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verbatims-unvalid

Text Classification
Scikit-learn
skops
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verbatims-unvalid / README.md
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huynhdoo
pushing model SVC with camember base embeddings
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metadata
library_name: sklearn
license: mit
tags:
  - sklearn
  - skops
  - text-classification
model_format: pickle
model_file: skops-zquiq5g5.pkl

Model description

This is a Support Vector Classifier model trained on SIRIUS 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

Click to expand
Hyperparameter Value
memory
steps [('columntransformer', ColumnTransformer(transformers=[('num',
Pipeline(steps=[('imputer',
SimpleImputer(strategy='median')),
('scaler', StandardScaler()),
('pca',
PCA(n_components=84))]),
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=84))]),
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__force_int_remainder_cols True
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=84))]), 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=84))])
columntransformer__num__memory
columntransformer__num__steps [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=84))]
columntransformer__num__verbose False
columntransformer__num__imputer SimpleImputer(strategy='median')
columntransformer__num__scaler StandardScaler()
columntransformer__num__pca PCA(n_components=84)
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__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 84
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=84))]),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 0.935065
f1 score 0.935709

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={2024}}

get_started_code

import pickle as pickle with open(pkl_filename, 'rb') as file: pipe = pickle.load(file)