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Hugging Face

Hugging Face

Overview

What is Hugging Face?

Hugging Face is an open-source provider of natural language processing (NLP) technologies.

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Recent Reviews
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Pricing

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Pro Account

$9

Cloud
per month

Enterprise Hub

$20

Cloud
per month per user

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://huggingface.co/pricing

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

Starting price (does not include set up fee)

  • $9 per month
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Product Demos

YouTube Video Transcript Summarizer | Hugging Face Speech-to-Text ASR | Video Summarizer Project

YouTube

Webinar: Special NLP Session with Hugging Face

YouTube

FourthBrain and Hugging Face demo on Building NLP Applications with Transformers

YouTube

TrOCR Transformer-based Optical Character Recognition Microsoft Hugging Face TrOCR Demo

YouTube

GPT-3 Alternative - OPT-175B Hugging Face Language Model Tutorial

YouTube

Easy Custom NLP T5 Model Training Tutorial - Abstractive Summarization Demo with SimpleT5

YouTube
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Product Details

What is Hugging Face?

Hugging Face is an open-source provider of natural language processing (NLP) technologies. The company develops a chatbot applications used to offer a personalized AI-powered communication platform. Its platform analyzes the user's tone and word usage to decide what current affairs it may chat about or what GIFs to send that enable users to chat based on emotions and entertainment.


Hugging Face Screenshots

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Product Presentation

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Hugging Face Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Hugging Face is an open-source provider of natural language processing (NLP) technologies.

Hugging Face starts at $9.

Tungsten TotalAgility are common alternatives for Hugging Face.

The most common users of Hugging Face are from Small Businesses (1-50 employees).
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Comparisons

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Reviews and Ratings

(11)

Reviews

(1-6 of 6)
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Score 10 out of 10
Vetted Review
Verified User
Incentivized
We use Hugging Face models and datasets to design, test a compare multiple approaches for ML projects and, and in general, for research purposes. Thanks to Hugging Face, we do not need extensive training, and our NLP models' fine-tuning is simpler and more cost efficient.
  • NLP models
  • NLP datasets
  • Version control for models and datasets.
  • phonetic models
  • phonetic datasets
Hugging Face is an excellent starting point when working on NLP projects; it is also great for prototyping and developing pipelines for NLP tasks, being those tasks general like embedding representation or specific, like SQUAD models and datasets. It needs more phonetic models or datasets to be as advantageous in that regard.
Vijay Irlapati | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
For most of the ML problems, we use hugging face prediction models as these models give better performance than any other models. It helps in addressing the technological advancements in an organisation. Any organisation that wants to adopt to latest technologies should consider Hugging face. Hugging face has many open-source transformer models hosted. The scope of this product is to give better performance on NLP problems.
  • Has access to hundreds of models useful for any NLP usecase.
  • Gives better accuracy on prediction tasks.
  • Easy to test the model in the website itself to check the accuracy without actually implementing it.
  • Has many algorithms for all the prediction problems.
  • 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.
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
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We use Hugging Face APIs to import the models in our code (mostly language models with weights). This is very important use case as it makes the building part of model very easy. We don't have to spend much time refering to repositories, reading complex ReadMe's. Other than that, we deploy demo apps on the Hugging Face spaces using the gradio tool they provide. This helps in testing out the product very easily by not spending much time on making the UI and also not caring about the compute management.
  • Model APIs
  • Hugging Face Spaces for deploying demo apps
  • Latest updated models available easily
  • Vast support for language parsing and other relevant tasks
  • Facility to deploy on spaces but with better compute
1. First point of start when looking to build something with transformer models. 2. Amazing community to handle your doubts / bugs. 3. Simple description of model and how to use it. 4. Never faced any bug related to size mismatch of weight, or wrong version. The weights and model always stay updated. 5. Very intuitive way to create apps using Gradio and one click deployment on Hugging Face spaces.
Ivan Cui | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
I have use Hugging Face to develop Natural Language Processing applications for other amazon web services customers. Some of the common applications are intelligent document processing, call center support, machine translation, sentiment analysis and so on. These Hugging Face solutions are implemented on the cloud for easier manage and maintain as well.
  • Easy to use API
  • Super well integrated to o cloud
  • Large community
  • Better documentation
  • Have dedicated support
If your organization is looking for a fast turn around development time on natural language processing machine learning use case, and your organization is also well developed on cloud platforms such as google cloud or amazon web services, then implementing Hugging Face into your solution is a very good idea
Score 9 out of 10
Vetted Review
Verified User
Incentivized
In our organization, Hugging Face is used for a lot of text-processing and natural language processing tasks. Hugging face addresses our business problem of finding good NLP algorithms for running classification analysis by using open source API to keep our costs low. The website provides world's best systems for doing NLP and using this model, we are able to do advance NLP analyses and classification using text data.
  • NLP
  • Neural Network
  • Open Source
  • Difficult to find certain models
  • Lacking descriptions
Hugging Face is well suited for situations where you need access to specific advanced natural language processing libraries. The quality of the libraries available on the platform is extremely good and it can be used to make production level classification models. This is also great for building MVP use cases for NLP projects.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Hugging Face keeps handy when you work with machine learning projects specially neuronal networks. Neuronal networks are complex and becomes cumbersome when you perform transformation on it. We are resolving this issue with Hugging Face. It has huge amount of libraries with pre-trained models which are optimised too. Hugging Face plays a vital role in machine learning models.
  • Great collections of ML libraries (transformers)
  • Well Documented in multi language.
  • Perform complex transformations.
  • Open source driven community
  • Libraries documentations can be improved.
  • sometime hard to select appropriate libraries.
  • Can add more features.
If you or organisation heavily work on machine learning complex neuronal network models Hugging Face has vast libraries transformers who makes the the model easier and efficient. it gives the ability to check the outcome and make changes accordingly and save a lot of time. If you're not working on machine learning neuronal network models it might not appropriate though try it.
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