In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face.
The AI product — Watson Assistant, provided by IBM, enables the companies to respond to customers’ queries in a timely, straightforward, and precise manner, regardless of the application, platform, or channel they use. It creates an application capable of understanding natural languages and can create a human-like conversation with customers in multiple languages. It can further provide customised solutions to the organisations depending on the needs, thereby dynamic in nature. However, the platform does not support third-party integration.
A machine learning-based service, LUIS, i.e. Language Understanding, is provided by Microsoft to modify natural languages and help organisations build IoT devices, apps, and bots. Valuable information like user conversations from various built-in apps, including calendar, music, weather apps, and the dictionary, is typically used to improve LUIS services. Not only can it understand multiple-languages, but it also provides client-friendly customised models. Besides, to make scaling easy, it connects seamlessly to messaging platforms, web environments, and social networks. It supports speech recognition, as well as third-party integration.
Developed by Amazon, Lex provides advanced deep learning modules such as automatic speech recognition (ASR) for translating speech to text and natural language understanding (NLU) for recognising the meaning of the text. It comes with the AWS platform, which can further be used for various purposes, including security, user authentication, storage, monitoring, and mobile app development. Chatbots created using this platform can fulfil customer requests like the latest news updates, live scores, food recipes and weather information.
Dialogflow has been developed by Google with the help of deep-learning technologies to power Google Assistant. The platform uses BERT-based natural language understanding (NLU), a model that can improve the company’s call or chat containment rate. Further, it can build text to speech as well as speech to text models. One of its key features includes multilingual support, making it easy for organisations to engage with their global customer base. One of the best features it can provide users with is unlimited API calls.
A natural language interface, Wit.ai, by Facebook, can convert sentences or speech into structured data. With wit.ai, developers can create apps, bots, smart home appliances, including smart speakers, lighting, and even wearable devices for customers. With more than 200,000 developers supporting 132 languages, this AI technology supports Slack, Alexa, Google Assistant and Facebook Messenger. It’s third-party integration, and diverse language support is its best asset.
One of the new open-source NLP with Python libraries, SpaCy, has pre-trained NLP models to handle large volumes of data at a lightning-fast speed. With components like named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatisation, morphological analysis, entity linking, and more, the product acts handy to derive insights from unstructured data available in large volume. The platform supports more than 64 languages and is equipped with pre-trained transformers like BERT. Moreover, it has in-built support for custom models in PyTorch, TensorFlow, and other frameworks.
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