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> (<a href="https://github.com/e0xextazy/nlp_huawei_new2_task" rel="noopener noreferrer" target="_blank">github repo</a> with baselines). Each of these folders contains a file&nbsp;<code>requirements.txt</code>&nbsp;that will help you with the installation of the dependencies.

&nbsp;

You should beat the leaderboard baselines:&nbsp;

<ul>
<li>The easy baseline has quality 0.65. If you beat it you get 12 points.</li>
<li>The hard baseline is 0.68. If you beat this baseline you get 25 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>

Your task is to classify the store's review rating into 5 classes. The metric is F1-score.

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

Predictions

Дмитрий Ивашников

Aleksandr Sokolov

Anastasiia M

Floria Viktor

Bahram Azizi

Ilia Karkin

Иван Сокарев

Oleg Shindelov

Artem Tsukanov

Иван Козлов

i17fcf0665ad9

Fedor Dobryanski

Kirill Zaika

Vladislav Poletaev

Ilia Kuptsov

r33731298ce12

Segey P

Alexander Demidovskij

Alexey Akhundov

Anastasia

Victoria Frolova

Александр Скороходов

Efim Verzakov

Hard Baseline

Pavel A

z93bfc5a311b0

Andrey K

Aleksei Diomin

Putilova Elena

Яна Ульяненко

Viktoriia Azanova

Alina Galiullina

Михаил Кедров

Nastya Larionova

Elizaveta Makhotina

Liubov Orlova

Ilnaz Salimov

Данил Кизеев

Andrey Germanov

Любовь Фонова

Andrey Ovsyannikov

Anatolii Karasev

Alexander Potekhin

Aleksa Bolgar

Igor Salnikov

Mariia Rudomiotova

Diana Sokolova

Osip Podderegin

Egor Pomozov

NovoselovaNatalia

F1 Score

#	Teams (102)	F1 Score	Last	Entries
1	Дмитрий Ивашников	0.6987501624	10m	39
2	Aleksandr Sokolov	0.6963057993	10m	9
3	Anastasiia M	0.685866335	10m	22
4	Floria Viktor	0.6856515113	10m	32
5	Bahram Azizi	0.6856349565	10m	118
6	Ilia Karkin	0.685547073	10m	20
7	Иван Сокарев	0.6847188045	10m	6
8	Oleg Shindelov	0.6841455044	10m	86
9	Artem Tsukanov	0.684010564	10m	19
10	Иван Козлов	0.6839734223	10m	12
11	i17fcf0665ad9	0.68345269	10m	9
12	Fedor Dobryanski	0.683290556	10m	30
13	Kirill Zaika	0.6830029011	10m	2
14	Vladislav Poletaev	0.6827090172	10m	6
15	Ilia Kuptsov	0.6819506654	10m	10
16	r33731298ce12	0.6816048812	10m	7
17	Segey P	0.6812628345	10m	30
18	Alexander Demidovskij	0.6812274868	10m	18
19	Alexey Akhundov	0.6806456982	10m	13
20	Anastasia	0.6806349482	10m	13
21	Victoria Frolova	0.6804114301	10m	65
22	Александр Скороходов	0.6802109442	10m	65
23	Efim Verzakov	0.6800957152	10m	14
Hard Baseline 0,68		—
24	Pavel A	0.6797908402	10m	28
25	z93bfc5a311b0	0.6784394263	10m	1
26	Andrey K	0.6782975932	10m	1
27	Aleksei Diomin	0.6773412267	10m	9
28	Putilova Elena	0.6760867679	10m	45
29	Яна Ульяненко	0.6733504581	10m	42
30	Viktoriia Azanova	0.6731557915	10m	9
31	Alina Galiullina	0.6718552484	10m	3
32	Михаил Кедров	0.6718436278	10m	10
33	Nastya Larionova	0.6717861581	10m	3
34	Elizaveta Makhotina	0.6716172077	10m	10
35	Liubov Orlova	0.6702621867	10m	1
36	Ilnaz Salimov	0.6700595742	10m	70
37	Данил Кизеев	0.6696946211	10m	2
38	Andrey Germanov	0.6681960666	11m	16
39	Любовь Фонова	0.6678608575	10m	4
40	Andrey Ovsyannikov	0.6666516216	10m	14
41	Anatolii Karasev	0.6659682452	10m	69
42	Alexander Potekhin	0.6655539165	10m	6
43	Aleksa Bolgar	0.6648702324	10m	44
44	Igor Salnikov	0.6647831431	10m	9
45	Mariia Rudomiotova	0.6623748551	10m	7
46	Diana Sokolova	0.6622388043	10m	18
47	Osip Podderegin	0.6603413996	10m	2
48	Egor Pomozov	0.6600245015	10m	2
49	NovoselovaNatalia	0.6596157006	10m	9