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BERT_​Sentiment_​Analysis_​with_​BERT_​extension(2)

Workflow

Sentiment Analysis with BERT extension by Redfield

This workflow demonstrates how to do sentiment analysis with BERT extension for Knime by Redfield.

The dataset used here consists of the first 10000 reviews in the IMDB Movie Reviews dataset (http://ai.stanford.edu/~amaas/data/sentiment/) from "Learning Word Vectors for Sentiment Analysis" by Maas et al.

Required Python packages (need to be available in your TensorFlow 2 Python environment):
bert==2.2.0
bert-for-tf2==0.14.4
Keras-Preprocessing==1.1.2
numpy==1.19.1
pandas==0.23.4
pyarrow==0.11.1
tensorboard==2.2.2
tensorboard-plugin-wit==1.7.0
tensorflow==2.2.0
tensorflow-estimator==2.2.0
tensorflow-hub==0.8.0
tokenizers==0.7.0
tqdm==4.48.0
transformers==3.0.2

Deep LearningNLPMachine LearningText ProcessingText ClassificationSentiment AnalysisBERTTensorFlowTensorFlow 2PythonTensorFlow HubRedfield
70 / 30training / testWithout fine tuningWithout fine tuningBERT modelcan be cached on diskMeasure nodeexecution timeWith fine tuningWith fine tuningreplace "<br />" with "" and make texts lower caseCSV Reader (Labs) Partitioning BERT Predictor BERT ClassificationLearner BERT Model Selector Models comparison Timer Info BERT Predictor BERT ClassificationLearner String Manipulation 70 / 30training / testWithout fine tuningWithout fine tuningBERT modelcan be cached on diskMeasure nodeexecution timeWith fine tuningWith fine tuningreplace "<br />" with "" and make texts lower caseCSV Reader (Labs) Partitioning BERT Predictor BERT ClassificationLearner BERT Model Selector Models comparison Timer Info BERT Predictor BERT ClassificationLearner String Manipulation

Download

Get this workflow from the following link: Download

Nodes

BERT_​Sentiment_​Analysis_​with_​BERT_​extension(2) consists of the following 19 nodes(s):

Plugins

BERT_​Sentiment_​Analysis_​with_​BERT_​extension(2) contains nodes provided by the following 7 plugin(s):