--- license: mit library_name: sklearn tags: - sklearn - skops - text-classification model_format: pickle model_file: skops-rlpuhh_z.pkl --- # Model description This is a `Support Vector Classifier` model trained on JeVeuxAider dataset. As input, the model takes text embeddings encoded with camembert-base (768 tokens) ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure [More Information Needed] ### Hyperparameters

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| Hyperparameter | Value | |---------------------------------------------------------|---------------------------------------------------------------------------------------------------------------| | memory | | | 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))] | | verbose | False | | 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) | | columntransformer__n_jobs | | | columntransformer__remainder | drop | | columntransformer__sparse_threshold | 0.3 | | columntransformer__transformer_weights | | | 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))] | | columntransformer__verbose | False | | columntransformer__verbose_feature_names_out | False | | columntransformer__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler()), ('pca', PCA(n_components=563))]) | | columntransformer__num__memory | | | columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))] | | columntransformer__num__verbose | False | | columntransformer__num__imputer | SimpleImputer(strategy='median') | | columntransformer__num__scaler | StandardScaler() | | columntransformer__num__pca | PCA(n_components=563) | | columntransformer__num__imputer__add_indicator | False | | columntransformer__num__imputer__copy | True | | columntransformer__num__imputer__fill_value | | | columntransformer__num__imputer__keep_empty_features | False | | columntransformer__num__imputer__missing_values | nan | | columntransformer__num__imputer__strategy | median | | columntransformer__num__imputer__verbose | deprecated | | columntransformer__num__scaler__copy | True | | columntransformer__num__scaler__with_mean | True | | columntransformer__num__scaler__with_std | True | | columntransformer__num__pca__copy | True | | columntransformer__num__pca__iterated_power | auto | | columntransformer__num__pca__n_components | 563 | | columntransformer__num__pca__n_oversamples | 10 | | columntransformer__num__pca__power_iteration_normalizer | auto | | columntransformer__num__pca__random_state | | | columntransformer__num__pca__svd_solver | auto | | columntransformer__num__pca__tol | 0.0 | | columntransformer__num__pca__whiten | False | | svc__C | 1.0 | | svc__break_ties | False | | svc__cache_size | 200 | | svc__class_weight | | | svc__coef0 | 0.0 | | svc__decision_function_shape | ovr | | svc__degree | 3 | | svc__gamma | scale | | svc__kernel | rbf | | svc__max_iter | -1 | | svc__probability | True | | svc__random_state | 42 | | svc__shrinking | True | | svc__tol | 0.001 | | svc__verbose | False |

### Model Plot

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))])

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## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.985849 | | f1 score | 0.985849 | ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) # How to Get Started with the Model [More Information Needed] # Model Card Authors huynhdoo # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation **BibTeX** ``` @inproceedings{...,year={2023}} ``` # get_started_code import pickle as pickle with open(pkl_filename, 'rb') as file: pipe = pickle.load(file)