<p>Your task is to classify toxic comments into 6&nbsp;classes (from 0 to 5).&nbsp;The metric is&nbsp;<strong>accuracy</strong>, the baselines quality is 0.60 and 0.7. If you beat the first baseline you will get 12 points, if you bet the second baseline you will get 25 points. If you will be the first in your group, you&#39;ll get 3 bonus points.<br />
The test data is a set of strings, one comment per line. You need to return a label for each comment, also one&nbsp;per line.</p>

<p>The baseline solution <a href="https://github.com/huawei-university/nlp-assignment-2">is here</a>.</p>

The task of to classifying toxic comments

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NLP course. Text classification: toxic comments

Predictions

Final Projects. NLP-course

Соревнование

Final Projects

Introduction to Natural Language Processing

Теория

Unit 1: Introduction and Basic Text Processing. Lecture 01

Тестирование

Test 1

Course block deadline expired on 28.09.2021 20:59

Block deadline expired

Machine Learning Basics and Text Classification

Unit 2: NLP Techniques. Lecture 02

Test 2

Course block deadline expired on 05.10.2021 20:59

Unit 2: NLP Techniques. Lecture 03

Test 3

Course block deadline expired on 12.10.2021 20:59

Unit 2: NLP Techniques. Lecture 04

Test 4

Course block deadline expired on 19.10.2021 20:59

Unit 2: NLP Techniques. Lecture 05

Test 5

Course block deadline expired on 26.10.2021 20:59

Practice (beta)

Course block deadline expired on 10.11.2021 20:59

Unit 2: NLP Techniques. Lecture 06

Test 6

Course block deadline expired on 16.11.2021 20:59

Unit 2: NLP Techniques. Lecture 07

Test 7

Unit 3: NLP Applications. Lecture 08

Test 8

Course block deadline expired on 23.11.2021 20:59

Unit 3: NLP Applications. Lecture 09

Test 9

Course block deadline expired on 01.12.2021 20:59

Practice 2

Test 10

Course block deadline expired on 15.12.2021 20:59

Unit 3: NLP Applications. Lecture 10

Unit 3: NLP Applications. Lecture 11

Unit 3: NLP Applications. Lecture 12

<p>Official support channel: <a href="https://opendatascience.slack.com/archives/CUGP21CKU" rel="noopener noreferrer" target="_blank"><strong>#course_nlp_huawei</strong></a> in ODS.ai slack. If you are not registered, please<a href="https://ods.ai/join-community" rel="noopener noreferrer" target="_blank"> join the community</a>.</p>

<h3>Lecturers:</h3>

<p>Valentin Malykh (<a href="http://val.maly.hk/" rel="noopener noreferrer" target="_blank">personal page</a>)</p>

<h3>Logistics:</h3>

<p>The lectures are presented at <strong>10:15 MSK each Tuesday online</strong>&nbsp;and seminars at <strong>18:30 MSK</strong> <strong>each Tuesday online</strong>.<br />
<strong>Course started: 14th of September 2021.</strong><br />
The main lectures end: 16th of November 2021.</p>

<h3>Prerequisites</h3>

<ul>
	<li>Basic Python Programming</li>
	<li>College Calculus, Linear Algebra</li>
	<li>Basic Probability and Statistics</li>
	<li>Foundations of Machine Learning</li>
</ul>

<h3>Assignments</h3>

<p>There will be two assignments on the course.</p>

<h3>Projects</h3>

<p>The participants will be suggested to work on a project during the course. The successful project development is crucial to pass the course.</p>

<p>The projects from previous semester are available <a href="https://github.com/huawei-university/nlp-course-projects" rel="noopener noreferrer" target="_blank">here</a>.</p>

Валентин Малых

Natural Language Processing (NLP) is a domain of research whose objective is to analyze and understand human languages and develop technologies to enable human machine interactions with natural languages. NLP is an interdisciplinary field involving linguistics, computer sciences and artificial intelligence. The goal of this course is to provide students with comprehensive knowledge of NLP. Students will be equiped with the principles and theories of NLP, as well as various NLP technologies, including rule-based, statistical and neural network ones. After this course, students will be able to conduct NLP research and develop state-of-the-art NLP systems.

#	Teams (27)	Accuracy	Last	Entries
1	Denis Pikhenko	0.7874456184	3y	2
2	Vlad Gusev	0.7787445618	3y	15
3	Maksim Kleshchenok	0.7425419515	3y	20
4	Alexander Samokhin	0.7389683033	3y	8
5	Ksenia	0.7371037912	3y	3
6	Denis Grushentsev	0.7361715351	3y	6
7	George Varenikov	0.7290242387	3y	1
8	Semernya Vladislav	0.7263828465	3y	1
9	Alexander Barluka	0.7257613424	3y	1
10	Masyagin	0.7251398384	3y	1
11	Eduard Belov	0.7234307023	3y	6
12	Kseniya Shutova	0.7092914854	3y	4
13	Dmitriy Faiazov	0.7086699814	3y	5
14	Vladislav Dolgopolov	0.7060285892	3y	6
15	Maxim Talimanchuk	0.702610317	3y	1
16	Nikolay Andreychik	0.7022995649	3y	4
17	NorthernRay	0.7002796768	3y	15
baseline 25 points		0.7
18	Ekaterina Timofeeva	0.6853635799	3y	1
19	Valeriia Garashchenko	0.6825668117	3y	5
20	Artem Kopin	0.6699813549	3y	2
21	Dbadeev	0.6662523306	3y	6
22	Dmitrii Sitnikov	0.6379738968	3y	6
23	Polina Vladykina	0.6379738968	3y	8
24	Daria Tikhonova	0.6053449347	3y	1
25	Vera Lobanova	0.6013051585	3y	3
baseline 12 points		0.6
26	Andy Slepukhin	0.4729645743	3y	1
27	Maria Potapova	0.4729645743	3y	2