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Browse files- GGU-CLF.pkl +2 -2
- README.md +107 -93
- config.json +4 -4
- confusion_matrix.png +0 -0
GGU-CLF.pkl
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README.md
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- text-classification
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- english
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- german
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model_format: pickle
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model_file: GGU-CLF.pkl
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get_started_code: "```python\nimport pickle\nwith open(pkl_filename, 'rb') as file:\n\
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## Training Procedure
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-
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### 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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| memory |
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| steps | [('vect', TfidfVectorizer(ngram_range=(
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| verbose | False
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| vect | TfidfVectorizer(ngram_range=(
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| clf | MultinomialNB(alpha=0.
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| vect__analyzer |
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| vect__binary | False
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| vect__decode_error | strict
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| vect__dtype | <class 'numpy.float64'>
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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 | (
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| vect__norm | l2
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| vect__preprocessor |
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| vect__smooth_idf | True
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| vect__stop_words |
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| vect__strip_accents |
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| vect__sublinear_tf | False
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| vect__token_pattern | (?u)\b\w\w+\b
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| vect__tokenizer |
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| vect__use_idf | True
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| vect__vocabulary |
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| clf__alpha | 0.
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| clf__class_prior |
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| clf__fit_prior | True
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| clf__force_alpha | True
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</details>
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### Model Plot
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<style>#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}div.sk-parallel-item,
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div.sk-serial,
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div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
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}/* Parallel-specific style estimator block */#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}/* Serial-specific style estimator block */#sk-container-id-
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}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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clickable and can be expanded/collapsed.
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- Pipeline and ColumnTransformer use this feature and define the default style
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- Estimators will overwrite some part of the style using the `sk-estimator` class
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*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-
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}/* Toggleable label */
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#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}/* Toggleable content - dropdown */#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-
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}#sk-container-id-
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}/* Estimator-specific style *//* Colorize estimator box */
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#sk-container-id-
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}#sk-container-id-
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}#sk-container-id-
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#sk-container-id-
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}/* On hover, darken the color of the background */
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#sk-container-id-
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}/* Label box, darken color on hover, fitted */
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#sk-container-id-
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}/* Estimator label */#sk-container-id-
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}#sk-container-id-
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}/* Estimator-specific */
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#sk-container-id-
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}#sk-container-id-
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}/* on hover */
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#sk-container-id-
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}#sk-container-id-
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}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
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a:link.sk-estimator-doc-link,
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a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
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.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
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}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
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}.sk-estimator-doc-link:hover span {display: block;
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}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-
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}#sk-container-id-
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}/* On hover */
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#sk-container-id-
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}#sk-container-id-
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}
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</style><div id="sk-container-id-
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## Evaluation Results
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| Metric | Value |
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|----------|----------|
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| accuracy | 0.
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| f1 score | 0.
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### Evaluation Methods
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#### Model description/Evaluation Results/Evaluation Methods/Classification Report
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<details>
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<summary> Click to expand </summary>
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| index | precision | recall | f1-score | support |
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|--------------|-------------|----------|------------|-----------|
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| greeting | 0.977011 | 0.934066 | 0.955056 | 91 |
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| gratitude | 1 | 0.905405 | 0.950355 | 74 |
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| unknown | 0.925466 | 0.993333 | 0.958199 | 150 |
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| macro avg | 0.967492 | 0.944268 | 0.954537 | 315 |
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| weighted avg | 0.957866 | 0.955556 | 0.955448 | 315 |
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</details>
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# How to Get Started with the Model
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```python
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This model card is written by following authors:
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[philipp-zettl](https://huggingface.co/philipp-zettl/)
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- text-classification
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- english
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- german
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datasets:
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- philipp-zettl/GGU-xx
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model_format: pickle
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model_file: GGU-CLF.pkl
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get_started_code: "```python\nimport pickle\nwith open(pkl_filename, 'rb') as file:\n\
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## Training Procedure
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This model was trained using the [philipp-zettl/GGU-xx](https://huggingface.co/datasets/philipp-zettl/GGU-xx) dataset.
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You can find it's performance metrics under [Evaluation Results](#evaluation-results).
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### 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', TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 3))), ('clf', MultinomialNB(alpha=0.01))] |
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| verbose | False |
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| vect | TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 3)) |
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| clf | MultinomialNB(alpha=0.01) |
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| vect__analyzer | char_wb |
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| vect__binary | False |
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| vect__decode_error | strict |
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| vect__dtype | <class 'numpy.float64'> |
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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 | (2, 3) |
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| vect__norm | l2 |
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| vect__preprocessor | |
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| vect__smooth_idf | True |
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| vect__stop_words | |
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| vect__strip_accents | |
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| vect__sublinear_tf | False |
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| vect__token_pattern | (?u)\b\w\w+\b |
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| vect__tokenizer | |
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| vect__use_idf | True |
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| vect__vocabulary | |
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| clf__alpha | 0.01 |
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| clf__class_prior | |
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| clf__fit_prior | True |
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| clf__force_alpha | True |
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</details>
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### Model Plot
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<style>#sk-container-id-11 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
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}#sk-container-id-11 {color: var(--sklearn-color-text);
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}#sk-container-id-11 pre {padding: 0;
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}#sk-container-id-11 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;
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}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
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}#sk-container-id-11 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 thedefault 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;
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}#sk-container-id-11 div.sk-text-repr-fallback {display: none;
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}div.sk-parallel-item,
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div.sk-serial,
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div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
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}/* Parallel-specific style estimator block */#sk-container-id-11 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
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}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
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}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;
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}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
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}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
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}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;
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}/* Serial-specific style estimator block */#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
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}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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clickable and can be expanded/collapsed.
