The hugging face will raise $40 million for the library for natural language processing.
Hugging Face has raised $40,000 million in Series B funding. Addition is the leader in the round. The company has been working on an open-source library to support natural language processing (NLP) technologies. You can access the Transformers Library on GitHub. It has 4,22,000 forks as well as 10,000 stars.
Investors who are already in the market—Lux Capital, A. Capital, and Betaworks—were also part of today’s round of financing. The other investors included Dev Ittycheria, Olivier Pomel, Alex Wang, Aghi Marietti, Florian Douetteau, Richard Socher, Paul St. John, Kevin Durant, and Rich Kleiman.
With Transformers, it is possible to leverage well-known NLP models like BERT, GPT, XLNet T5, or DistilBERT, and apply these models to alter text in any way. In particular, it could sort text, extract information, and automatically answer questions to summarise text, generate text, etc.
There are a variety of use cases for NLP. One of the most popular is the chatbot to help with support. For example, the rival lender Monzo uses Hugging Face within the shadows to address questions from customers. In total, more than 5,000 businesses use Hugging Face in some way, including Microsoft using Bing, its own search engine. Bing.
For commercial models and business, this company has recently announced the possibility of getting prioritised support, managing private models, and offering the Inference API. The clients comprise Bloomberg as well as Typeform.
With the latest round of funding, it is expected to increase its workforce by three times within New York and Paris. There are also remote positions. It is interesting to note that the company also revealed certain details regarding their bank account.
Hugging Face has been cash flow positive in February and January 2021. The company was able to raise $15 million about one year ago. 90 percent of that prior round remains in their bank accounts. Its valuation also went up fivefold. This shouldn’t be an unexpected result since you can bargain for better terms if you don’t really need to raise funds.
It seems like Hugging Face has a good track record since it hosts a lively network that includes NLP developers. It is possible to browse the models as well as datasets, use their capabilities, and even contribute to them for the community, as Hugging Face is fast becoming the mainstay of NLP enthusiasts.
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