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