Pre-train and Fine-tune Language Model with Hugging Face and Gaudi HPU.
In this blog, we provide a general guideline of pre-training and fine-tuning language models using Hugging Face. For illustration, we use pre-training language models for question generation (answer-agnostic) in Korean as running example.Building a digital guide dog for railway passengers with impaired vision
Catching your train on time can be challenging under the best of circumstances. Trains typically only stop for a few minutes, leaving little room for mistakes. For example, at Munich Main station around 240 express trains and 510 regional trains leave from 28 platforms per day. Some trains can also be quite long, up to 346 meters (1,135 ft) for express ICE trains. It is extremely important to quickly find the correct platform and platform section, and then the door closest to a reserved seat needs to be located. This already challenging adventure becomes even more so, if a vision impairment forces a customer to rely exclusively on auditory or tactile feedback. When traveling autonomously, without assistance, it is common practice to walk along the outside of a train, continuously tapping it with a white cane, to discover opened and closed doors (figure 1). While this works in principle, this practice has limitations, both in terms of speed and reliability. We therefore partnered with DB Systel GmbH, the digital partner for all Deutsche Bahn Group companies, to build the Digital Guide Dog. This is a feasibility study based on an AI-powered smartphone application that uses computer vision, auditory and haptic feedback to guide customers to the correct platform section and train car door. In this blog post, we are sharing some of the details and unique challenges that we experienced while the AI model behind this application.Reducing the distance to your Azure ML remote compute jobs
Under (hopefully) rare circumstances, after developing a training script and thorough local testing, it can still happen that the same script fails when executed on a remote AML compute target. Here, we are sharing some best practices around how to debug remote workloads on Azure ML.
