metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - text-classification
model_format: pickle
model_file: GGU-CLF-40m0exlk.pkl

Model description

[More Information Needed]

Intended uses & limitations

[More Information Needed]

Training Procedure

[More Information Needed]

Hyperparameters

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Hyperparameter	Value
memory
steps	[('vect', CountVectorizer()), ('clf', MultinomialNB())]
verbose	False
vect	CountVectorizer()
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
clf__alpha	1.0
clf__class_prior
clf__fit_prior	True
clf__force_alpha	True

Model Plot

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

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Evaluation Results

Metric	Value
accuracy	0.949206
f1 score	0.949206

How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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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:

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citation_bibtex

get_started_code

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

model_card_authors

philipp-zettl

limitations

This model is ready to be used in production.

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: Greeting1: Gratitude2: Unknown

eval_method

The model is evaluated on validation data from the dataset's test split, using accuracy and F1-score with micro average.

Confusion matrix

Classification Report

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index	precision	recall	f1-score	support
greeting	0.965517	0.923077	0.94382	91
gratitude	1	0.905405	0.950355	74
unknown	0.919255	0.986667	0.951768	150
macro avg	0.961591	0.938383	0.948648	315
weighted avg	0.951588	0.949206	0.94914	315