10-bert-tpu Fine-tuning with BERT¶ In this workshop, we’ll learn how to use a pre-trained BERT model for a sentiment analysis task. We’ll be using the pytorch framework, and huggingface’s transformers library, which provides a suite of transformer models with a consistent interface. Note: You may find certain parts of the code difficult to follow. This […]
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10-bert Fine-tuning with BERT¶ In this workshop, we’ll learn how to use a pre-trained BERT model for a sentiment analysis task. We’ll be using the pytorch framework, and huggingface’s transformers library, which provides a suite of transformer models with a consistent interface. Note: You may find certain parts of the code difficult to follow. This
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Greg Aloupis Greg Aloupis Greg Aloupis Greg Aloupis Greg Aloupis Greg Aloupis (R. Prim 1957, but also V. Jarnik 1930) Greg Aloupis Greg Aloupis For a detailed example of Prim’s algorithm on this graph, please see full version of class notes. Summary follows. Greg Aloupis
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2b_Informed_Search.dvi COMP9414 Informed Search 1 Informed (Heuristic) Search � Uninformed methods of search are capable of systematically exploring the state space in finding a goal state � However, uninformed search methods are very inefficient � With the aid of problem-specific knowledge, informed methods of search are more efficient � All implemented using a priority queue
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l20-topic-model-v2 COPYRIGHT 2021, THE UNIVERSITY OF MELBOURNE 1 COMP90042 Natural Language Processing Lecture 20 Semester 1 2021 Week 10 Jey Han Lau Topic Modelling COMP90042 L20 2 Making Sense of Text • English Wikipedia: 6M articles • Twitter: 500M tweets per day • New York Times: 15M articles • arXiv: 1M articles • What can
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tutorial2.dvi COMP9414: Artificial Intelligence Tutorial 2: Search 1. This exercise concerns the route-finding problem using the Romania map from Russell & Norvig (Artificial Intelligence: A Modern Approach) as an example. Bucharest Giurgiu Urziceni Hirsova Eforie Neamt Oradea Zerind Arad Timisoara Lugoj Mehadia Dobreta Craiova Sibiu Fagaras Pitesti Rimnicu Vilcea Vaslui Iasi Straight−line distance to Bucharest
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l12-discourse-v3 COPYRIGHT 2021, THE UNIVERSITY OF MELBOURNE 1 COMP90042 Natural Language Processing Lecture 12 Semester 1 2021 Week 6 Jey Han Lau Discourse COMP90042 L12 2 Discourse • Most tasks/models we learned operate at word or sentence level: ‣ POS tagging ‣ Language models ‣ Lexical/distributional semantics • But NLP often deals with documents •
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Natural Language Processing Jacob Eisenstein October 15, 2018 Contents Contents 1 Preface i Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i How to use
4a_Knowledge_Representation.dvi COMP9414 Knowledge Representation 1 This Lecture � Knowledge Representation and Logic � Logical Arguments � Propositional Logic ◮ Syntax ◮ Semantics � Validity, Equivalence, Satisfiability, Entailment � Inference by Natural Deduction UNSW ©W. Wobcke et al. 2019–2021 COMP9414: Artificial Intelligence Lecture 4a: Knowledge Representation Wayne Wobcke e-mail:w. .au UNSW ©W. Wobcke et al. 2019–2021
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1a_Foundations.dvi COMP9414 Foundations 1 About Me • Logic and Natural Language Processing (1985–1989) • Logic and Knowledge Representation (1989–1995) • Intelligent Agent Theory (1996–2007) • Personal Assistant Applications • Intelligent Desktop Assistant (1998–2000) • Smart Personal Assistant, like Siri (2002–2006) • Clinical Handover Assistant (2003–2007) • Agent-Based Modelling (2008–2013) • Recommender Systems (2008–2014) • Data
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