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Zero-shot text classification and evaluation for Topic Analysis

Zero shot text classification and evaluation for Topic Analysis

This workflow shows how to use Symanto Brain for zero shot text classification for the task of topic classification.

Zero shot classification on the Spanish HeadQA topics identification dataset (Vilares and Gómez-Rodríguez,2019)This workflow shows how to use the Symanto Brain for zero shot text classification, in this case for the task of topics classification.ReferencesDavid Vilares and Carlos Gómez-Rodríguez. 2019. HEAD-QA: A healthcare dataset for complex reasoning. In Proceedings of the Annual Meeting of the Association forComputational Linguistics, pages 960–966. Dataset freely available under the MIT license. Load and preprocess dataset Dataset statistics Shows the statistical properties of thedataset. Zero-shot classification with Symanto BrainPlease note that the zero-shot task parameters (labels, language, etc.)and the authentication key must be introduced inside the Zero-shotcomponent. Specifically at the Authentication and Zero-shotcomponents, respectively. Evaluate the modelCompute a confusion matrix betweenreal and predicted class values andcalculate the related metrics. Node 9sb10kNode 513Node 514Node 518 DuplicateRow Filter CSV Reader Dataset statisticscomponent Scorer Column Appender Zero-shot component ROC Curve Zero shot classification on the Spanish HeadQA topics identification dataset (Vilares and Gómez-Rodríguez,2019)This workflow shows how to use the Symanto Brain for zero shot text classification, in this case for the task of topics classification.ReferencesDavid Vilares and Carlos Gómez-Rodríguez. 2019. HEAD-QA: A healthcare dataset for complex reasoning. In Proceedings of the Annual Meeting of the Association forComputational Linguistics, pages 960–966. Dataset freely available under the MIT license. Load and preprocess dataset Dataset statistics Shows the statistical properties of thedataset. Zero-shot classification with Symanto BrainPlease note that the zero-shot task parameters (labels, language, etc.)and the authentication key must be introduced inside the Zero-shotcomponent. Specifically at the Authentication and Zero-shotcomponents, respectively. Evaluate the modelCompute a confusion matrix betweenreal and predicted class values andcalculate the related metrics. Node 9sb10kNode 513Node 514Node 518 DuplicateRow Filter CSV Reader Dataset statisticscomponent Scorer Column Appender Zero-shot component ROC Curve

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: mchinea
Created at: 2023-02-17
On NodePit since: 2023-04-27
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: Text classificationNLPZeroshotNLPDeep learningMachine learningTopic classificationSymanto

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