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--- |
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license: mit |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- text-classification |
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--- |
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# Model description |
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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. |
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## Intended uses & limitations |
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This model is not ready to be used in production. |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|---------------------|----------------------------------------------------------------------------------------| |
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| memory | | |
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| steps | [('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())] | |
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| verbose | False | |
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| vect | CountVectorizer() | |
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| tfidf | TfidfTransformer() | |
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| clf | MultinomialNB() | |
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| vect__analyzer | word | |
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| vect__binary | False | |
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| vect__decode_error | strict | |
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| vect__dtype | <class 'numpy.int64'> | |
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| vect__encoding | utf-8 | |
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| vect__input | content | |
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| vect__lowercase | True | |
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| vect__max_df | 1.0 | |
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| vect__max_features | | |
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| vect__min_df | 1 | |
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| vect__ngram_range | (1, 1) | |
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| vect__preprocessor | | |
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| vect__stop_words | | |
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| vect__strip_accents | | |
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| vect__token_pattern | (?u)\b\w\w+\b | |
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| vect__tokenizer | | |
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| vect__vocabulary | | |
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| tfidf__norm | l2 | |
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| tfidf__smooth_idf | True | |
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| tfidf__sublinear_tf | False | |
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| tfidf__use_idf | True | |
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| clf__alpha | 1.0 | |
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| clf__class_prior | | |
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| clf__fit_prior | True | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 {color: black;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 pre{padding: 0;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable {background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-item {z-index: 1;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:only-child::after {width: 0;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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> |
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## Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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| Metric | Value | |
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|----------|---------| |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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import pickle |
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with open(pkl_filename, 'rb') as file: |
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clf = pickle.load(file) |
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``` |
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</details> |
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# Model Card Authors |
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This model card is written by following authors: |
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merve |
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# Model Card Contact |
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You can contact the model card authors through following channels: |
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[More Information Needed] |
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# Citation |
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Below you can find information related to citation. |
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**BibTeX:** |
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``` |
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bibtex |
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@inproceedings{...,year={2020}} |
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``` |
