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Fine tune and deploy Hugging Face NLP models

  1. Webinar: Fine-tune and deploy Hugging Face NLP models BastienVerdebout Product Manager @ OVHcloud AbhishekThakur Data scientist @ Hugging Face Stay tuned, we’ll be getting started very soon!
  2. Submit your questions using the ‘questions’ tab under Chat. Follow us & tweet the session @OVHcloud The recording will be sent after today’s session Email us at Housekeeping
  3. 3  Hugging Face ? NLP ?  Fine-Tuning  Demo : Fine-tuning  Demo : Deployment in production  Sum-up Agenda Webinar : fine-tune and deploy Hugging Face NLP models
  4. Hugging Face On a mission to solve NLP, one commit at a time.
  5. Hugging Face is the most popular open source Natural Language Processing (NLP) library ! • Community model hub with more than 4,000 models • More than 3To of models stored in the cloud • More than 5 new models uploaded each day • Founded in 2016 • 20+ employees
  6. Natural Language Processing : the concept Natural Language Processing Models Understand what is being said/written Determine the right answer Give an answer humanly-readable NLP is broadly defined as the automatic manipulation of natural language, like speech and text, by software. It’s a subset of artificial intelligence. How do I say « Hello ! » in Spanish ? Output : « Hola! » Natural Language Understanding Machine Learning Natural Language Generation
  7. Let’s use NLP pre-trained models !
  8. Use-case example #1 : sentiment analysis Code sample Use-cases Analyze emails, product reviews, tweets, ... Then react :  Brand : monitor your brand reputation on social medias  E-commerce : remove bad products, highlight good ones  Support team : priorize negative emails  …
  9. Use-case example #2 : question answering Code sample Use-cases You can drastically improve user experience :  Website : contextual « search engines »  Internal documentation : easier to find what you need  Voice Assistants (« Alexa, … » )  …
  10. .. And much more ! Use-cases  Text Analysis : detect fake news, detect spams and scams, …  Text Generation : better video games, better AI assistants, SEO, …  Text Summarization : auto generated excerpts for products, for webpages, for SEO, … Text generation Fake detector
  11. Fine-tuning Why and how with Abhishek Thakur
  12. Fine-tuning Transformer Models Abhishek Thakur
  13. Translation Sentiment Classification Chatbots / VAs Autocomplete Entity Extraction Question Answering Review Rating Prediction Search Engine Speech to Text Topic Extraction Applications of natural language processing
  14. Why fine tune?
  15. Fine-tuning often gives good results
  16. Pretrained model Adaptation Head Tokenizer Transfer Learning for text classification 17 Jim Henson was a puppeteer Jim Hens on was a Tokenization 1106 7 5567 245 120 1.2 2.7 0.6 -0.2 3.7 9.1 -2.1 3.1 1.5 -4.7 2.4 6.7 6.1 2.4 7.3 -0.6 -3.1 2.5 1.9 -0.1 0.7 2.1 4.2 -3.1 Classifi er model Convert to vocabula ry indices Pretraine d model Tru e 0.78 86 Fals e - 0.22 3 via Thomas Wolf
  17. A – Transfer Learning for text classification 18 Remarks: ❏ The error rate goes down quickly! After one epoch we already have >90% accuracy. ⇨ Fine-tuning is highly data efficient in Transfer Learning ❏ We took our pre-training & fine-tuning hyper-parameters straight from the literature on related models. ⇨ Fine-tuning is often robust to the exact choice of hyper-parameters via Thomas Wolf
  18. Transformers library We’ve built an opinionated framework providing state-of-the-art general-purpose tools for Natural Language Understanding and Generation. Features:  Super easy to use – fast to on-board  For everyone – NLP researchers, practitioners, educators  State-of-the-Art performances – on both NLU and NLG tasks  Reduce costs/footprint – 30+ pretrained models in 100+ languages  Deep interoperability between TensorFlow 2.0 and PyTorch
  19. Live demo Hugging Face model fine-tuning via OVHcloud AI Training
  20. AI Training : demo video
  21. ML Serving : demo video
  22. Sum-up ! Bastien
  23. Sum-up ! 1 NLP is fun and useful. Even more since Hugging Face is here 2 It’s community based. Don’t hesitate to contribute ! More info : 3 OVHcloud now provides all required tools for your AI workflows. From Storage to Processing to Training to Serving ! Contact-us or browse
  24. 100€ voucher on OVHcloud For Training / Serving / Storage / … You’ll receive it via email this week Try it by yourself ! Santa is here Image credit to  50 hours of GPU training (1,75€ /GPU V100s /hour )  12 months of Model Serving (1 node)  10TB of data in Object Storage (1 month)
  25. 2626 Thank you! Open for questions OVHcloud

Hinweis der Redaktion

  1. Introduction: John Digby, presales at OVHcloud for 8 months, with a technical background in Virtualisation, Storage and general infrastructure. Introduction: Bradley Harrad, Product Marketing Manager for the Northern Europe cluster for 4 months, 10 years experience in marketing and strategic alliance partnerships, taking product to market, communicating solutions clearly and engaging with customers to help develop the market.
  2. Before we begin, I’d like to go over a few housekeeping points. Throughout the webinar, you can submit your questions to us. You can see on the control panel the questions tab. If you pop your questions in there, we’ll have some time at the end to dig into them. If you have any other issues with sound or visuals please let us know through the chat function as well. If you want to follow us on social media we are on Twitter, at OVHcloud_UK. We will also be recording today’s session. And at the end of the webinar we will send out a recording of the presentations. So you’ll get that via the email address you registered with us. And finally, if you have any other questions, you can email those to us at
  3. I will start by giving you a quick overview of OVHcloud. 1. Who we are OVHcloud are and the businesses evolution after 20 years. Bradley Harrad 2. From Cloud-ready to business-ready – Overcoming challenges during cloud transformation, importance of multi-cloud strategy, and how OVHcloud can provide multiple solutions, Ensuring success. Bradley Harrad 3. Data Sovereignty and how OVHcloud positions for GDPR, the cloud act, etc… Bradley Harrad 4. Total cost of ownership of On-prem versus private cloud/Software defined Data Centre at OVHcloud John Digby 5. Three scenarios about how current OVHcloud customers consume OVHcloud Software Defined Data Centre in the form of Datacentre Replacement, Disaster Recovery and Datacentre extension John Digby