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

Теория

1. Introduction to Natural Language Processing

Квест

Test 1

Course block deadline expired on 24.02.2026 20:59

Block deadline expired

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

Materials B

C. Programming Language Processing

Course block will be opened on 25.05.2026 21:00

Block is not open yet

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