Building Transformer-Based Natural Language Processing Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10249843" target="_blank" >RIV/61989100:27740/22:10249843 - isvavai.cz</a>

Výsledek na webu

<a href="https://events.it4i.cz/event/129/" target="_blank" >https://events.it4i.cz/event/129/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Building Transformer-Based Natural Language Processing Applications

Popis výsledku v původním jazyce

Applications for natural language processing (NLP) have exploded in the pastdecade. With the proliferation AI assistants and organizations infusing their businesses with more interactive human-machine experiences, understanding how NLP techniques can be used to manipulate analyze, and generate text-based data is essential. Modern techniques can capture the nuance, context and sophistication of language just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within chatbots, AI voice agents, and more. Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. In this workshop, participants learned how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. They have also learned how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

Název v anglickém jazyce

Building Transformer-Based Natural Language Processing Applications

Popis výsledku anglicky

Applications for natural language processing (NLP) have exploded in the pastdecade. With the proliferation AI assistants and organizations infusing their businesses with more interactive human-machine experiences, understanding how NLP techniques can be used to manipulate analyze, and generate text-based data is essential. Modern techniques can capture the nuance, context and sophistication of language just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within chatbots, AI voice agents, and more. Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. In this workshop, participants learned how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. They have also learned how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů