Hugging Face vs. MangoApps

If an organisation has more access to data and have access to high end computers like GPUs it’s recommended to use Hugging face as it will give better accuracy than any other models. If an organisation having less data and has less access to GPUsis looking for decent performance then traditional algorithms are more appropriate than hugging face

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The ability to create optional interest groups for users to join if they like is a great way brings users together, and promote group meet ups and events. The polls ability is always great for taking a quick check of people's availability, we've used it to gauge interest in events and dates. One thing that could be improved is that there is both a messaging and an instant message functionality which seems a little redundant having users being able to message you from different places in separate inboxes, however this may be due to our set up.

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• Model APIs
• Hugging Face Spaces for deploying demo apps
• Latest updated models available easily
• Vast support for language parsing and other relevant tasks

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• MangoApps provide a large number of options to interact within a community. People can post updates, ask questions, create polls, quizzes, wiki, surveys, chats, or forms.
• The Web interface is a modern application (Facebook inspired). The navigation (although not the most efficient) is overall quite intuitive.
• MangoApps has a web interface, and apps for desktop (Windows, Mac), and mobile.
• MangoApps features a centralized notification system. Unread messages are notified from the Web app, the mobile, or the desktop app. The synchronization status is synchronized.
• MangoApps integrates with Active Directory.

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• Most of the Hugging face models are of big size, hence difficult to work if there is no access to high computational system like GPU.
• It’s good to have some visualization tool in hugging face for viewing model architecture.
• I recommend to implement hugging face lite version so that it can run on any system with less specifications.

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• The messaging system is quite new and doesn't always clear down when read
• The mentions tab can get very busy if you're added to groups
• Lots of customisation can make it overwhelming when editing

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There are some other services offer similar capacity as to Hugging Face, but not entirely the same. For example, amazon web services have a machine learning service called Comprehend, which offer a set of easy to use APIs to do machine translation and entity recognition and some other common NLP use case.

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Although, Facebook at Work featured a very modern interface and was praised by many of the beta testers, we primarily didn't choose Facebook at Work due to data security reasons.

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• Hugging Face is cost and time saving.
• Pay is less, you pay what you use, doesn't affect much.
• Overall positive impact on business.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Users can connect from all over the business, especially useful when working from home
• A friendly informal communication method outside of teams/slack/emails
• A way to communicate events to the whole business

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info