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>

test.csv

train.csv

sample_submission.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/2025/09/10/4fe2e1a5db2a/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 Autumn 2025

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

Predictions

Final Projects. NLP-course. Autumn 2025

Хакатон

Final Projects

Теория

1. Introduction to Natural Language Processing

Квест

Test 1

Course block deadline expired on 30.09.2025 20:59

Block deadline expired

Materials 1

2. Machine Learning Basics and Text Classification

Test 2

Course block deadline expired on 07.10.2025 20:59

Materials 2

3. Word Embeddings

Test 3

Course block deadline expired on 14.10.2025 20:59

Materials 3

Practice word2vec Lite

Course block deadline expired on 21.10.2025 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 28.10.2025 20:59

Materials 5

6. Machine Translation

Test 6

Course block deadline expired on 04.11.2025 20:59

Materials 6

Соревнование

Practice 2

Course block deadline expired on 11.11.2025 20:59

7. Transformers

Test 7

Materials 7

8. Large Language Models

Test 8

Course block deadline expired on 25.11.2025 20:59

Materials 8

A. Hidden Markov Models and Tagging

Test A

Materials A

B. Topic Modeling

Test B

Materials B

C. Programming Language Processing

Course block deadline expired on 16.12.2025 15:41

Materials C

D. Conversational AI

Test D

Course block deadline expired on 16.12.2025 20:59

Materials D

E. LLM Compression

Materials E

F. Reinforcement Learning for NLP

Materials F

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 Nutrition API) to accurately determine the nutritional value per serving for given dishes.

Practice 3

Course block deadline expired on 23.12.2025 20:59

Lecturers: 
Valentin Malykh (<a href="http://val.maly.hk/" rel="noopener noreferrer" target="_blank">personal page</a>)

Logistics: 
Lectures at 18:35&nbsp;MSK at Tuesdays online. First lecture will be at 16th of September.&nbsp;

Link to join lectures: <a href="https://my.mts-link.ru/j/MTC/nlp2025autumn" rel="noopener noreferrer" target="_blank">https://my.mts-link.ru/j/MTC/nlp2025autumn</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 (118)	F1 Score	Last	Entries
1	Dmitrii Osin	0.697751274	6w	10
2	Konstantin K	0.6962318177	6w	53
3	Boris Tolstokulakov	0.6947010847	6w	13
4	Ivan Baklanov	0.6931168122	2m	3
5	Дмитрий Беркутов	0.6913761427	6w	9
6	Jegor Vorsin	0.6903181198	6w	7
7	Егор Шульгин	0.6893777676	6w	3
8	Igor Romaykin	0.6885775545	7w	11
9	Vladimir Z	0.6882216517	8w	24
10	Александр Хамицаев	0.6877170438	6w	8
11	абобус	0.6871688621	7w	12
12	g7acb339dcaed	0.687083345	7w	3
13	Sergei	0.6867366282	7w	25
14	Anna Yurishcheva	0.6863851227	6w	28
15	Иван	0.6846320197	7w	21
16	Алексей Чернышев	0.6843505884	7w	19
17	Anastasiia Gapeeva	0.6840877753	6w	5
18	Mikhail Borisochkin	0.6834940562	2m	1
19	Rizvan Iskaliev	0.6830259377	7w	3
20	le3ce84e86cfa	0.6826875495	2m	18
21	Анна Смекалова	0.6821167773	6w	6
22	Вадим Коробковский	0.6819812412	7w	15
23	Vladislav Shkarovskii	0.6819812412	2m	1
24	p097c88304c63	0.6819500788	7w	8
25	Алексей Смирнов	0.6818102936	6w	9
26	София Баранецкая	0.6817261735	6w	5
27	Andrei Chupeev	0.681083295	6w	10
28	Анна Безенкова	0.6808773419	6w	13
29	Dmitriy Gorbunov	0.6808428922	7w	13
30	Shura V	0.6806612908	7w	1
31	Amir Diyanov	0.6800171715	7w	7
32	Anastasia Nikitina	0.6789861772	6w	19
33	Лев Кобелев	0.6773584295	7w	10
34	Максим Горбаченко	0.6769297665	7w	4
35	Максим Коновалов	0.6745360143	6w	16
36	o99cbf929f5f1	0.674396219	7w	10
37	T	0.6735661701	7w	3
38	SergBenz	0.6726490668	6w	1
39	Dmitrii Shcheglov	0.6712202187	6w	6
40	Gulko Matvey	0.6704872224	6w	4
41	Aleksandra Dronova	0.6698535366	7w	8
42	Виктория Пузанова	0.6686307379	6w	10
43	Grigory D	0.668368337	6w	12
44	David M	0.668368337	6w	10
45	Daria	0.6681716659	7w	2
46	Artem Kamentsev	0.6680697054	7w	2
47	Алексей Шевченко	0.6675886039	7w	2
48	Kate Zinyakova	0.666928485	6w	14
49	Михаил Вотинов	0.6666615881	7w	11
50	r635a69a7c2b4	0.6651759893	2m	14