An Introduction to Transfer Learning in NLP and HuggingFace

Thomas Wolf

Power of Transfer Learning in NLP: Build a Text Classification Model Using BERT

Jayeeta Putatunda

DeepPavlov Agent: Open-source Framework for Multiskill Conversational AI

Mikhail Burtsev

Can You Sing with All the Voices of the Features?

Yama Anin Aminof

ML conf EU

ML conf EU 2020

The domain of Natural Language Processing have seen a tremendous amount of research and innovation in the past couple of years to tackle the problem of implementing high quality machine learning and AI solutions using natural text. Text Classification is one such area that is extremely important in all sectors like finance, media, product development, etc. Building up a text classification system from scratch for every use case can be challenging in terms of cost as well as resources, considering there is a good amount of dataset to begin training with. Here comes the concept of transfer learning. Using some of the models that has been pre-trained on terates of data and fine-tuning it based on the problem at hand is the new way to efficiently implement machine learning solutions without spending months on data cleaning pipeline. This talk with highlight ways of implementing the newly launched BERT and fine tuning the base model to build an efficient text classifying model. Basic understanding of python is desirable. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

After this talk, you will know how to write the perfect song for your favourite singer! This is not a songwriting retreat but a talk about some of the lyrical, structural, harmonic, and melodic features song analysis includes. We will discuss the extraction of song structures using NLP tools and repetition analysis, musical features, and how to use all of those features to predict which songs fit which artist the best. Attend this talk to discover what is the future that the music industry can achieve with machine learning. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

DeepPavlov Agent is a framework designed to facilitate the development of scalable and production-ready multi-skill virtual assistants, complex dialogue systems, and chatbots. Key features of DeepPavlov Agent include (1) scalability and reliability in the high load environment due to micro-service architecture; (2) ease of adding and orchestrating conversational skills; (3) shared dialogue state memory and NLP annotations accessible to all skills. DeepPavlov DREAM is a socialbot platform with a modular design with the main components such as annotators, skills and selectors run as independent services. These components are conﬁgured and deployed using Docker containers. It allows developers to focus on application development instead of focusing on the intrinsic details of the manual low-level infrastructure conﬁguration. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

In this talk I'll start introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures. The second part of the talk will be dedicated to an introduction of the open-source tools released HuggingFace, in particular our Transformers, Tokenizers and Datasets libraries and our models.

#nlp