jva-structure-report-v2

jeveuxaider/jva-structure-report-v2

jva-structure-report-v2 is a text classification model from jeveuxaider. Use it when you need a label for a piece of text. It is set up for sklearn. The card lists the license as mit.

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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Public

Updated Dec 28, 2023

Repo size

11.6 MB

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Public

Hugging Face

Repo makeup

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.pkl11.6 MB · 100%

At a glance

Task: Text Classification
Library: sklearn
License: mit
Access: Public
Created: Dec 28, 2023
Updated: Dec 28, 2023
SHA: ea827cf4

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Model card

From the Hugging Face model README

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

<details> <summary> Click to expand </summary>

Hyperparameter	Value
memory
steps	[('columntransformer', ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=689))]),<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)), ('randomforestclassifier', RandomForestClassifier(max_depth=15, max_features=20, min_samples_split=10,<br /> random_state=42))]
verbose	False
columntransformer	ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=689))]),<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)
randomforestclassifier	RandomForestClassifier(max_depth=15, max_features=20, min_samples_split=10,<br /> 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')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=689))]), 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))]
columntransformer__verbose	False
columntransformer__verbose_feature_names_out	False
columntransformer__num	Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=689))])
columntransformer__num__memory
columntransformer__num__steps	[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=689))]
columntransformer__num__verbose	False
columntransformer__num__imputer	SimpleImputer(strategy='median')
columntransformer__num__scaler	StandardScaler()
columntransformer__num__pca	PCA(n_components=689)
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	689
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
randomforestclassifier__bootstrap	True
randomforestclassifier__ccp_alpha	0.0
randomforestclassifier__class_weight
randomforestclassifier__criterion	gini
randomforestclassifier__max_depth	15
randomforestclassifier__max_features	20
randomforestclassifier__max_leaf_nodes
randomforestclassifier__max_samples
randomforestclassifier__min_impurity_decrease	0.0
randomforestclassifier__min_samples_leaf	1
randomforestclassifier__min_samples_split	10
randomforestclassifier__min_weight_fraction_leaf	0.0
randomforestclassifier__n_estimators	100
randomforestclassifier__n_jobs
randomforestclassifier__oob_score	False
randomforestclassifier__random_state	42
randomforestclassifier__verbose	0
randomforestclassifier__warm_start	False

</details>

Model Plot

Evaluation Results

Metric	Value
accuracy	0.964661
f1 score	0.964637

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)

Task: Text Classification
Library: sklearn
License: mit
Created: Dec 28, 2023
Updated: Dec 28, 2023