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.

Click to expand

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

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)