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- Pipeline and ColumnTransformer use this feature and define the default style
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- Estimators will overwrite some part of the style using the `sk-estimator` class
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*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-11 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
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}/* Toggleable label */
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#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
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}#sk-container-id-11 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
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}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
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}/* Toggleable content - dropdown */#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
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}#sk-container-id-11 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
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}#sk-container-id-11 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
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}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
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}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
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}/* Estimator-specific style *//* Colorize estimator box */
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#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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}#sk-container-id-11 div.sk-label label.sk-toggleable__label,
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#sk-container-id-11 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
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}/* On hover, darken the color of the background */
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#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
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}/* Label box, darken color on hover, fitted */
|
| 152 |
+
#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
|
| 153 |
+
}/* Estimator label */#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
|
| 154 |
+
}#sk-container-id-11 div.sk-label-container {text-align: center;
|
| 155 |
}/* Estimator-specific */
|
| 156 |
+
#sk-container-id-11 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
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| 157 |
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}#sk-container-id-11 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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| 158 |
}/* on hover */
|
| 159 |
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#sk-container-id-11 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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| 160 |
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}#sk-container-id-11 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
|
| 162 |
a:link.sk-estimator-doc-link,
|
| 163 |
a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
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| 177 |
.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
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}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
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| 179 |
}.sk-estimator-doc-link:hover span {display: block;
|
| 180 |
+
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-11 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
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}#sk-container-id-11 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
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| 182 |
}/* On hover */
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| 183 |
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#sk-container-id-11 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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}#sk-container-id-11 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
|
| 185 |
}
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| 186 |
+
</style><div id="sk-container-id-11" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('vect',TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 3))),('clf', MultinomialNB(alpha=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('vect',TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 3))),('clf', MultinomialNB(alpha=0.01))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> TfidfVectorizer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></label><div class="sk-toggleable__content fitted"><pre>TfidfVectorizer(analyzer='char_wb', ngram_range=(2, 3))</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> MultinomialNB<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html">?<span>Documentation for MultinomialNB</span></a></label><div class="sk-toggleable__content fitted"><pre>MultinomialNB(alpha=0.01)</pre></div> </div></div></div></div></div></div>
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## Evaluation Results
|
| 189 |
|
| 190 |
| Metric | Value |
|
| 191 |
|----------|----------|
|
| 192 |
+
| accuracy | 0.987055 |
|
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| f1 score | 0.987055 |
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| 194 |
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| 195 |
### Evaluation Methods
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# How to Get Started with the Model
|
| 204 |
|
| 205 |
```python
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|
| 213 |
This model card is written by following authors:
|
| 214 |
|
| 215 |
[philipp-zettl](https://huggingface.co/philipp-zettl/)
|
| 216 |
+
|
| 217 |
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# Classification Report
|
| 218 |
+
|
| 219 |
+
<details>
|
| 220 |
+
<summary> Click to expand </summary>
|
| 221 |
+
|
| 222 |
+
| index | precision | recall | f1-score | support |
|
| 223 |
+
|--------------|-------------|----------|------------|-----------|
|
| 224 |
+
| greeting | 0.978261 | 0.978261 | 0.978261 | 92 |
|
| 225 |
+
| gratitude | 1 | 0.977011 | 0.988372 | 87 |
|
| 226 |
+
| unknown | 0.984848 | 1 | 0.992366 | 130 |
|
| 227 |
+
| macro avg | 0.987703 | 0.985091 | 0.986333 | 309 |
|
| 228 |
+
| weighted avg | 0.987153 | 0.987055 | 0.987042 | 309 |
|
| 229 |
+
|
| 230 |
+
</details>
|
| 231 |
+
|
| 232 |
+
# Training Procedure
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
This model was trained using the [philipp-zettl/GGU-xx](https://huggingface.co/datasets/philipp-zettl/GGU-xx) dataset.
|
| 236 |
+
|
| 237 |
+
You can find it's performance metrics under [Evaluation Results](#evaluation-results).
|
| 238 |
+
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config.json
CHANGED
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|
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{
|
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"sklearn": {
|
| 3 |
"environment": [
|
| 4 |
-
"scikit-learn=1.
|
| 5 |
],
|
| 6 |
"example_input": {
|
| 7 |
"data": [
|
| 8 |
-
"
|
| 9 |
-
"
|
| 10 |
-
"
|
| 11 |
]
|
| 12 |
},
|
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"model": {
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{
|
| 2 |
"sklearn": {
|
| 3 |
"environment": [
|
| 4 |
+
"scikit-learn=1.5.0"
|
| 5 |
],
|
| 6 |
"example_input": {
|
| 7 |
"data": [
|
| 8 |
+
"list of english-speaking dentists in berlin",
|
| 9 |
+
"Got it! And what's Mietb\u00fcrgschaft",
|
| 10 |
+
"hast du einen kurs f\u00fcr sozialp\u00e4dagogik?"
|
| 11 |
]
|
| 12 |
},
|
| 13 |
"model": {
|
confusion_matrix.png
CHANGED
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