Best starting point for NLP projects
We use Hugging Face models and datasets to design, test a compare multiple approaches for ML projects and, and in general, for research …
Starting at $9 per month
View Pricing Overview
What is Hugging Face?
Hugging Face is an open-source provider of natural language processing (NLP) technologies.
Recent Reviews
Pricing
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
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
Product Details
- About
- Competitors
- Tech Details
- FAQs
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
Hugging Face Video
Product Presentation
Hugging Face Competitors
- Kofax TotalAgility
- NLTK
- Microsoft Knowledge Exploration Service
Hugging Face Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
Hugging Face is an open-source provider of natural language processing (NLP) technologies.
Hugging Face starts at $9.
Kofax TotalAgility are common alternatives for Hugging Face.
The most common users of Hugging Face are from Mid-sized Companies (51-1,000 employees).
Comparisons
Compare with
Reviews and Ratings
(10)Reviews
(1-4 of 4)Companies can't remove reviews or game the system. Here's why
December 10, 2022
Best starting point for NLP projects
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
- Multiple models for multiple tasks to try
- Multiple dataset for multiple tasks to try
- Using Hugging Face is cost efficient vs other paid alternatives
Still need to run more experiments to be able to compare them.
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.
- It’s better accuracy over all other models.
- Easy to implement with little understanding on the models.
- Has access to hundreds of models and easy to change.
- Can attract more clients if using the latest and advanced technologies.
- Thus increases ROI of a company.
- Can have impact the business highly by delivering the best product to customer
- TensorFlow and Keras
Hugging face is the latest technology built using transformers hence it gives better performance than other similar products.
December 07, 2022
Hugging Face review from a cloud Data Scientist
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
- Open source
- Simple to use
- Rich api
- Increase use case number
- Support more features
- Increase ROI
- decreased development time
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.
November 28, 2022
A great collection of vast libraries for ML models...!
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.
- Hugging Face libraries(transformers)
- Neural networks models
- Models are highly efficient, cost effective and time saver.
- Hugging Face is cost and time saving.
- Pay is less, you pay what you use, doesn't affect much.
- Overall positive impact on business.
I haven't tried any other product expect Hugging face yet.
5
3
- Machine Learning
- Neural networks