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Our friendly team would love to hear from you.
Day 1
Day 2
Day 3
Day 4
Day 5
This day is a general introduction to NLP and the context in which its tasks are performed. The main goal is to discover steps that should be performed before feeding the data to the neural network, talk about how AI models work with textual data, and introduce the participants to the Hugging Face ecosystem and their libraries which are a staple of NLP workflows.
Beginner
This day’s course can be divided into two parts. The introduction to the various tools and Hugging Face ecosystem, that are more of a high-level demonstration than a challenge, and the more advanced sections such as the Tokenization, Training custom Tokenizers, Word Embeddings, and Transformer Architecture, some of them with more theory than others. All newly introduced terms and techniques are explained from scratch, thus leaving no one in the dark.
The beginner audience, with little to no NLP experience, is best suited for this day, since during the course its participants get to know a little bit of everything from the tokenization, through the transformers library and its use cases to the whole Hugging Face ecosystem.
However, even the advanced users, already acquainted with the presented tools may find some of the more theoretical sections interesting and valuable, since an understanding of them is vital for successful participation in the following days of the course.
Throughout the various stages of the workshop we will be introducing ways to deal with the imbalanced datasets, whether it’s during the training or evaluation.
On this day we take another step into the world of NLP, focusing on one of the most versatile tasks in the field – text classification. We learn about the metrics with which you can measure a model’s performance, discover ways to work with imbalanced datasets and most importantly explore different ways to classify text.
Intermediate
The difficulty for this part of the course varies between medium (for the sections like the introduction of metrics, Masked Language Modeling, or working with datasets) and hard (like managing class imbalance, usage of SHAP, and especially working with Torch framework to implement the MLP).
In this part of the course we expect the more advanced participants to thrive, as we introduce more complex ways to work with the neural networks, including devising network architecture with PyTorch.
Less experienced users should also find a lot of interesting parts, such as new metrics, fine-tuning of models with transformers API, or working with the datasets.
In this workshop, we dive deep into various Token Classification problems, highlighting their main challenges, potential pitfalls, and strategies to overcome them. We’ll cover preprocessing techniques for data, and methods to evaluate results tailored to specific tasks, including leveraging several new libraries.
Intermediate
This particular workshop’s difficulty can safely be evaluated as medium as it mostly leverages what the participants should already be familiar with from the transformers library and pure Python, while providing more details on the theoretical side of things.
We expect this part of the course to appeal to both the beginner and the advanced participants, as we go deeper into possible use-cases of NLP and discover another of its tasks, while mostly using the transformers API that was described in detail in the previous parts of the course.
To those more interested in the technicalities of model training and evaluation we introduce the usage of monitoring tools and a new evaluation library.
Continuing our journey through the NLP field with the use of the transformer models. This time we focus on the variants that leverage the full transformer architecture, also known as seq2seq models. We will explore two of the tasks those models thrive in: question answering and text summarization, learn how to measure the quality of text generated by a model and try to create a multi-task model able to perform both aforementioned tasks.
Intermediate
Considering the similarity to the previous workshop, this one is also of moderate difficulty. Just like the former, it contains significant theoretical content, while also touching upon important technical aspects of the systems solving those tasks, once again relying heavily on the transformers library.
As mentioned in the ‘level’ section, due to similarities to the day 3’s workshop, we expect this part of the course to be enjoyable and valuable for participants on all levels of the knowledge tree.
While keeping the formula similar to the previous workshop, we provide new information by introducing a whole new set of NLP problems, classical and innovative metrics to evaluate the performance of models solving those tasks and methods of text generation, important for yet another architecture variant of Transformers.
Venturing away from two previous days, we introduce the participants to the concept of LLMs, prompt engineering, as well as the zero-shot and few-shot learning techniques, translating from fine-tuning a smaller, dedicated model for each task to using one LLM with fitting prompts for all of them.
We compare results achieved by those two approaches on various tasks from previous days and explore newly introduced aspects regarding LLMs.
Beginner
This workshop acts as an introduction to the wide field of LLMs, thus its difficulty level is low. We aim to make the transition from the smaller models to LLMs as gentle and easy as possible, slowly, but surely building the foundation for the more advanced techniques and aspects of leveraging the biggest neural networks.
While we hope that everyone could gain some useful knowledge from this part of the course, we have to admit that it is mostly aimed at those who, have barely touched upon the Large Language Models in general, as this workshop takes the participants a step lower from using the UI of LLMs available online to being able to tweak model’s hyperparameters and try some new, albeit simple, prompting techniques.
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