added model
Browse files- README.md +11 -12
- config.json +3 -3
- model.joblib +2 -2
README.md
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LegalName:
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- Fabryka Pierścieni Tłokowych "Prima" S.A. w Łodzi
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---
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# Model description
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| Hyperparameter | Value |
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|------------------------------------------------------|----------------------------------------------------------------|
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| memory | |
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| steps | [('feature_extraction', ColumnTransformer(transformers=[('abbreviations',<br /> <__main__.ELFAbbreviationTransformer object at
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| verbose | False |
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| feature_extraction | ColumnTransformer(transformers=[('abbreviations',<br /> <__main__.ELFAbbreviationTransformer object at
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| classifier | ComplementNB() |
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| feature_extraction__n_jobs | |
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| feature_extraction__remainder | drop |
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| feature_extraction__sparse_threshold | 0.3 |
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| feature_extraction__transformer_weights | |
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| feature_extraction__transformers | [('abbreviations', <__main__.ELFAbbreviationTransformer object at
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| feature_extraction__verbose | False |
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| feature_extraction__verbose_feature_names_out | True |
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| feature_extraction__abbreviations | <__main__.ELFAbbreviationTransformer object at
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| feature_extraction__tokenizer | CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<function tokenize at
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| feature_extraction__abbreviations__elf_abbreviations | <__main__.ELFAbbreviations object at
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| feature_extraction__abbreviations__jurisdiction | PL |
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| feature_extraction__abbreviations__use_endswith | True |
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| feature_extraction__abbreviations__use_lowercasing | True |
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| feature_extraction__tokenizer__stop_words | |
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| feature_extraction__tokenizer__strip_accents | |
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| feature_extraction__tokenizer__token_pattern | (?u)\b\w\w+\b |
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| feature_extraction__tokenizer__tokenizer | <function tokenize at
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| feature_extraction__tokenizer__vocabulary | |
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| classifier__alpha | 1.0 |
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| classifier__class_prior | |
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The model plot is below.
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<style>#sk-
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## Evaluation Results
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widget:
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structuredData:
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LegalName:
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- TECH LAKE SYSTEMS SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
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- Radosław Wiśniewski wspólnik spółki cywilnej Agenda
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- SINOGRAF SPÓŁKA AKCYJNA
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---
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# Model description
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| Hyperparameter | Value |
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|------------------------------------------------------|----------------------------------------------------------------|
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| memory | |
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| steps | [('feature_extraction', ColumnTransformer(transformers=[('abbreviations',<br /> <__main__.ELFAbbreviationTransformer object at 0x7f38e1fc3310>,<br /> 0),<br /> ('tokenizer',<br /> CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<function tokenize at 0x7f38e9fd4a60>),<br /> 0)])), ('classifier', ComplementNB())] |
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| verbose | False |
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| feature_extraction | ColumnTransformer(transformers=[('abbreviations',<br /> <__main__.ELFAbbreviationTransformer object at 0x7f38e1fc3310>,<br /> 0),<br /> ('tokenizer',<br /> CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<function tokenize at 0x7f38e9fd4a60>),<br /> 0)]) |
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| classifier | ComplementNB() |
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| feature_extraction__n_jobs | |
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| feature_extraction__remainder | drop |
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| feature_extraction__sparse_threshold | 0.3 |
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| feature_extraction__transformer_weights | |
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| feature_extraction__transformers | [('abbreviations', <__main__.ELFAbbreviationTransformer object at 0x7f38e1fc3310>, 0), ('tokenizer', CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<function tokenize at 0x7f38e9fd4a60>), 0)] |
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| feature_extraction__verbose | False |
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| feature_extraction__verbose_feature_names_out | True |
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| feature_extraction__abbreviations | <__main__.ELFAbbreviationTransformer object at 0x7f38e1fc3310> |
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| feature_extraction__tokenizer | CountVectorizer(binary=True, lowercase=False,<br /> tokenizer=<function tokenize at 0x7f38e9fd4a60>) |
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| feature_extraction__abbreviations__elf_abbreviations | <__main__.ELFAbbreviations object at 0x7f38e1f10220> |
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| feature_extraction__abbreviations__jurisdiction | PL |
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| feature_extraction__abbreviations__use_endswith | True |
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| feature_extraction__abbreviations__use_lowercasing | True |
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| feature_extraction__tokenizer__stop_words | |
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| feature_extraction__tokenizer__strip_accents | |
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| feature_extraction__tokenizer__token_pattern | (?u)\b\w\w+\b |
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| feature_extraction__tokenizer__tokenizer | <function tokenize at 0x7f38e9fd4a60> |
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| feature_extraction__tokenizer__vocabulary | |
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| classifier__alpha | 1.0 |
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| classifier__class_prior | |
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The model plot is below.
