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.

<details> <summary> Click to expand </summary>

</details>

Model Plot

The model plot is below.

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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" type="checkbox" ><label for="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" type="checkbox" ><label for="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69b80eb1-41d4-421a-9875-a9e95faa6d45" type="checkbox" ><label for="69b80eb1-41d4-421a-9875-a9e95faa6d45" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfTransformer</label><div class="sk-toggleable__content"><pre>TfidfTransformer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" type="checkbox" ><label for="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>

## Evaluation Results

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

How to Get Started with the Model

Use the code below to get started with the model.

<details> <summary> Click to expand </summary>

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

</details>

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}}