:29 How spaCy was started 6:11 Business model, open source 9:55 What was spaCy designed to solve? 12:23 advances in NLP and modern practices in industry 17:19 what differentiates spaCy from a more research focused NLP library? 19:28 Multi-lingual/domain specific support 23:52 spaCy V3 configuration 28:16 Thoughts on Python, Syphon, other programming languages for ML 33:45 Making things clear and reproducible 37:30 prodigy and getting good training data 44:09 most underrated aspect of ML 51:00 hardest part of putting models into production Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: bit.ly/2WdrUvI Spotify: bit.ly/2SqtadF Google:tiny.cc/GD_Google We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. app.wandb.ai/gallery

We talked about how computers increased Ines's creativity as a teen. We talked about Ines's disappointment at German university studies. We discussed the steps she took away from computer sciences, and how she ended up combining two of her passions: developing software and linguistics into one new field: computer linguistics. We finished by talking about having developers as customers and dog-feeding your own products.Here are the links of the show:https://www.twitter.com/_inesmontanihttps://explosion.aihttps://spacy.iohttps://prodi.gyhttps://ines.iohttps://ines.io/blog/how-i-started-codingCreditsMusic Aye by Yung Kartz is licensed CC BY-NC-ND 4.0.Your host is Timothée (Tim) Bourguignon, more about him at timbourguignon.fr.Gift the podcast a rating on one of the major platforms https://devjourney.info/subscribe.htmlSupport the podcast, support us on Patreon: https://bit.ly/devjpatreonSupport the show (http://bit.ly/2yBfySB)

