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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

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)

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