We present you 4 baseline solutions based on logistic regression, catboost, LSTM and Transformers. 
You can find them in their respective folders:&nbsp;<code>./baseline_tfidf_logreg</code>,&nbsp;<code>./&lt;catboost_baseline&gt;</code>,&nbsp;<code>./baseline_rnn</code>&nbsp;and&nbsp;<code>./&lt;transformer_baseline&gt;</code> in <a href="https://github.com/e0xextazy/nlp_huawei_new2_task" rel="noopener noreferrer" target="_blank">github repo</a>. Each of these folders contains a file&nbsp;<code>requirements.txt</code>&nbsp;that will help you with the installation of the dependencies.

Please be aware that you are not required to use them. The baselines provided only for kickstart.

&nbsp;

You should beat the leaderboard baselines:&nbsp;

<ul>
	<li>The easy baseline has quality 0.65. If you beat it you get 10 points.</li>
	<li>The hard baseline is 0.68. If you beat this baseline you get 10 point for the task. Good luck!</li>
	<li>If you will be the first in your group, you&#39;ll get 3 bonus points.</li>
</ul>

sample_submission.csv

test.csv

train.csv

Your task is to classify the store's review rating into 5 classes. The metric is F1-score.

Public

Agreement

Регистрируясь на курс, я даю <a href="https://storage.yandexcloud.net/ds-ods/files/content/2026/02/09/7ca54807ba64/2025_09_08_%D0%A1%D0%BE%D0%B3%D0%BB%D0%B0%D1%81%D0%B8%D0%B5_%D0%BC%D0%B5%D1%80%D0%BE%D0%BF%D1%80%D0%B8%D1%8F%D1%82%D0%B8%D1%8F_%D0%B8_%D0%BA%D1%83%D1%80%D1%81%D1%8B.pdf">согласие</a> на обработку персональных данных.

first_name

First name

last_name

Last name

email

Email

telegram

Telegram

study_place

Study place

study_specialty

Study specialty

work_place

Work place

work_position

Work position

expectations

Expectations

interests

Interests

preferred_language

Preferred language

about

About

where_from_course

Откуда узнали о курсе?

nlp_student

Если в вашей учебной программе в этом семестре есть курс NLP, выберите свой вуз:

Registration application for Course Season Spring 2026

Practical Assignment 2: Text multiclass classification: store's review rating

Predictions

Final Projects. NLP-course. Spring 2026

Хакатон

Final Projects

Course block deadline expired on 30.05.2026 20:59

Block deadline expired

Теория

1. Introduction to Natural Language Processing

Квест

Test 1

Course block deadline expired on 24.02.2026 20:59

Materials 1

2. Machine Learning Basics and Text Classification

Test 2

Course block deadline expired on 03.03.2026 20:59

Materials 2

3. Word Embeddings

Test 3

Course block deadline expired on 10.03.2026 20:59

Materials 3

Practice word2vec Lite

Course block deadline expired on 17.03.2026 20:59

Practice Word2Vec Hard

4. Convolutional Neural Networks

Test 4

Materials 4

5. Recurrent Neural Networks and Sequence Tagging

Test 5

Course block deadline expired on 24.03.2026 20:59

Materials 5

6. Machine Translation

Test 6

Course block deadline expired on 31.03.2026 20:59

Materials 6

Соревнование

Practice 2

Course block deadline expired on 07.04.2026 20:59

7. Transformers

Test 7

Materials 7

8. Large Language Models

Test 8

Course block deadline expired on 14.04.2026 20:59

Materials 8

Practical Assignment 3: Agents

You need to develop a solution to answer questions about the composition of dishes (for example, calories and macronutrients or by recipes), using large language models (LLMs) with support for tool calling. Participants are required to use an open LLM from Hugging Face together with external APIs (a Recipe API and a Calories API) to accurately determine the nutritional value per serving for given dishes.

Practice 3

Course block deadline expired on 28.04.2026 20:59

D. Conversational AI

Test D

Materials D

E. LLM Compression

Materials E

F. Reinforcement Learning for NLP

Materials F

A. Hidden Markov Models and Tagging

Test A

Course block deadline expired on 19.05.2026 20:59

Materials A

B. Topic Modeling

Test B

Course block deadline expired on 26.05.2026 20:59

Materials B

C. Programming Language Processing

Materials C

Lecturers: 
Valentin Malykh (<a href="http://val.maly.hk/" rel="noopener noreferrer" target="_blank">personal page</a>)

Logistics: 
Lectures at 18.50&nbsp;MSK at Tuesdays online. First lecture will be at 10th of February.&nbsp;

Link to join lectures: <a href="https://my.mts-link.ru/j/MTC/nlp2026spring" rel="noopener noreferrer" target="_blank">https://my.mts-link.ru/j/MTC/nlp2026spring</a>&nbsp;

Seminars will be separate for universities and for ODS. Look info regarding schedule in the chat.

