82 lines
2.9 KiB
Plaintext
82 lines
2.9 KiB
Plaintext
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{
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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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"source": [
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"# Informacje ogólne"
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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 prowadzącym\n",
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"\n",
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"prowadzący: mgr inż. Jakub Pokrywka\n",
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"\n",
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"Najlepiej kontaktowąć się ze mną przez MS TEAMS na grupie kanału (ogólne sprawy) lub w prywatnych wiadomościach. Odpisuję co 2-3 dni. Można też umówić się na zdzwonko w godzinach dyżuru (wt 12.00-13.00) lub umówić się 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 chociaż przejrzeć.\n",
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"- Marie-Francine Moens. 2006. Information Extraction: Algorithms and Prospects in a Retrieval Context. Springer. (polecam mniej, jest trochę 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 Graliński, Tomasz Stanisławek, Anna Wróblewska, Dawid Lipiński, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemysław 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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"- Łukasz Garncarek, Rafał Powalski, Tomasz Stanisławek, Bartosz Topolski, Piotr Halama, Filip Graliński. 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 będzie conajmniej 500 punktów.\n",
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"\n",
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"Ocena:\n",
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"\n",
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"- -299 — 2\n",
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"\n",
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"- 300-349 — 3\n",
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"\n",
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"- 350-399 — 3+\n",
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"\n",
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"- 400-449 — 4\n",
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"\n",
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"- 450—499 — 4+\n",
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"\n",
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"- 500- — 5\n",
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"\n"
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]
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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": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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