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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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model_format: pickle |
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model_file: skops-rlpuhh_z.pkl |
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--- |
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# Model description |
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This is a `Support Vector Classifier` model trained on JeVeuxAider dataset. As input, the model takes text embeddings encoded with camembert-base (768 tokens) |
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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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[More Information Needed] |
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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 | [('columntransformer', ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=563))]),<br /> Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br /> 'avg_9', 'avg_10',<br /> ...<br /> 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br /> 'max_765', 'max_766', 'max_767', 'max_768'],<br /> dtype='object', length=2304))],<br /> verbose_feature_names_out=False)), ('svc', SVC(probability=True, random_state=42))] | |
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| verbose | False | |
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| columntransformer | ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=563))]),<br /> Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br /> 'avg_9', 'avg_10',<br /> ...<br /> 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br /> 'max_765', 'max_766', 'max_767', 'max_768'],<br /> dtype='object', length=2304))],<br /> verbose_feature_names_out=False) | |
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| svc | SVC(probability=True, random_state=42) | |
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| columntransformer__n_jobs | | |
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| columntransformer__remainder | drop | |
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| columntransformer__sparse_threshold | 0.3 | |
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| columntransformer__transformer_weights | | |
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| columntransformer__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=563))]), Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br /> 'avg_9', 'avg_10',<br /> ...<br /> 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br /> 'max_765', 'max_766', 'max_767', 'max_768'],<br /> dtype='object', length=2304))] | |
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| columntransformer__verbose | False | |
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| columntransformer__verbose_feature_names_out | False | |
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| columntransformer__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=563))]) | |
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| columntransformer__num__memory | | |
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| columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))] | |
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| columntransformer__num__verbose | False | |
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| columntransformer__num__imputer | SimpleImputer(strategy='median') | |
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| columntransformer__num__scaler | StandardScaler() | |
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| columntransformer__num__pca | PCA(n_components=563) | |
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| columntransformer__num__imputer__add_indicator | False | |
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| columntransformer__num__imputer__copy | True | |
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| columntransformer__num__imputer__fill_value | | |
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| columntransformer__num__imputer__keep_empty_features | False | |
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| columntransformer__num__imputer__missing_values | nan | |
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| columntransformer__num__imputer__strategy | median | |
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| columntransformer__num__imputer__verbose | deprecated | |
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| columntransformer__num__scaler__copy | True | |
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| columntransformer__num__scaler__with_mean | True | |
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| columntransformer__num__scaler__with_std | True | |
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| columntransformer__num__pca__copy | True | |
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| columntransformer__num__pca__iterated_power | auto | |
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| columntransformer__num__pca__n_components | 563 | |
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| columntransformer__num__pca__n_oversamples | 10 | |
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| columntransformer__num__pca__power_iteration_normalizer | auto | |
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| columntransformer__num__pca__random_state | | |
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| columntransformer__num__pca__svd_solver | auto | |
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| columntransformer__num__pca__tol | 0.0 | |
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| columntransformer__num__pca__whiten | False | |
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| svc__C | 1.0 | |
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| svc__break_ties | False | |
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| svc__cache_size | 200 | |
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| svc__class_weight | | |
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| svc__coef0 | 0.0 | |
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| svc__decision_function_shape | ovr | |
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| svc__degree | 3 | |
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| svc__gamma | scale | |
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| svc__kernel | rbf | |
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| svc__max_iter | -1 | |
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| svc__probability | True | |
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| svc__random_state | 42 | |
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| svc__shrinking | True | |
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| svc__tol | 0.001 | |
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| svc__verbose | False | |
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</details> |
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### Model Plot |
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<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-container-id-4 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-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler()),('pca',PCA(n_components=563))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', SVC(probability=True, random_state=42))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler()),('pca',PCA(n_components=563))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', SVC(probability=True, random_state=42))])</pre></div></div></div><div class="sk-serial"><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="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler()),('pca',PCA(n_components=563))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-22" type="checkbox" ><label for="sk-estimator-id-22" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">PCA</label><div class="sk-toggleable__content"><pre>PCA(n_components=563)</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC(probability=True, random_state=42)</pre></div></div></div></div></div></div></div> |
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## Evaluation Results |
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| Metric | Value | |
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|----------|----------| |
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| accuracy | 0.985849 | |
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| f1 score | 0.985849 | |
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### Confusion Matrix |
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# How to Get Started with the Model |
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[More Information Needed] |
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# Model Card Authors |
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huynhdoo |
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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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**BibTeX** |
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``` |
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@inproceedings{...,year={2023}} |
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``` |
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# get_started_code |
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import pickle as pickle |
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with open(pkl_filename, 'rb') as file: |
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pipe = pickle.load(file) |
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