<p>We present you 4 baseline solutions based on logistic regression, catboost, LSTM and Transformers.&nbsp;<br />
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).&nbsp;</p>

<p>Each of these folders contains a file&nbsp;<code>requirements.txt</code>&nbsp;that will help you with the installation of the dependencies. To see the score and how many points you get if you can beat him, look at the table below:</p>

<figure class="table">
<table>
<thead>
<tr>
<th>baseline</th>
<th>Accuracy</th>
</tr>
</thead>
<tbody>
<tr>
<td>LogReg</td>
<td>0.43687</td>
</tr>
<tr>
<td>CatBoost</td>
<td>0.23240</td>
</tr>
<tr>
<td>LSTM</td>
<td>0.34413</td>
</tr>
<tr>
<td>Transformer</td>
<td>0.61340</td>
</tr>
</tbody>
</table>
</figure>

<p>You should beat the leaderboard baselines:&nbsp;</p>

<ul>
<li>The easy baseline has quality 0.65. If you beat it you get 12 points.</li>
<li>The hard baseline is 0.75. 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 film descriptions into 6 classes. The metric is Accuracy.

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Registration application for Course Season Spring 2023

Practical Assignment 2: Text multiclass classification: movie's genre

Predictions

Ruslan Sirazhetdinov

alexey-zhavoronkin

Fedor Dobryanski

Nikita Chukhrov

Vladimir_Netrepkin

Dmitrii Poliakov

Denis Tsybrick

Mikhail Shutov

Maksim Maklygin

Vladislav Shimanskii

Alexandr Migunov

Dmitrii Sudakov

Samuil Burovin

Fedor Pavlov

Rinat Mahmutov

Yuriy Kim

Leonid Dzyatko

19d1ffdb45a6

Nikita Girman

ivan tsvetkov

Andrey Popov

Daniil Avtushko

Nikita Venediktov

Rheamer

Vsevolod Vlasov

Vladimir Lazarev

Eugene Krylov

Konstantin Mitrofanov

Kirill Dubovik

Shkut Maxim

Grigorii Fetisov

Vladislav Mostovik

Alexandr Maximenko

Dmitry Kosmachev

KONSTANTIN LIKHOTA

Fomberg Vladislav

Irina Prokofieva

Eugene Astafurov

Dmitry Vedenichev

Orkhan Makhmudov

Konstantin Templin

Sergey Nichiporchik

Mikhail Lazukov

Dmitry Stepakov

Vadim Sorokin

Tatiana Zakharova

Arina Gritsai

Nikita Odinets

Yulian Gilyazev

David Moiseev

Accuracy

#	Teams (139)	Accuracy	Last	Entries
1	Ruslan Sirazhetdinov	1	3y	27
2	alexey-zhavoronkin	1	3y	7
3	Fedor Dobryanski	1	3y	8
4	Nikita Chukhrov	1	3y	5
5	Vladimir_Netrepkin	1	3y	2
6	Dmitrii Poliakov	0.9988826816	3y	3
7	Denis Tsybrick	0.9988826816	3y	15
8	Mikhail Shutov	0.9988826816	3y	5
9	Maksim Maklygin	0.9988826816	3y	4
10	Vladislav Shimanskii	0.9988826816	3y	3
11	Alexandr Migunov	0.9977653631	3y	34
12	Dmitrii Sudakov	0.9977653631	3y	10
13	Samuil Burovin	0.9966480447	3y	9
14	Fedor Pavlov	0.9966480447	3y	1
15	Rinat Mahmutov	0.9921787709	3y	26
16	Yuriy Kim	0.9921787709	3y	8
17	Leonid Dzyatko	0.9921787709	3y	8
18	19d1ffdb45a6	0.9910614525	3y	6
19	Nikita Girman	0.9251396648	3y	6
20	ivan tsvetkov	0.9162011173	3y	1
21	Andrey Popov	0.912849162	3y	11
22	Daniil Avtushko	0.9039106145	3y	6
23	Nikita Venediktov	0.9027932961	3y	4
24	Rheamer	0.8938547486	3y	3
25	Vsevolod Vlasov	0.8927374302	3y	16
26	Vladimir Lazarev	0.8748603352	3y	3
27	Eugene Krylov	0.8737430168	3y	10
28	Konstantin Mitrofanov	0.8715083799	3y	33
29	Kirill Dubovik	0.869273743	3y	23
30	Shkut Maxim	0.8636871508	3y	7
31	Grigorii Fetisov	0.8625698324	3y	9
32	Vladislav Mostovik	0.8581005587	3y	22
33	Alexandr Maximenko	0.851396648	3y	20
34	Dmitry Kosmachev	0.8469273743	3y	14
35	KONSTANTIN LIKHOTA	0.8458100559	3y	4
36	Fomberg Vladislav	0.8446927374	3y	3
37	Irina Prokofieva	0.8402234637	3y	17
38	Eugene Astafurov	0.8379888268	3y	3
39	Dmitry Vedenichev	0.8357541899	3y	4
40	Orkhan Makhmudov	0.8346368715	3y	2
41	Konstantin Templin	0.8245810056	3y	16
42	Sergey Nichiporchik	0.8223463687	3y	7
43	Mikhail Lazukov	0.8145251397	3y	9
44	Dmitry Stepakov	0.8089385475	3y	7
45	Vadim Sorokin	0.8089385475	3y	3
46	Tatiana Zakharova	0.8067039106	3y	6
47	Arina Gritsai	0.8067039106	3y	10
48	Nikita Odinets	0.8044692737	3y	3
49	Yulian Gilyazev	0.7988826816	3y	7
50	David Moiseev	0.7988826816	3y	3