Machine Learning

Tasks that we solve in our Machine Learning cases:

• Named-entity recognition/classification
• Information extraction
• Topic modeling
• Summarizing
• Paraphrase identification
• Question answering
• Searching engine
• Text classification

About open-source models that we utilize

Nowadays there a lot of powerful open-source Machine Learning libraries and models that are freely available on the market. We don’t hesitate to use them to improve our Machine Learning algorithms and NLU potential. Here are some of them:

• Bidirectional Encoder Representations from Transformers (BERT)
• Multilingual Universal Sentence Encoder for Semantic Retrieval
• Tensorflow and Simple Tensorflow Serving
• FAISS by Facebook
• ElasticSearch
• gRPC for the API

Here you can check the more detailed article in which we described how to build a semantic search pipeline using open-source components and a little bit of coding (based on our project OneBar).

OneBar

In addition to consulting services, we are all working on Onebar. OneBar is a search tool (based on Machine Learning) for teams that helps them boost productivity and promote knowledge sharing. It integrates with Slack and provides a seamless search experience through a convenient interface. Here we try to use and experiment with the most progressive features of Machine Learning in the field of NLP. Read more about OneBar via this link https://onebar.io/.

Machine Learning has a really borderless potential for business, science, human lives, and humanity in general. For instance, we develop Chatbots based on Machine Learning algorithms and the circle of our interests in ML is constantly growing.