This paper describes our open source character-based language model chars2vec. This model was developed with Keras library (TensorFlow backend) and now is available for Python 2.7 and 3.0+.

Introduction

Creating and using word embeddings is the mainstream approach for handling most of the NLP tasks. Each word is matched with a numeric vector which is then used in some way if the word appears in text. Some simple models use one-hot word embeddings or initialise words with random vectors or with integer numbers. …


iki daily feed screenshot

This post adresses the general problem of constructing a deep learning based recommender system. The particular architecture discribed in the paper is the one powering the new smart feed of the iki service, pushing your skills on daily basis — to check its performance, please try product beta.

If you feel familiar with the general idea of recommender systems, mainstream approaches and would like to go straight to the details of our solution, please skip first two sections of the paper.

Introduction

Recommender systems have changed the way we interact with lots of services. Instead of providing static data they bring…


We want to present our brand new open source library RedBlackPy. Now it is available for Python 3.6+ on MacOS and Linux (Windows in near future).
It is distributed under Apache License 2.0. You can easily install it via pip:

>>> pip install redblackpy

Article structure:

Highlights

RedBlackPy is a Python library with data structures based on red-black trees. In some sense, it can be considered as an additional library for pandas. Pandas containers are aimed at efficient work with…


This is the first one of the series of technical posts related to our work on iki project, covering some applied cases of Machine Learning and Deep Learning techniques usage for solving various Natural Language Processing and Understanding problems.

In this post we shall tackle the problem of extracting some particular information form an unstructured text. We needed to extract our users’ skills from their Curriculam Vitaes (CVs) even if they are written in an arbitrary way such as “was deploying quantitative trading algorithms on production server”.

This post has a demo page, check our model’s performance on your CV.

Linguistic models

Intuition Engineering

Research team of https://intuition.engineering company: Ivan Ilin, PhD; Vladimir Chikin; Kirill Solodskih

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