--- license: mit library_name: sklearn tags: - sklearn - skops - text-classification --- # 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. ## Training Procedure ### Hyperparameters 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 | | | 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 |

### Model Plot 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.

## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| # How to Get Started with the Model Use the code below to get started with the model.

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

# Model Card Authors This model card is written by following authors: merve # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` bibtex @inproceedings{...,year={2020}} ```