--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: model.joblib widget: structuredData: LegalName: - Miejskie Przedsiębiorstwo Energetyki Cieplnej Spółka z ograniczoną odpowiedzialnością - Przedsiębiorstwo Produkcyjno Usługowe Mimal Krystyna Fludra - NGS OIL & GAS S.A. --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | Hyperparameter | Value | |------------------------------------------------------|----------------------------------------------------------------| | memory | | | steps | [('feature_extraction', ColumnTransformer(transformers=[('abbreviations',
<__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>,
0),
('tokenizer',
CountVectorizer(binary=True, lowercase=False,
tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>),
0)])), ('classifier', ComplementNB())] | | verbose | False | | feature_extraction | ColumnTransformer(transformers=[('abbreviations',
<__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>,
0),
('tokenizer',
CountVectorizer(binary=True, lowercase=False,
tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>),
0)]) | | classifier | ComplementNB() | | feature_extraction__n_jobs | | | feature_extraction__remainder | drop | | feature_extraction__sparse_threshold | 0.3 | | feature_extraction__transformer_weights | | | feature_extraction__transformers | [('abbreviations', <__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>, 0), ('tokenizer', CountVectorizer(binary=True, lowercase=False,
tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>), 0)] | | feature_extraction__verbose | False | | feature_extraction__verbose_feature_names_out | True | | feature_extraction__abbreviations | <__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0> | | feature_extraction__tokenizer | CountVectorizer(binary=True, lowercase=False,
tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>) | | feature_extraction__abbreviations__elf_abbreviations | <__main__.ELFAbbreviations object at 0x7f38f438b670> | | feature_extraction__abbreviations__jurisdiction | PL | | feature_extraction__abbreviations__use_endswith | True | | feature_extraction__abbreviations__use_lowercasing | True | | feature_extraction__tokenizer__analyzer | word | | feature_extraction__tokenizer__binary | True | | feature_extraction__tokenizer__decode_error | strict | | feature_extraction__tokenizer__dtype | | | feature_extraction__tokenizer__encoding | utf-8 | | feature_extraction__tokenizer__input | content | | feature_extraction__tokenizer__lowercase | False | | feature_extraction__tokenizer__max_df | 1.0 | | feature_extraction__tokenizer__max_features | | | feature_extraction__tokenizer__min_df | 1 | | feature_extraction__tokenizer__ngram_range | (1, 1) | | feature_extraction__tokenizer__preprocessor | | | feature_extraction__tokenizer__stop_words | | | feature_extraction__tokenizer__strip_accents | | | feature_extraction__tokenizer__token_pattern | (?u)\b\w\w+\b | | feature_extraction__tokenizer__tokenizer | <__main__.LegalEntityTokenizer object at 0x7f38e082ee50> | | feature_extraction__tokenizer__vocabulary | | | classifier__alpha | 1.0 | | classifier__class_prior | | | classifier__fit_prior | True | | classifier__norm | False |
### Model Plot The model plot is below. 
Pipeline(steps=[('feature_extraction',ColumnTransformer(transformers=[('abbreviations',<__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>,0),('tokenizer',CountVectorizer(binary=True,lowercase=False,tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>),0)])),('classifier', ComplementNB())])
Please rerun this cell to show the HTML repr or trust the notebook.
## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | f1 | 0.971647 | | f1 macro | 0.522164 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```