--- language: - multilingual - de - en license: mit library_name: sklearn tags: - sklearn - skops - text-classification - english - german datasets: - philipp-zettl/GGU-xx model_format: pickle model_file: GGU-CLF.pkl get_started_code: "```python\nimport pickle\nwith open(pkl_filename, 'rb') as file:\n\ \ clf = pickle.load(file)\n```" model_card_authors: https://huggingface.co/philipp-zettl limitations: This model is ready to be used in production. model_description: GGU (Greeting/Gratitude/Unknown) classifier for natural language chat messages. model_id: GGU-CLF funded_by: https://huggingface.co/easybits repo: https://huggingface.co/philipp-zettl/GGU-CLF widget: - example_title: 'Greeting (English #1)' text: Hey there - example_title: 'Greeting (English #2)' text: Good to see you - example_title: Greeting (German) text: Guten Morgen - example_title: 'Gratitude (English #1)' text: Thank you - example_title: 'Gratitude (English #2)' text: Cheers mate --- # Model description This is a Multinomial Naive Bayes model trained on a custom dataset. Count vectorizer is used for vectorization. It is used to classify user text into the classes: - 0: Greeting - 1: Gratitude - 2: Unknown ## Intended uses & limitations ### Direct use Use this model to classify messages from natural laguage chats. ### Out Of Scope Usage The model was not trained on multi-sentence samples. You should avoid those. Officially tested and supported languages are **english, german** any other language is considered out of scope. ## Training Procedure This model was trained using the [philipp-zettl/GGU-xx](https://huggingface.co/datasets/philipp-zettl/GGU-xx) dataset. You can find it's performance metrics under [Evaluation Results](#evaluation-results). ### Hyperparameters

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| Hyperparameter | Value | |---------------------|---------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('vect', TfidfVectorizer(analyzer='char_wb', lowercase=False, ngram_range=(1, 3))), ('clf', MultinomialNB(alpha=0.112))] | | verbose | False | | vect | TfidfVectorizer(analyzer='char_wb', lowercase=False, ngram_range=(1, 3)) | | clf | MultinomialNB(alpha=0.112) | | vect__analyzer | char_wb | | vect__binary | False | | vect__decode_error | strict | | vect__dtype | | | vect__encoding | utf-8 | | vect__input | content | | vect__lowercase | False | | vect__max_df | 1.0 | | vect__max_features | | | vect__min_df | 1 | | vect__ngram_range | (1, 3) | | vect__norm | l2 | | vect__preprocessor | | | vect__smooth_idf | True | | vect__stop_words | | | vect__strip_accents | | | vect__sublinear_tf | False | | vect__token_pattern | (?u)\b\w\w+\b | | vect__tokenizer | | | vect__use_idf | True | | vect__vocabulary | | | clf__alpha | 0.112 | | clf__class_prior | | | clf__fit_prior | True | | clf__force_alpha | True |

### Model Plot

Pipeline(steps=[('vect',TfidfVectorizer(analyzer='char_wb', lowercase=False,ngram_range=(1, 3))),('clf', MultinomialNB(alpha=0.112))])

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## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.951691 | | f1 score | 0.951691 | ### Evaluation Methods The model is evaluated on validation data from the dataset's test split, using accuracy and F1-score with micro average. #### Confusion matrix ![Confusion matrix](confusion_matrix.png) ### Model description/Evaluation Results/Classification Report

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| index | precision | recall | f1-score | support | |--------------|-------------|----------|------------|-----------| | greeting | 0.926471 | 0.969231 | 0.947368 | 65 | | gratitude | 0.982456 | 0.888889 | 0.933333 | 63 | | unknown | 0.95122 | 0.987342 | 0.968944 | 79 | | macro avg | 0.953382 | 0.948487 | 0.949882 | 207 | | weighted avg | 0.952955 | 0.951691 | 0.951331 | 207 |

# How to Get Started with the Model ```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: [philipp-zettl](https://huggingface.co/philipp-zettl/)