Natural Language Process (NLP) is a subfield of phonetics, software engineering, and AI concerned about the connections between PCs and human language. The objective is to make a PC to do "getting" the items in records, including the logical subtleties of the language inside them. The NLP can then precisely extricate data and experiences contained in the archives as well as sort and coordinate the actual reports. Take, for instance, Megatron 530B, which was made and delivered by Microsoft and Nvidia together. Microsoft and Nvidia say that they saw somewhere in the range of 113 and 126 teraflops each second for every GPU while preparing Megatron 530B, which would put the preparation cost in the large numbers of dollars. Induction and really running the prepared model – is another test.

Let the OSS Enterprise newsletter guide your open source journey! Large language models capable of writing poems, summaries, and computer code are driving the demand for "natural language processing (NLP) as a service." As these models become more capable -- and accessible, relatively speaking -- appetite in the enterprise for them is growing. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their NLP budgets grew by at least 10% compared to 2020, while a third -- 33% -- said that their spending climbed by more than 30%. Well-resourced providers like OpenAI, Cohere, and AI21 Labs are reaping the benefits.

OpenAI has become an extremely well-known AI company after the deserved popularity of GPT-3, their celebrity AI model. GPT-3 has amazing skills, it can compose poetry, write essays, or code, but none of that could've been possible without the help of Microsoft's money and computing power. GPT-3 is arguably the most advanced language model out there (at least among those that are publicly available). As such, it'd be reasonable to make it accessible for research purposes at universities and non-profit institutes. Instead, OpenAI decided they'd limit its access to a few privileged through a private API.

FastAPI is a new Python API framework that is more and more used in production today. We are using FastAPI under the hood behind NLP Cloud. NLP Cloud is an API based on spaCy and HuggingFace transformers in order to propose Named Entity Recognition (NER), sentiment analysis, text classification, summarization, and much more. FastAPI helped us quickly build a fast and robust machine learning API serving NLP models. Let me tell you why we made such a choice, and show you how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER).

The NLP Cloud API uses spaCy under the hood for NER. The spaCy pre-trained models can natively recognize entities like name, company, country... If you want to extract non-native entities like job titles, you will need to train your own model using annotation and upload it from your NLP Cloud dashboard in order to use it in production. All the large spaCy pre-trained models are supported by default by NLP Cloud. For more details, see our documentation about NER.