Official support chat: <a href="https://mm.ods.ai/ods/channels/nlp_course" rel="noopener noreferrer" target="_blank">https://mm.ods.ai/ods/channels/nlp_course</a>&nbsp;

Syllabus:

<ul>
	<li>Introduction to Natural Language Processing</li>
	<li>Machine Learning Basics and Text Classification</li>
	<li>Word Embeddings
	<ul>
		<li>1st Assignment</li>
	</ul>
	</li>
	<li>Convolutional Neural Networks
	<ul>
		<li>2nd Assignment</li>
	</ul>
	</li>
	<li>Recurrent Neural Networks</li>
	<li>Statistical Machine Translation</li>
	<li>Transformers</li>
	<li>LLMs</li>
	<li>Project Consultation
	<ul>
		<li>3rd Assignment</li>
	</ul>
	</li>
</ul>

&nbsp;

Prerequisites:

<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>

Assignments:

&nbsp; There will be two assignments on the course.

Projects: 
The participants will be suggested to work on a project during the course. The successful project development is crucial to pass the course. 
The projects from previous runs are available<a href="https://ods.ai/projects" rel="noopener noreferrer" target="_blank"> here</a>.

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 (172)	F1 Score	Last	Entries
1	Alexander Dudin	0.7003724532	5m	5
2	Виктор Негробов	0.6976247384	5m	10
3	Lidiya S	0.6966371937	6m	43
4	Evgeny Bratkovsky	0.6955607637	6m	1
5	Константин Румовский	0.6942109543	5m	7
6	Aleksei Beglov	0.6941251226	5m	2
7	Vladislav Avdeev	0.6931231811	5m	13
8	j41ddcf54520d	0.6930570633	5m	24
9	Nikolay Glukhov	0.6929986051	6m	6
10	Владимир Скворцов	0.6925819693	5m	5
11	wb5d6582f2dca	0.6924949927	6m	5
12	Ulyana Klyuchnikova	0.6919632746	5m	4
13	i30d790ceffb6	0.6919581431	6m	21
14	z19ccecbb1330	0.6913722761	5m	4
15	d1e933a9e118a	0.6913662522	5m	1
16	Maxim Mikhalevich	0.6909235442	5m	14
17	Dima Garanin	0.6908532471	5m	7
18	Matvey Kurchenko	0.6900654467	5m	12
19	Alex Zavrin	0.6897961222	5m	4
20	Evgenii Pakhaliuk	0.6889753658	5m	7
21	je76a57537fa3	0.688931646	6m	7
22	u47177d61cde3	0.6887740542	6m	3
23	y3f93f3e8c6bf	0.6884396164	6m	1
24	Ruslan Akhmetvaleev	0.6884365532	6m	1
25	r820d13c2eeb5	0.6881736829	6m	32
26	Anastasia Chernaya	0.6873141713	6m	1
27	x56e587c17916	0.687235898	5m	10
28	l9254cb101c2c	0.68703215	5m	4
29	Георгий Фирсанов	0.68703215	5m	10
30	Ivan Papai	0.6869186656	6m	17
31	Радмир Исламов	0.6868941604	5m	1
32	Abdurahim Ganiev	0.686702525	5m	46
33	Sg Iu	0.6866834793	5m	4
34	tdbeef5ead93a	0.6864694835	5m	1
35	bb3790b2b23bb	0.6863515589	5m	4
36	Тимур Еникеев	0.68632106	5m	1
37	Danil Kornovan	0.6857993191	5m	51
38	Dorush hh	0.6857683289	5m	10
39	c52b40dba634d	0.6854066065	5m	4
40	w57db610d794a	0.68504856	5m	22
41	Nick Linker	0.685012402	5m	2
42	Mark Smirnov	0.684919962	5m	13
43	e0e49d25c1c6d	0.684919962	5m	6
44	Никита Деулин	0.6846111444	5m	8
45	Fedor	0.6843267116	6m	46
46	m781360ec67c2	0.6843203886	5m	2
47	Daniil Chindin	0.6842914212	5m	22
48	Andrey Ivanov	0.6842219759	5m	2
49	Daria Shushkova	0.6840363213	6m	1
50	Semyon Korobko	0.6837235795	5m	8