Contextual brand safety is an ongoing series. This is the second blog in this series. Through this series, we talk about steps to be taken to do multi-label text classification in the industry. This blog post talks about model training and evaluation.
Brand safety is an important offering of GumGum. Contextual Brand Safety-I talks about the problem and data preprocessing techniques in depth. In this blog post, we will discuss model training, evaluation and steps to production.
We set up a multi-step mlflow project tracking system to track and store artifacts across each step i.e,
Contextual brand safety is an ongoing series. This is the first blog in this series. Through this series, we talk about steps to be taken to do multi-label text classification in the industry. This blog post sets the stage by talking about the problem and data collection.
GumGum is dedicated to ensuring a brand-safe environment to all our clients; advertisers and publishers alike. In order to implement brand safety, we have a variety of methods which assist in ensuring that we deliver ads on safe, relevant and high-quality content.
One of the most important measures taken to ensure brand safety is Publisher vetting. We ensure that each new content under every publisher is validated against our brand safety guidelines, where the publisher content is filtered in such a way that it does not consist of the…
Be it customer reviews, news articles or conversations between people, when we are tasked with the ordeal of having to figure out what the corpus is about, it is impossible to manually read and summarize them. Topic modeling is a natural language processing technique that extracts latent topics from a corpus of documents. Unlike a classification problem, there are no labels directing this process, hence it is unsupervised. There are many algorithms that perform topic modeling. The most important ones are:
In this blog, we will restrict our discussion to topic modeling using LDA. …