Mechanical turk hit template

Here's a demo of the task interface:. Skip to content. Star Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats 25 commits. Failed to load latest commit information. Both the Tagging of an Image project template and the Transcription from an Image template include a placeholder for a single image followed by three input text fields.

There are two ways to change the variable name. The first is to double-click on the image placeholder and edit the URL field. Also make sure that your variable name is a single word containing no space.

You can add or remove input fields based on your project requirements. Similarly, if you need an additional input field, you can insert additional fields by copy and pasting the same HTML highlighted above , and updating the name of the input field attribute with a new, unique name.

Your CSV file should contain a column header that matches the variable name referenced in the previous section. Using MTurk to outsource microtasks ensures that work gets done quickly, while freeing up time and resources for the company — so internal staff can focus on higher value activities. With access to a global, on-demand, 24x7 workforce, MTurk enables businesses and organizations to get work done easily and quickly when they need it — without the difficulty associated with dynamically scaling your in-house workforce.

MTurk offers a way to effectively manage labor and overhead costs associated with hiring and managing a temporary workforce. By leveraging the skills of distributed Workers on a pay-per-task model, you can significantly lower costs while achieving results that might not have been possible with just a dedicated team.

MTurk offers developers access to a diverse, on-demand workforce through a flexible user interface or direct integration with a simple API. Organizations can harness the power of crowdsourcing via MTurk for a range of use cases, such as microwork, human insights, and machine learning development.

MTurk can be a great way to minimize the costs and time required for each stage of ML development. It is easy to collect and annotate the massive amounts of data required for training machine learning ML models with MTurk. Building an efficient machine learning model also requires continuous iterations and corrections. An example is drawing bounding boxes to build high-quality datasets for computer vision models, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts.

In particular, we use crowdsourcing platforms such as Amazon Mechanical Turk to build datasets that help our models learn common sense knowledge, which is often necessary to answer basic questions that are easy for humans but still quite hard for machines. Amazon Mechanical Turk provides a flexible platform that enables us to harness human knowledge to advance machine learning research.