Model description

This is a multinomial naive Bayes model trained on 20 new groups dataset. Count vectorizer and TFIDF vectorizer are used on top of the model.

Intended uses & limitations

This model is not ready to be used in production.

The model is trained with below hyperparameters.

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Hyperparameter	Value
memory
steps	[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())]
verbose	False
vect	CountVectorizer()
tfidf	TfidfTransformer()
clf	MultinomialNB()
vect__analyzer	word
vect__binary	False
vect__decode_error	strict
vect__dtype	<class 'numpy.int64'>
vect__encoding	utf-8
vect__input	content
vect__lowercase	True
vect__max_df	1.0
vect__max_features
vect__min_df	1
vect__ngram_range	(1, 1)
vect__preprocessor
vect__stop_words
vect__strip_accents
vect__token_pattern	(?u)\b\w\w+\b
vect__tokenizer
vect__vocabulary
tfidf__norm	l2
tfidf__smooth_idf	True
tfidf__sublinear_tf	False
tfidf__use_idf	True
clf__alpha	1.0
clf__class_prior
clf__fit_prior	True

The model plot is below.

Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])

Please rerun this cell to show the HTML repr or trust the notebook.

You can find the details about evaluation process and the evaluation results.

Metric	Value

Use the code below to get started with the model.

Click to expand

import pickle
with open(pkl_filename, 'rb') as file:
    clf = pickle.load(file)

This model card is written by following authors:

merve

You can contact the model card authors through following channels: [More Information Needed]

Below you can find information related to citation.

BibTeX:

bibtex
@inproceedings{...,year={2020}}