--- license: mit library_name: sklearn tags: - sklearn - skops - text-classification model_format: pickle model_file: skops-4dusypwz.pkl --- # Model description This is a `RandomForestClassifier` 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 ### Hyperparameters The model is trained with below hyperparameters.

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| Hyperparameter | Value | |---------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('scaler', StandardScaler()), ('pca', PCA(n_components=374)), ('rfc', RandomForestClassifier(class_weight='balanced', random_state=42))] | | verbose | False | | scaler | StandardScaler() | | pca | PCA(n_components=374) | | rfc | RandomForestClassifier(class_weight='balanced', random_state=42) | | scaler__copy | True | | scaler__with_mean | True | | scaler__with_std | True | | pca__copy | True | | pca__iterated_power | auto | | pca__n_components | 374 | | pca__n_oversamples | 10 | | pca__power_iteration_normalizer | auto | | pca__random_state | | | pca__svd_solver | auto | | pca__tol | 0.0 | | pca__whiten | False | | rfc__bootstrap | True | | rfc__ccp_alpha | 0.0 | | rfc__class_weight | balanced | | rfc__criterion | gini | | rfc__max_depth | | | rfc__max_features | sqrt | | rfc__max_leaf_nodes | | | rfc__max_samples | | | rfc__min_impurity_decrease | 0.0 | | rfc__min_samples_leaf | 1 | | rfc__min_samples_split | 2 | | rfc__min_weight_fraction_leaf | 0.0 | | rfc__n_estimators | 100 | | rfc__n_jobs | | | rfc__oob_score | False | | rfc__random_state | 42 | | rfc__verbose | 0 | | rfc__warm_start | False |

### Model Plot The model plot is below.

Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=374)),('rfc',RandomForestClassifier(class_weight='balanced',random_state=42))])

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## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.962669 | | f1 score | 0.945696 | ### 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)