Hugging has closed a new round of funding and is now worth $2 billion Solarwinds Chinacimpanu. The company has also been working to build an open source library of natural language processing technologies.
Hugging Face is now worth $2 billion
The Hugging Face brand of artificial intelligence is gaining recognition and value. Its flagship product, the Hugging Face Hub, allows thousands of companies to build technology with machine learning. They can also collaborate on ML models. Several companies, including Google, eBay, and Intel, are customers of Hugging Face.
Hugging Face has been growing at a fast pace. In a recent funding round, the company raised $100 million from Lux Capital and Sequoia Capital. This round brings the company’s valuation to $2 billion.
Hugging Face’s products have been used by more than 5,000 organizations in some capacity. They include a chatbot powered by natural language processing and a library for developing and sharing machine learning models.
Hugging Face has developed an enormous community. The Hugging Face Hub hosts more than 4,000 datasets, 100,000 pre-trained models, and more. And the Hugging Face API lets users access these models via a programming interface.
Hugging Face also offers other products to help businesses manage their AI models. One of its latest offerings is Hugging Face Endpoints on Azure, a machine learning model hosting platform that runs on the cloud. Many more products will likely be integrated with the platform in the future.
Hugging Face’s newest product, Hugging Face Spaces, will allow its community members to become creators. These spaces feature git-based workflows and allow community members to control versioning of projects.
Hugging Face has closed a new round of funding
Hugging Face, a machine learning (ML) startup, has raised a new round of funding. The round was led by New York based venture capital firm Addition, and included Lux Capital and Betaworks Investments LLC.
Hugging Faces has more than a thousand customers, including Amazon Web Services, eBay, Google, Intel, Microsoft, and Yahoo! It is also one of the fastest growing open-source projects in the world. In addition to its NLP capabilities, Hugging Face has an image and audio classification platform, a chatbot with machine learning models, and a host of other ML tools.
Hugging Faces offers a range of natural language processing tools, including text to speech, automatic speech recognition, sentence similarity, fill mask, object detection, and voice activity detection. Its API lets users deploy thousands of models through a programming interface.
Hugging Faces is a community-driven platform, which lets developers share and collaborate on ML models. Hugging Faces provides a library of machine learning and natural language processing models that is available as an open source project on GitHub. Using the API, developers can access thousands of models and build models with the models’ AutoTrain feature.
Hugging Face is working to build a machine learning hub and ecosystem. As such, it is trying to become a GitHub for ML.
Hugging Faces has already accumulated a large number of stars on GitHub. With the recent announcement of its $100 million Series C round, the company is ready to move on from its current NLP capabilities into broader ML space.
Hugging Face has been building an open source library of natural language processing technologies
Hugging Face, a New York-based startup, focuses on building an open source library of natural language processing technologies. The company was founded in 2016, and has since raised $64 million in six rounds. This has enabled Hugging Face to focus on building an effective and open NLP platform.
Hugging Face provides an API for developers to access thousands of pre-trained models. It also offers a container service for deep learning applications. Among its features is an autotrain feature, which trains the best model for a given set of data. Customers use the platform to build their own NLP applications. Thousands of businesses and organizations have already partnered with Hugging Face. They include Intel, Pfizer, eBay, and Roche.
Hugging Face also recently launched a platform, called Spaces, which allows developers to easily share and develop their own NLP models. Developers can also control versioning, and perform git-based workflows.
Conclusion
Another feature of Hugging Face is its Model Card, which shows users the potential biases their models might have. Users can also use Transformers, which are three lines of code, to generate text. Similarly, developers can use the Inference API to easily access models via a programming interface.In addition, Hugging Face has acquired a platform, called Gradio, which allows people to interact with machine learning models. Currently, the interface is web-based.
