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---
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.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|------------------------------------------------------|----------------------------------------------------------------|
| memory | |
| steps | [('feature_extraction', ColumnTransformer(transformers=[('abbreviations',<br /> <__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>,<br /> 0),<br /> ('tokenizer',<br /> CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>),<br /> 0)])), ('classifier', ComplementNB())] |
| verbose | False |
| feature_extraction | ColumnTransformer(transformers=[('abbreviations',<br /> <__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>,<br /> 0),<br /> ('tokenizer',<br /> CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>),<br /> 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,<br /> 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,<br /> 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 | <class 'numpy.int64'> |
| 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 |
</details>
### Model Plot
The model plot is below.
<style>#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 {color: black;background-color: white;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 pre{padding: 0;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-toggleable {background-color: white;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-item {z-index: 1;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-parallel-item:only-child::after {width: 0;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-e1208602-57d4-43f2-85c3-031517eb1aa4 div.sk-text-repr-fallback {display: none;}</style><div id="sk-e1208602-57d4-43f2-85c3-031517eb1aa4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b22014d7-b892-49d0-a00f-77d5d3d91ace" type="checkbox" ><label for="b22014d7-b892-49d0-a00f-77d5d3d91ace" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>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())])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="be227d86-c6ce-4eff-88e2-6efe9bed489a" type="checkbox" ><label for="be227d86-c6ce-4eff-88e2-6efe9bed489a" class="sk-toggleable__label sk-toggleable__label-arrow">feature_extraction: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('abbreviations',<__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0>,0),('tokenizer',CountVectorizer(binary=True, lowercase=False,tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>),0)])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6b957cb5-d512-4dc4-8b89-0ce196c51db5" type="checkbox" ><label for="6b957cb5-d512-4dc4-8b89-0ce196c51db5" class="sk-toggleable__label sk-toggleable__label-arrow">abbreviations</label><div class="sk-toggleable__content"><pre>0</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a5d85fa3-7e72-43b2-b560-cf0b9bdf1b6b" type="checkbox" ><label for="a5d85fa3-7e72-43b2-b560-cf0b9bdf1b6b" class="sk-toggleable__label sk-toggleable__label-arrow">ELFAbbreviationTransformer</label><div class="sk-toggleable__content"><pre><__main__.ELFAbbreviationTransformer object at 0x7f38e082e4f0></pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2748f0f3-5698-4d09-83c0-f7a236486111" type="checkbox" ><label for="2748f0f3-5698-4d09-83c0-f7a236486111" class="sk-toggleable__label sk-toggleable__label-arrow">tokenizer</label><div class="sk-toggleable__content"><pre>0</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2adc89fe-7735-42a2-8fc4-1c272b44e547" type="checkbox" ><label for="2adc89fe-7735-42a2-8fc4-1c272b44e547" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer(binary=True, lowercase=False,tokenizer=<__main__.LegalEntityTokenizer object at 0x7f38e082ee50>)</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="330d4134-5949-4a02-985a-2a27ef3ed24c" type="checkbox" ><label for="330d4134-5949-4a02-985a-2a27ef3ed24c" class="sk-toggleable__label sk-toggleable__label-arrow">ComplementNB</label><div class="sk-toggleable__content"><pre>ComplementNB()</pre></div></div></div></div></div></div></div>
## 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]
```
|