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Sarcasm_​detection_​with_​BERT_​by_​Redfield

Sarcasm Detected with Machine Learning

In this workflow we are using BERT embeddings to detect sarcasm in texts. Other cases for embeddings are also considered: using PCA for dimensionality reduction, embeddings visualization, using more simple algorithims to train predictive models based on embeddings.

No fine-tuning Models based on fine-tuned BERT embeddings Fine-tuning BERT and getting fine-tune embeddings Models based on principal components of fine-tunedBERT embeddings Class distributionNode 8Get statisticson the texts lengthSplitvectortraining+validationtestNode 322Node 323No fine-tuningApply modeltrainingvalidationfine-tuningApply modelUse this nodeto control the amount of datadepending on your hardware capabilitiesSplitvectortraining+validation+testcontrol70 / 3070 / 30Node 358Node 359Node 360Node 361Node 363Node 364Node 365Node 366Node 371Node 372Node 377Save trained modelRead trained modelNode 382Score modelScore modelCreate fine-tunedembeddingsCreate non-fine-tunedembeddingsBar Chart BERT Model Selector Text assessment Split CollectionColumn Partitioning Random ForestLearner Random ForestPredictor BERT ClassificationLearner BERT Predictor Partitioning BERT ClassificationLearner BERT Predictor Row Sampling Split CollectionColumn Partitioning Partitioning Partitioning Random ForestLearner Random ForestPredictor PCA Compute PCA Apply Random ForestLearner Partitioning Random ForestPredictor K Nearest Neighbor PCA Apply PCA Compute K Nearest Neighbor Model Writer Model Reader K Nearest Neighbor Non-fine-tuned embeddignsmodels analysis Scorer (JavaScript) Scorer (JavaScript) Fine-tuned embeddignsmodels analysis Fine-tuned PC ofembeddigns models analysis Reading data BERT Embedder BERT Embedder No fine-tuning Models based on fine-tuned BERT embeddings Fine-tuning BERT and getting fine-tune embeddings Models based on principal components of fine-tunedBERT embeddings Class distributionNode 8Get statisticson the texts lengthSplitvectortraining+validationtestNode 322Node 323No fine-tuningApply modeltrainingvalidationfine-tuningApply modelUse this nodeto control the amount of datadepending on your hardware capabilitiesSplitvectortraining+validation+testcontrol70 / 3070 / 30Node 358Node 359Node 360Node 361Node 363Node 364Node 365Node 366Node 371Node 372Node 377Save trained modelRead trained modelNode 382Score modelScore modelCreate fine-tunedembeddingsCreate non-fine-tunedembeddingsBar Chart BERT Model Selector Text assessment Split CollectionColumn Partitioning Random ForestLearner Random ForestPredictor BERT ClassificationLearner BERT Predictor Partitioning BERT ClassificationLearner BERT Predictor Row Sampling Split CollectionColumn Partitioning Partitioning Partitioning Random ForestLearner Random ForestPredictor PCA Compute PCA Apply Random ForestLearner Partitioning Random ForestPredictor K Nearest Neighbor PCA Apply PCA Compute K Nearest Neighbor Model Writer Model Reader K Nearest Neighbor Non-fine-tuned embeddignsmodels analysis Scorer (JavaScript) Scorer (JavaScript) Fine-tuned embeddignsmodels analysis Fine-tuned PC ofembeddigns models analysis Reading data BERT Embedder BERT Embedder

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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: artem
Created at: 2021-10-18
On NodePit since: 2021-12-06
Last update: 2026-01-20
Created with KNIME version: v4.6.1
Tags: RedfieldBERTSarcasmEmbeddingsDimensionality reductionNLP

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