Model description

This is a pipeline for sentiment analysis trained on the Stanford Twitter dataset.TF-IDF vectorizer is used for vectorization.

Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
steps	[('vectorizer', TfidfVectorizer(encoding='latin-1', min_df=5, ngram_range=(1, 2), sublinear_tf=True)), ('mnb', MultinomialNB())]
verbose	False
vectorizer	TfidfVectorizer(encoding='latin-1', min_df=5, ngram_range=(1, 2), sublinear_tf=True)
mnb	MultinomialNB()
vectorizer__analyzer	word
vectorizer__binary	False
vectorizer__decode_error	strict
vectorizer__dtype	<class 'numpy.float64'>
vectorizer__encoding	latin-1
vectorizer__input	content
vectorizer__lowercase	True
vectorizer__max_df	1.0
vectorizer__max_features
vectorizer__min_df	5
vectorizer__ngram_range	(1, 2)
vectorizer__norm	l2
vectorizer__preprocessor
vectorizer__smooth_idf	True
vectorizer__stop_words
vectorizer__strip_accents
vectorizer__sublinear_tf	True
vectorizer__token_pattern	(?u)\b\w\w+\b
vectorizer__tokenizer
vectorizer__use_idf	True
vectorizer__vocabulary
mnb__alpha	1.0
mnb__class_prior
mnb__fit_prior	True
mnb__force_alpha	True

Model Plot

Pipeline(steps=[('vectorizer',TfidfVectorizer(encoding='latin-1', min_df=5,ngram_range=(1, 2), sublinear_tf=True)),('mnb', MultinomialNB())])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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Citation

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BibTeX:

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get_started_code

import joblib model = joblib.load('pipeline_sentiment_analysis.pkl')

model_card_authors

Rodrigo Rodrigues do Carmo

limitations

This pipeline is for studying purposes only.