Hugging Face Reviews and Ratings

Rating: 9.9 out of 10

Score

9.9 out of 10

Reviews

6 Reviews

Best starting point for NLP projects

Rating: 10 out of 10

Incentivized

December 10, 2022

Use Cases and Deployment Scope

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.

Pros

NLP models
NLP datasets
Version control for models and datasets.

Cons

phonetic models
phonetic datasets

Likelihood to Recommend

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.

Verified User

Team Lead in Research & Development (11-50 employees)

Vetted Review

3 years of experience

Amazing open source project that gives best access than any other product

Rating: 10 out of 10

Incentivized

December 9, 2022

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

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

Vijay Irlapati

Associate Lead Engineer in Research & Development at CogniCor Technologies (51-200 employees)

Vetted Review

3 years of experience

View profile

Fastest way to build complex models and deploy demo apps

Rating: 10 out of 10

Incentivized

December 9, 2022

Use Cases and Deployment Scope

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.

Pros

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

Cons

Facility to deploy on spaces but with better compute

Likelihood to Recommend

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.

Verified User

Employee in Research & Development (51-200 employees)

Vetted Review

1 year of experience

Hugging Face review from a cloud Data Scientist

Rating: 9 out of 10

Incentivized

December 7, 2022

Use Cases and Deployment Scope

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.

Pros

Easy to use API
Super well integrated to o cloud
Large community

Cons

Better documentation
Have dedicated support

Likelihood to Recommend

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

Ivan Cui

Data Scientist II in Engineering at Amazon Web Services (AWS) (10,001+ employees)

Vetted Review

2 years of experience

View profile

Must have for all NLP needs

Rating: 9 out of 10

Incentivized

December 6, 2022

Use Cases and Deployment Scope

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.

Pros

NLP
Neural Network
Open Source

Cons

Difficult to find certain models
Lacking descriptions

Likelihood to Recommend

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.

Verified User

Analyst in Research & Development (1-10 employees)

Vetted Review

1 year of experience

A great collection of vast libraries for ML models...!

Rating: 8 out of 10

Incentivized

November 28, 2022

Use Cases and Deployment Scope

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.

Pros

Great collections of ML libraries (transformers)
Well Documented in multi language.
Perform complex transformations.
Open source driven community

Cons

Libraries documentations can be improved.
sometime hard to select appropriate libraries.
Can add more features.

Likelihood to Recommend

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.

Verified User

Team Lead in Engineering (51-200 employees)

Vetted Review

2 years of experience

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