Special guest: Ines Montani Michael #1: VS Code Device Simulator Want to experiment with MicroPython? Teaching a course with little IoT devices? Circuit Playground Express BBC micro:bit Adafruit CLUE with a screen Get a free VS code extension that adds a high fidelity simulator Easily create the starter code (main.py) Interact with all the sensors (buttons, motion sensors, acceleration detection, device shake detection, etc.) Deploy and debug on a real device when ready Had the team over on Talk Python. Brian #2: pytest 6.0.0rc1 New features You can put configuration in pyproject.toml Inline type annotations. Most user facing API and internal code. New flags - --no-header - --no-summary - --strict-config : error on unknown config key - --code-highlight : turn on/off code highlighting in terminal Recursive comparison for dataclass and attrs Tons of fixes Improved documentation There’s a list of breaking changes and deprications. But really, nothing in the list seems like a big deal to me. Plugin authors, including myself, should go test this. Already found one problem. pytest-check: stop on fail works fine, but failing tests marked with xfail show up as xpass. Gonna have to look into that. And might have to recruit Anthony to help out again. To try it: pip install pytest==6.0.0rc1 I’m currently running through the pytest book to make sure it all still works with pytest 6. So far, so good. The one hiccup I’ve found so far, TinyDB had a breaking change with 4.0, so you need to pip install tinydb==3.15.2 to get the tasks project to run right. I should have pinned that in the original setup.py. However, all of the pytest stuff is still valid. Guido just tweeted: “Yay type annotations in pytest!” Ines #3: TextAttack Python framework for adversarial attacks and data augmentation for natural language processing What are adversarial attacks? You might have seen examples like these: image classifier predicting a cat even if the image is complete noise people at protests wearing shirts and masks with certain patterns to trick facial recognition Google Translate hallucinating bible texts if you feed it nonsense or repetitive syllables What does it mean to "understand" a model? How does it behave in different situations, with unexpected data? We can't just inspect the weights – that's not how neural networks work To understand a model, we need to run it and find behaviours we don't like TextAttack lets you run various different “attacks” from the current academic literature It also lets you create more robust training data using data augmentation, for example, replacing words with synonyms, swapping characters, etc. Michael #4: What is the core of the Python programming language? By Brett Cannon, core developer Brett and I discussed Python implementation for WebAssembly before Get Python into the browser, but with the fact that both iOS and Android support running JavaScript as part of an app it would also get Python on to mobile. We have lived with CPython for so long that I suspect most of us simply think that "Python == CPython". PyPy tries to be so compatible that they will implement implementation details of CPython. Basically most implementations of Python strive to pass CPython's test suite and to be as compatible with CPython as possible. Python’s dynamic nature makes it hard to do outside of an interpreter That has led Brett to contemplate the question of what exactly is Python? How much would one have to implement to compile Python directly to WebAssembly and still be considered a Python implementation? Does Python need a REPL? Could you live without locals()? How much compatibility is necessary to be useful? The answer dictates how hard it is to implement Python and how compatible it would be with preexisting software. [Brett] has no answers It might make sense to develop a compiler that translates Python code directly to WebAssembly and sacrifice some compatibility for performance. It might make sense to develop an interpreter that targets WebAssembly's design but maintains a lot of compatibility with preexisting code. It might make sense to simply support RustPython in their WebAssembly endeavours. Maybe Pyodide will get us there. Michael’s thoughts: How about a Python standard language spec? A standard-library “standard???!?” spec. It’s possible - .NET did it. What would be build if we could build it with web assembly? Interesting options open up, say with NodeJS like capabilities, front-end frameworks This could be MUCH bigger if we got browser makes to support alternative runtimes through WebAssembly Brian #5: Getting started with Pathlib Chris May Blog post: Stop working so hard on paths. Get started with pathlib! PDF “field guide”: Getting started with Pathlib Really great introduction to Pathlib Some of the info This file as a path object: Path(__file__) Parent directory: Path(__file__).parent Absolute path: Path(__file__).parent.resolve() Two levels up: Path(__file__).resolve(strict=True).parents[1] See pdf for explanation. Current working dir: Path.cwd() Path building with / Working with files and folders Using glob Finding parts of paths and file names. Any time spent learning Pathlib is worth it. If I can do it in Pathlib, I do. It makes my code more readable. Ines #6: Data Version Control (DVC) We're currently working on v3.0 of spaCy and one of the big features is going to be a completely new way to train your custom models, manage end-to-end training workflows and make your experiments reproducible It will also integrate with a tool called DVC (short for Data Version Control), which we've started using internally DVC is an open-source tool for version control, specifically for machine learning and data Machine learning = code + data. You can check your code into a Git repo, but you can't really check in your datasets and model weights. So it's very difficult to keep track of changes. You can think of DVC as “Git for data” and the command line usage is actually pretty similar – for example, you run dvc init to initialize a repo and dvc add to start tracking assets DVC lets you track any assets by adding meta files to your repository. So everything, including your data, is versioned, and you can always go back to the commit with the best accuracy It also builds a dependency graph based on the inputs and outputs of each step, so you only have to re-run a step if things changed for example, you might have a preprocessing step that converts your data and then a step that trains your model. If the data hasn't changed, you don't have to re-run the preprocessing step. They recently released a new tool called CML (short for Continuous Machine Learning), which we haven't tried yet. CI for Machine Learning Previews look pretty cool: you can submit a PR with some changes and a GitHub action will run your experiment and auto-comment on the PR with the results, changes in accuracy and some graphs (similar to tools like Code Coverage etc.) Extra Michael: Podcast Python Search API package, by Anton Zhiyanov Mid-string f-string upgrades coming to PyCharm. And Flynt! via Colin Martin Ines: Built-in generic types in 3.9 (PEP 585): you can now write list[str] ! Brian: https://testandcode.com/120: FastAPI & Typer - Sebastián Ramírez Jokes Fast API Job Experience Sebastián Ramírez - @tiangolo I saw a job post the other day. It required 4+ years of experience in FastAPI. I couldn't apply as I only have 1.5+ years of experience since I created that thing. Maybe it's time to re-evaluate that "years of experience = skill level". Defragged Zebra

