112 lines
5.3 KiB
Plaintext
112 lines
5.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 0. <i>Informacje na temat przedmiotu</i> [\u0107wiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Informacje og\u00f3lne"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Kontakt z prowadz\u0105cym\n",
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"\n",
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"prowadz\u0105cy: mgr in\u017c. Jakub Pokrywka\n",
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"\n",
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"Najlepiej kontaktow\u0105\u0107 si\u0119 ze mn\u0105 przez MS TEAMS na grupie kana\u0142u (og\u00f3lne sprawy) lub w prywatnych wiadomo\u015bciach. Odpisuj\u0119 co 2-3 dni. Mo\u017cna te\u017c um\u00f3wi\u0107 si\u0119 na zdzwonko w godzinach dy\u017curu (wt 12.00-13.00) lub um\u00f3wi\u0107 si\u0119 w innym terminie.\n",
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"\n",
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"\n",
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"## Literatura\n",
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"Polecana literatura do przedmiotu:\n",
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"\n",
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"\n",
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"- https://www.manning.com/books/relevant-search#toc (darmowa) Polecam chocia\u017c przejrze\u0107.\n",
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"- Marie-Francine Moens. 2006. Information Extraction: Algorithms and Prospects in a Retrieval Context. Springer. (polecam mniej, jest troch\u0119 nieaktualna)\n",
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"- Alex Graves. 2012. Supervised sequence labelling. Studies in Computational Intelligence, vol 385. Springer. Berlin, Heidelberg. \n",
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"\n",
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"- Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Association for Computational Linguistics (NAACL). \n",
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"\n",
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"- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research vol 21, number 140, pages 1-67. \n",
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"\n",
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"- Flip Grali\u0144ski, Tomasz Stanis\u0142awek, Anna Wr\u00f3blewska, Dawid Lipi\u0144ski, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemys\u0142aw Biecek. 2020. Kleister: A novel task for information extraction involving long documents with complex layout. URL https://arxiv.org/abs/2003.02356 \n",
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"\n",
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"- \u0141ukasz Garncarek, Rafa\u0142 Powalski, Tomasz Stanis\u0142awek, Bartosz Topolski, Piotr Halama, Filip Grali\u0144ski. 2020. LAMBERT: Layout-Aware (Language) Modeling using BERT. URL https://arxiv.org/pdf/2002.08087 \n",
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"\n",
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"## Zaliczenie\n",
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"\n",
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"\n",
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"\n",
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"Do zdobycia b\u0119dzie conajmniej 600 punkt\u00f3w.\n",
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"\n",
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"Ocena:\n",
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"\n",
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"- -299 \u2014 2\n",
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"\n",
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"- 300-349 \u2014 3\n",
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"\n",
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"- 350-399 \u2014 3+\n",
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"\n",
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"- 400-449 \u2014 4\n",
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"\n",
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"- 450\u2014499 \u2014 4+\n",
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"\n",
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"- 500- \u2014 5\n",
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"\n",
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"\n",
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"**\u017beby zaliczy\u0107 przedmiot nale\u017cy pojawia\u0107 si\u0119 na laboratoriach. Maksymalna liczba nieobecno\u015bci to 3. Obecno\u015b\u0107 b\u0119d\u0119 sprawdza\u0142 poprzez panel MS TEAMS, czyli b\u0119d\u0119 sprawdza\u0142 czy kto\u015b jest wdzwoniony na \u0107wiczenia. Je\u017celi kogo\u015b nie b\u0119dzie wi\u0119cej ni\u017c 3 razy, to nie b\u0119dzie mia\u0142 zaliczonego przedmiotu** \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"author": "Jakub Pokrywka",
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"email": "kubapok@wmi.amu.edu.pl",
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"lang": "pl",
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"subtitle": "0.Informacje na temat przedmiotu[\u0107wiczenia]",
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"title": "Ekstrakcja informacji",
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"year": "2021"
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |