Fine tune and deploy Hugging Face NLP models

Webinar:
Fine-tune and deploy
Hugging Face NLP models
BastienVerdebout
Product Manager @ OVHcloud
https://twitter.com/BastienOvh
AbhishekThakur
Data scientist @ Hugging Face
https://twitter.com/abhi1thakur
Stay tuned, we’ll be getting started very soon!

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Housekeeping

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

Hugging Face
On a mission to solve NLP,
one commit at a time.

www.HuggingFace.co
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

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

Let’s use NLP pre-trained models !

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
 …

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, … » )
 …

.. 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, …
https://huggingface.co/openai-detectorhttps://transformer.huggingface.co
Text generation Fake detector

Fine-tuning
Why and how
with Abhishek Thakur

Fine-tuning Transformer
Models
Abhishek Thakur

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

Fine-tuning often gives good results

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

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

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

Live demo
Hugging Face model fine-tuning
via OVHcloud AI Training

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 : https://huggingface.co/transformers/contributing.html
3
OVHcloud now provides all required tools for your AI workflows.
From Storage to Processing to Training to Serving !
Contact-us or browse https://www.ovhcloud.com/en/public-cloud/ai-solutions/

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 coil.com
 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)

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Thank you! Open for
questions
twitter.com/ovhcloud
facebook.com/ovhcloud
OVHcloud
twitter.com/huggingface
twitter.com/abhi1thakur

Fine tune and deploy Hugging Face NLP models

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