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<style>#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 {color: black;background-color: white;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 pre{padding: 0;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-toggleable {background-color: white;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 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-c81767c7-d9d8-4769-ac5d-4b45f8890723 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-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-estimator:hover {background-color: #d4ebff;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-item {z-index: 1;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-parallel-item:only-child::after {width: 0;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 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-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-c81767c7-d9d8-4769-ac5d-4b45f8890723 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-c81767c7-d9d8-4769-ac5d-4b45f8890723 div.sk-text-repr-fallback {display: none;}</style><div id="sk-c81767c7-d9d8-4769-ac5d-4b45f8890723" 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 0x7f38e1fc3310>,0),('tokenizer',CountVectorizer(binary=True,lowercase=False,tokenizer=<function tokenize at 0x7f38e9fd4a60>),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="7f00d457-5727-4740-99d7-a67bc34935ac" type="checkbox" ><label for="7f00d457-5727-4740-99d7-a67bc34935ac" 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 0x7f38e1fc3310>,0),('tokenizer',CountVectorizer(binary=True,lowercase=False,tokenizer=<function tokenize at 0x7f38e9fd4a60>),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="5dd188cf-eddf-4755-ba7a-1d04df0a93ce" type="checkbox" ><label for="5dd188cf-eddf-4755-ba7a-1d04df0a93ce" 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 0x7f38e1fc3310>,0),('tokenizer',CountVectorizer(binary=True, lowercase=False,tokenizer=<function tokenize at 0x7f38e9fd4a60>),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="b88e1154-d09d-483f-8022-dbb832f1b5d6" type="checkbox" ><label for="b88e1154-d09d-483f-8022-dbb832f1b5d6" 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="58a31e39-15f6-44a5-bc43-3d278c14db81" type="checkbox" ><label for="58a31e39-15f6-44a5-bc43-3d278c14db81" class="sk-toggleable__label sk-toggleable__label-arrow">ELFAbbreviationTransformer</label><div class="sk-toggleable__content"><pre><__main__.ELFAbbreviationTransformer object at 0x7f38e1fc3310></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="008f948d-968e-432e-b9bb-4f7fce72a248" type="checkbox" ><label for="008f948d-968e-432e-b9bb-4f7fce72a248" 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="26441d6e-c365-4cd3-84d9-44068e298da1" type="checkbox" ><label for="26441d6e-c365-4cd3-84d9-44068e298da1" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer(binary=True, lowercase=False,tokenizer=<function tokenize at 0x7f38e9fd4a60>)</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="75a60a57-701f-4e72-9ca6-f6deb6ef119f" type="checkbox" ><label for="75a60a57-701f-4e72-9ca6-f6deb6ef119f" 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>
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## Evaluation Results
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config.json
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],
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"example_input": {
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"LegalName": [
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"model": {
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],
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"example_input": {
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"LegalName": [
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"TECH LAKE SYSTEMS SP\u00d3\u0141KA Z OGRANICZON\u0104 ODPOWIEDZIALNO\u015aCI\u0104",
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"Rados\u0142aw Wi\u015bniewski wsp\u00f3lnik sp\u00f3\u0142ki cywilnej Agenda",
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"SINOGRAF SP\u00d3\u0141KA AKCYJNA"
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]
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},
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"model": {
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model.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:d919345fe63921c5b2300c2c2e0977c82de304abad0e22feb2afdde13571b286
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size 10283805
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