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

Dmitrii Osin

Konstantin K

Boris Tolstokulakov

Ivan Baklanov

Дмитрий Беркутов

Jegor Vorsin

Егор Шульгин

Igor Romaykin

Vladimir Z

Александр Хамицаев

абобус

g7acb339dcaed

Sergei

Anna Yurishcheva

Иван

Алексей Чернышев

Anastasiia Gapeeva

Mikhail Borisochkin

Rizvan Iskaliev

le3ce84e86cfa

Анна Смекалова

Вадим Коробковский

Vladislav Shkarovskii

p097c88304c63

Алексей Смирнов

София Баранецкая

Andrei Chupeev

Анна Безенкова

Dmitriy Gorbunov

Shura V

Amir Diyanov

Anastasia Nikitina

Лев Кобелев

Максим Горбаченко

Максим Коновалов

o99cbf929f5f1

SergBenz

Dmitrii Shcheglov

Gulko Matvey

Aleksandra Dronova

Виктория Пузанова

Grigory D

David M

Daria

Artem Kamentsev

Алексей Шевченко

Kate Zinyakova

Михаил Вотинов

r635a69a7c2b4

F1 Score

#	Teams (118)	F1 Score	Last	Entries
1	Dmitrii Osin	0.697751274	15d	10
2	Konstantin K	0.6962318177	14d	53
3	Boris Tolstokulakov	0.6947010847	14d	13
4	Ivan Baklanov	0.6931168122	29d	3
5	Дмитрий Беркутов	0.6913761427	14d	9
6	Jegor Vorsin	0.6903181198	15d	7
7	Егор Шульгин	0.6893777676	14d	3
8	Igor Romaykin	0.6885775545	16d	11
9	Vladimir Z	0.6882216517	24d	24
10	Александр Хамицаев	0.6877170438	15d	8
11	абобус	0.6871688621	17d	12
12	g7acb339dcaed	0.687083345	17d	3
13	Sergei	0.6867366282	18d	25
14	Anna Yurishcheva	0.6863851227	15d	28
15	Иван	0.6846320197	18d	21
16	Алексей Чернышев	0.6843505884	20d	19
17	Anastasiia Gapeeva	0.6840877753	14d	5
18	Mikhail Borisochkin	0.6834940562	5w	1
19	Rizvan Iskaliev	0.6830259377	17d	3
20	le3ce84e86cfa	0.6826875495	29d	18
21	Анна Смекалова	0.6821167773	14d	6
22	Вадим Коробковский	0.6819812412	21d	15
23	Vladislav Shkarovskii	0.6819812412	5w	1
24	p097c88304c63	0.6819500788	15d	8
25	Алексей Смирнов	0.6818102936	14d	9
26	София Баранецкая	0.6817261735	14d	5
27	Andrei Chupeev	0.681083295	15d	10
28	Анна Безенкова	0.6808773419	14d	13
29	Dmitriy Gorbunov	0.6808428922	16d	13
30	Shura V	0.6806612908	21d	1
31	Amir Diyanov	0.6800171715	20d	7
32	Anastasia Nikitina	0.6789861772	14d	19
33	Лев Кобелев	0.6773584295	15d	10
34	Максим Горбаченко	0.6769297665	17d	4
35	Максим Коновалов	0.6745360143	14d	16
36	o99cbf929f5f1	0.674396219	16d	10
37	T	0.6735661701	18d	3
38	SergBenz	0.6726490668	14d	1
39	Dmitrii Shcheglov	0.6712202187	14d	6
40	Gulko Matvey	0.6704872224	14d	4
41	Aleksandra Dronova	0.6698535366	15d	8
42	Виктория Пузанова	0.6686307379	14d	10
43	Grigory D	0.668368337	14d	12
44	David M	0.668368337	14d	10
45	Daria	0.6681716659	15d	2
46	Artem Kamentsev	0.6680697054	16d	2
47	Алексей Шевченко	0.6675886039	20d	2
48	Kate Zinyakova	0.666928485	14d	14
49	Михаил Вотинов	0.6666615881	17d	11
50	r635a69a7c2b4	0.6651759893	27d	14