turkle VS Mephisto

Compare turkle vs Mephisto and see what are their differences.

turkle

Django-based clone of Amazon's Mechanical Turk service running in your local environment. (by hltcoe)

Mephisto

A suite of tools for managing crowdsourcing tasks from the inception through to data packaging for research use. (by facebookresearch)
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turkle Mephisto
1 3
142 294
1.4% 0.3%
4.7 9.1
29 days ago 2 days ago
Python Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

turkle

Posts with mentions or reviews of turkle. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-26.

Mephisto

Posts with mentions or reviews of Mephisto. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-26.

What are some alternatives?

When comparing turkle and Mephisto you can also consider the following projects:

pybossa - PYBOSSA is the ultimate crowdsourcing framework (aka microtasking) to analyze or enrich data that can't be processed by machines alone.

hive - A platform for backing crowdsourcing websites, built in golang for elasticsearch

diffgram - The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.