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