Course curriculum

  • 1

    🤗 Transformers & 🤗 Datasets for Research and Production

    • Abstract and Bio

    • 🤗 Transformers & 🤗 Datasets for Research and Production
  • 2

    Natural Language Processing in Accelerating Business Growth

    • Abstract and Bio

    • Natural Language Processing in Accelerating Business Growth
  • 3

    Self-supervised Representation Learning for Speech Processing

    • Abstract and Bio

    • Self-supervised Representation Learning for Speech Processing
  • 4

    Transformer Based Approaches to Named Entity Recognition (NER) and Relationship Extraction (RE)

    • Abstract and Bio

    • Transformer Based Approaches to Named Entity Recognition (NER) and Relationship Extraction (RE)
  • 5

    Towards Data Scientist - Friendly Natural Language Processing

    • Abstract and Bio

    • Towards Data Scientist - Friendly Natural Language Processing

Abstracts and Speaker

🤗 Transformers & 🤗 Datasets for Research and Production

The half-day training will train attendees on how to use Hugging Face's Hub as well as the Transformers and Datasets library to efficiently prototype and productize machine learning models.
 The training will cover the following topics
 
 1. Open-Source Philosophy
 2. From Research to Prototyping
 3. From Prototype to Production

   Patrick von Platen, Research Engineer @ Hugging Face

Natural Language Processing in Accelerating Business Growth

In a talk that guides the audience through the key strategies, strengths and challenges around NLP, Dr. Sameer Maskey will deliver the following key takeaways:
 
1. Impact and application of NLP by enterprise across various industries
2. Key NLP techniques that are being leveraged to accelerate business success
3. The do’s and don’ts of adopting and deploying NLP techniques and how it can take businesses to the next level


   Sameer Maskey, PhD, Founder & CEO @ Fusemachines

Self-supervised Representation Learning for Speech Processing

This talk will present self-supervised speech representation learning approaches and their connection to related research areas. Since many of the current methods focused solely on automatic speech recognition as a downstream task, we will review recent efforts on benchmarking learned representations to extend the application of such representations beyond speech recognition.


   Abdel-rahman Mohamed, PhD, Research Scientist @ Meta AI Research

Transformer Based Approaches to Named Entity Recognition (NER) and Relationship Extraction (RE)

Fortunately, Language Models learned by pre-trained Transformers learn a lot about the language of the domain it is trained and fine-tuned on, and therefore NER and RE models based on these Language Models require fewer training examples to deliver the same level of performance. In this workshop, participants will learn about, train, and evaluate Transformer based neural models for NER and RE.


   Sujit Pal, Technology Research Director @ Elsevier Labs

Towards Data Scientist - Friendly Natural Language Processing

When it comes to the Natural Language Processing (NLP) applications, we are usually using various NLP libraries with complex and more importantly incompatible output structures. That makes the integration of NLP features and solutions into the machine learning and data science pipeline difficult and time-consuming. To resolve this issue, the Center for Open Source Data and AI Technologies (CODAIT) has developed Text Extensions for Pandas, an open source library of the extensions that turns Pandas data frames into the universal data structure for NLP and hence offers transparency, simplicity and compatibility. In this talk, we will first talk about how we can solve the real-world text analytic problems with NLP. We then show how the Text Extensions for Pandas make these analyses easier.


   Sepideh Seifzadeh, Principal Data Scientist - Technical Lead @ IBM

   Monireh Ebrahimi, Senior Cognitive Software Developer @ IBM