Video Version: https://youtu.be/C5DGFSDlMBM Subscribe here to the newsletter: https://tinyletter.com/sanyambhutani In this episode, Sanyam Bhutani interviews a machine learning hero who's been inspiring and empowering many, many future heroes through their open source work: Ines Montani, co founder of explosion, the company behind spacy, prodigy and thinc.ai. They talk about all of the amazing work that is, and the team at explosion has been doing, her journey into the field of programming her journey into the field of NLP and her journey at explosion. They talk about all these things, all these three things along with all of the amazing work that Explosion has been putting out. Yes, Spacy, prodigy and thinc.ai, thinc,ai is the latest framework that has been put out along with open source development and the NLP industry. Personal Note: Yes, those are a lot of things that have been All (Luckily for us), been covered in this single podcast! This has been a treat for me & I hope you enjoy it as much as I did. Links: https://explosion.ai/about https://spacy.io https://prodi.gy https://thinc.ai Follow: Ines Montani: https://twitter.com/_inesmontani https://www.linkedin.com/in/inesmontani/ https://ines.io Sanyam Bhutani: https://twitter.com/bhutanisanyam1 Blog: sanyambhutani.com About: https://sanyambhutani.com/tag/chaitimedatascience/ A show for Interviews with Practitioners, Kagglers & Researchers and all things Data Science hosted by Sanyam Bhutani. You can expect weekly episodes every available as Video, Podcast, and blogposts. If you'd like to support the podcast: https://www.patreon.com/chaitimedatascience Intro track: Flow by LiQWYD https://soundcloud.com/liqwyd --- Send in a voice message: https://anchor.fm/chaitimedatascience/message

SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!

SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!

In der zweiten Podcastfolge der Reihe zu natürlicher Sprachverarbeitung ist Ines Montani zu Gast, Entwicklerin von SpaCy und Mitgründerin von Prodigy. Zunächst sprechen wir generell über den Umgang mit Sprache, warum dieser so komplex ist und wie die open source Bibliothek spaCy hier hilft. Dabei geht es um typische Aufgaben wie Part of Speach Tagging, Lemmatization und Named Entity Recognition genauso wie um geeignete Einsatz-Szenarien in der Industrie. Des Weiteren gibt Ines Einblicke in ihre tägliche Arbeit am open source Tool und erklärt, warum NLP Modelle auch ohne GPU trainierbar sein müssen und Prodigy kein Interesse an den Daten seiner Kunden hat. Letztlich geben wir einen Überblick über das wachsende spaCy Ökosystem, einen Rückblick auf die spaCy in Real Life Konferenz und Ines gewährt einen Ausblick in zukünftige Entwicklungen bei spaCy und Prodigy. SpaCy Online Kurs: https://course.spacy.io/ SpaCy IRL Videos: https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc Coreference Resolution: https://github.com/huggingface/neuralcoref Sci-SpaCy: https://github.com/allenai/scispacy

We are delighted to welcome Ines Montani and Matt Honnibal, the developers of spaCy - a powerful and advanced library for NLP. Everything you've ever wanted to know about the wonderful spaCy library is right here in the latest DataHack Radio podcast. Highlights of topic covered: - The idea behind developing spaCy - spaCy's awesome evolution from the first alpha release to the current version 2.1 - Use cases of spaCy including a couple of surprising applications - Ines and Matt's advice to NLP enthusiasts Read the full article here: https://www.analyticsvidhya.com/blog/2019/05/datahack-radio-ines-montani-matthew-honnibal-brains-behind-spacy

In this episode of PyDataSci, we’re joined by Ines Montani, Cofounder of Explosion, Co-developer of SpaCy and lead developer of Prodigy. Ines and I caught up to discuss her various projects, including the aforementioned SpaCy, an open-source NLP library built with a focus on industry and production use cases. The complete show notes for this episode can be found at twimlai.com/talk/262. Check out the rest of the PyDataSci series at twimlai.com/pydatasci. We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey. Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community. 

Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development workflows. Prodigy is an annotation tool implemented as a python library, and it comes with a web application and a command line interface. A developer can define input data streams and design simple annotation interfaces. Prodigy can help break down complex annotation decisions into a series of binary decisions, and it provides easy integration with spaCy models. Developers can specify how models should be modified as new annotations come in in an active learning framework. Prodigy: https://prodi.gy Prodigy recipe scripts: https://github.com/explosion/prodigy-recipes Twitter: https://twitter.com/_inesmontani https://twitter.com/honnibal