Bricks is an open-source content library for natural language processing, which provides the building blocks to quickly and easily enrich, transform or analyze text data for machine learning projects. For many Pythonistas, contributing to an open-source project seems scary and intimidating. In this tutorial, we offer a hands-on experience in which programmers and data scientists learn how to code their own building blocks and share their creations with the community with ease.

With an average of 3.2 new papers published on Arxiv every day in 2022, causal inference has exploded in popularity, attracting large amount of talent and interest from top researchers and institutions including industry giants like Amazon or Microsoft. Text data, with its high complexity, posits an exciting challenge for causal inference community. In the workshop, we'll review the latest advances in the field of Causal NLP and implement a causal Transformer model to demonstrate how to translate these developments into a practical solution that can bring real business value. All in Python!

Models in Natural Language Processing are fun to train but can be difficult to deploy. The size of their models, libraries and necessary files can be challenging, especially in a microservice environment. When services should be built as lightweight and slim as possible, large (language) models can lead to a lot of problems. With a recent real-world use case as an example, which runs productively for over a year and in 10 different languages, I will walk you through my experiences with deploying NLP models. What kind of pitfalls, shortcuts, and tricks are possible while bringing an NLP model to production?

In this talk, you will learn about different ways and possibilities to deploy NLP services. I will speak briefly about the way leading from data to model and a running service (without going into much detail) before I will focus on the MLOps part in the end. I will take you with me on my past journey of struggles and successes so that you don’t need to take these detours by yourselves.

Running machine learning models in a production environment brings its own challenges. In this talk we would like to present our solution of a machine learning lifecycle for the text-based cataloging classification system from idealo.de. We will share lessons learned and talk about our experiences during the lifecycle migration from a hosted cluster to a cloud solution within the last 3 years. In addition, we will outline how we embedded our ML components as part of the overall idealo.de processing architecture.

How can NLP and Haystack help answer sustainability questions and fight climate change? In this talk we walkthrough our experience using Haystack to build Question Answering Models for the climate change and sustainability domain. We discuss how we did it, some of the challenges we faced, and what we learnt along the way!

Forget specialized hardware. Get GPU-class performance on your commodity CPUs with compound sparsity and sparsity-aware inference execution. This talk will demonstrate the power of compound sparsity for model compression and inference speedup for NLP and CV domains, with a special focus on the recently popular Large Language Models. The combination of structured + unstructured pruning (to 90%+ sparsity), quantization, and knowledge distillation can be used to create models that run an order of magnitude faster than their dense counterparts, without a noticeable drop in accuracy. The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.

Everybody knows our yellow vans, trucks and planes around the world. But do you know how data drives our business and how we leverage algorithms and technology in our core operations? We will share some “behind the scenes” insights on Deutsche Post DHL Group’s journey towards a Data-Driven Company. • Large-Scale Use Cases: Challenging and high impact Use Cases in all major areas of logistics, including Computer Vision and NLP • Fancy Algorithms: Deep-Neural Networks, TSP Solvers and the standard toolkit of a Data Scientist • Modern Tooling: Cloud Platforms, Kubernetes , Kubeflow, Auto ML • No rusty working mode: small, self-organized, agile project teams, combining state of the art Machine Learning with MLOps best practices • A young, motivated and international team – German skills are only “nice to have” But we have more to offer than slides filled with buzzwords. We will demonstrate our passion for our work, deep dive into our largest use cases that impact your everyday life and share our approach for a timeseries forecasting library - combining data science, software engineering and technology for efficient and easy to maintain machine learning projects..

Sometimes the internet can be a bit overwhelming, so I thought I would make a tool to create a personalized summary of it! In this talk, I'll demonstrate a personal front-page project that allows me to filter info on the internet on a certain topic, built using spaCy, an open-source library for NLP, and Prodigy, a scriptable annotation tool. With this project, I learned about the power of working with tools that provide extensive customizability without sacrificing ease of use. Throughout the talk, I'll also discuss how design concepts of developer tools can improve the development experience when building complex and adaptable software.

“Got an NLP problem nowadays? Use transformers! Just download a pretrained model from the hub!” - every blog article ever

As if it’s that easy, because nearly all pretrained models have a very annoying limitation: they can only process short input sequences. Not every NLP practitioner happens to work on tweets, but instead many of us have to deal with longer input sequences. What started as a minor design choice for BERT, got cemented by the research community over the years and now turns out to be my biggest headache: the 512 tokens limit.

In this talk, we’ll ask a lot of dumb questions and get an equal number of unsatisfying answers:

1. How much text actually fits into 512 tokens? Spoiler: not enough to solve my use case, and I bet a lot of your use cases, too.

2. I can feed a sequence of any length into an RNN, why do transformers even have a limit? We’ll look into the architecture in more detail to understand that.

3. Somebody smart must have thought about this sequence length issue before, or not? Prepare yourself for a rant about benchmarks in NLP research.

4. So what can we do to handle longer input sequences? Enjoy my collection of mediocre workarounds.

Chatbots are fun to use, ranging from simple chit-chat (“How are you today?”) to more sophisticated use cases like shopping assistants, or the diagnosis of technical or medical problems. Despite their mostly simple user interaction, chatbots must combine various complex NLP concepts to deliver convincing, intelligent, or even witty results.

With the advancing development of machine learning models and the availability of open source frameworks and libraries, chatbots are becoming more powerful every day and at the same time easier to implement. Yet, depending on the concrete use case, the implementation must be approached in specific ways. In the design process of chatbots it is crucial to define the language processing tasks thoroughly and to choose from a variety of techniques wisely.

In this talk, we will look together at common concepts and techniques in modern chatbot implementation as well as practical experiences from an E-mobility bot that was developed using the Rasa framework.

Innovations such as sentence-transformers, neural search and vector databases fueled a very fast development of question-answering systems recently. At scieneers, we wanted to test those components to satisfy our own information needs using a slack-bot that will answer our questions by reading through our internal documents and slack-conversations. We therefore leveraged the HayStack QA-Framework in combination with a Weaviate vector database and many fine-tuned NLP-models. This talk will give you insights in both, the technical challenges we faced and the organizational learnings we took.

Large Language Models (LLM), like ChatGPT, have shown miraculous performances on various tasks. But there are still unsolved issues with these models: they can be confidently wrong and their knowledge becomes outdated. GPT also does not have any of the information that you have stored in your own data. In this talk, you'll learn how to use Haystack, an open source framework, to chain LLMs with other models and components to overcome these issues. We will build a practical application using these techniques. And you will walk away with a deeper understanding of how to use LLMs to build NLP products that work.

In this talk, we will explore the build-measure-learn paradigm and the role of baselines in natural language processing (NLP). We will cover the common NLP tasks of classification, clustering, search, and named entity recognition, and describe the baseline approaches that can be used for each task. We will also discuss how to move beyond these baselines through weak learning and transfer learning. By the end of this talk, attendees will have a better understanding of how to establish and improve upon baselines in NLP.

In this talk, I'll show how large language models such as GPT-3 complement rather than replace existing machine learning workflows. Initial annotations are gathered from the OpenAI API via zero- or few-shot learning, and then corrected by a human decision maker using an annotation tool. The resulting annotations can then be used to train and evaluate models as normal. This process results in higher accuracy than can be achieved from the OpenAI API alone, with the added benefit that you'll own and control the model for runtime.

The materials presented during this tutorial are open source and can be used by coaches and tutors who want to teach their students how to use Python for text processing and text classification. (A minimal understanding of programming (in any language) is required by the students)