Know what you can do with tagtog with the following tutorials. Moreover, we publish other tips on our 🗞 Medium blog.
tagtog is a collaborative text annotation platform to find, create, and maintain NLP datasets efficiently. Accessible on the Cloud and On-Premises. Find a quick introduction in this article.
Learn how to create and use an custom ML model from scratch just by using some text annotations. We are going to show you how to build a model that extracts dates in text. All will take 5 minutes of your time.
Webhooks can be used to notify your AI model when you have labelled new data in tagtog, so it can be retrained and improve its performance
Overlapping annotations increase the flexibility and allow you to make the most out of your data. Learn how to use them and in which scenarios.
Dictionaries are simple controlled vocabularies, and yet a powerful resource when you have a well-defined list of items you want to recognize in text, especially if those items are identified with different names. Learn how to use them and when to use them with a practical example.
Here we show you to easily train an ML model given a few text annotations. Later we use this model to analyze some of the tweets from Donald Trump and identify automatically those connected with the environment. Let's see what he thinks about it!
tagtog gives support to annotate Markdown files. Therefore images, nested lists, or code blocks are fully supported. This opens many new possibilities for annotation. This article focuses on three.
Some useful tips and suggestions that may be useful for anyone who is managing a team of annotators. tagtog allows users to filter documents based on whether a given user has confirmed them (or any user has confirmed them). This feature, despite it may not look like such a big deal, it can be used for many different things. In this post we discuss some of the applications 🚀.
When multiple annotators produce annotations for the same data, tagtog chooses the annotations from the available-best annotator for each annotation task. Check out step by step how to setup this process.
There may be several reasons why your annotators do not agree on the annotation tasks. It is important to mitigate these risks as soon as possible by identifying the causes. Discover how to prevent a low Inter-Annotator Agreement.
If multiple annotators work on the same data, as a result, there are multiple annotation versions. We define adjudication as the process to resolve inconsistencies among these versions before a version is promoted to the gold standard. This process is manual, semi-automatic or automatic. Learn the differences and when to use each of them.