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README.md +116 -0
config.json +25 -0
model.joblib +3 -0

README.md ADDED Viewed

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_format: pickle
+model_file: model.joblib
+widget:
+  structuredData:
+    LegalName:
+    - United Partners Sp. z o.o.
+    - 'RAFAŁ SZYMAŃSKI : IMPORT- EXPORT "RAFAEL"; STUDIO TAŃCA "PASJA" ,RAFAEL LOGISTICS,
+      VILLA LUANDA'
+    - Fabryka Pierścieni Tłokowych "Prima" S.A. w Łodzi
+---
+# 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 0x7f38e5329160>,<br />                                 0),<br />                                ('tokenizer',<br />                                 CountVectorizer(binary=True, lowercase=False,<br />                                                 tokenizer=<function tokenize at 0x7f38e46cb700>),<br />                                 0)])), ('classifier', ComplementNB())]                                                                |
+| verbose                                              | False                                                          |
+| feature_extraction                                   | ColumnTransformer(transformers=[('abbreviations',<br />                                 <__main__.ELFAbbreviationTransformer object at 0x7f38e5329160>,<br />                                 0),<br />                                ('tokenizer',<br />                                 CountVectorizer(binary=True, lowercase=False,<br />                                                 tokenizer=<function tokenize at 0x7f38e46cb700>),<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 0x7f38e5329160>, 0), ('tokenizer', CountVectorizer(binary=True, lowercase=False,<br />                tokenizer=<function tokenize at 0x7f38e46cb700>), 0)]                                                                |
+| feature_extraction__verbose                          | False                                                          |
+| feature_extraction__verbose_feature_names_out        | True                                                           |
+| feature_extraction__abbreviations                    | <__main__.ELFAbbreviationTransformer object at 0x7f38e5329160> |
+| feature_extraction__tokenizer                        | CountVectorizer(binary=True, lowercase=False,<br />                tokenizer=<function tokenize at 0x7f38e46cb700>)                                                                |
+| feature_extraction__abbreviations__elf_abbreviations | <__main__.ELFAbbreviations object at 0x7f38ebe22be0>           |
+| 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             | <function tokenize at 0x7f38e46cb700>                          |
+| 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-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b {color: black;background-color: white;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b pre{padding: 0;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-toggleable {background-color: white;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b 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-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b 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-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-estimator:hover {background-color: #d4ebff;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-item {z-index: 1;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-parallel-item:only-child::after {width: 0;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b 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-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b 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-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b div.sk-text-repr-fallback {display: none;}</style><div id="sk-f2c1bf91-a172-421a-80a6-a1cc1e6bfd1b" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;feature_extraction&#x27;,ColumnTransformer(transformers=[(&#x27;abbreviations&#x27;,&lt;__main__.ELFAbbreviationTransformer object at 0x7f38e5329160&gt;,0),(&#x27;tokenizer&#x27;,CountVectorizer(binary=True,lowercase=False,tokenizer=&lt;function tokenize at 0x7f38e46cb700&gt;),0)])),(&#x27;classifier&#x27;, 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="6c657019-d3cc-4bd2-acbf-c35e96ec9647" type="checkbox" ><label for="6c657019-d3cc-4bd2-acbf-c35e96ec9647" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;feature_extraction&#x27;,ColumnTransformer(transformers=[(&#x27;abbreviations&#x27;,&lt;__main__.ELFAbbreviationTransformer object at 0x7f38e5329160&gt;,0),(&#x27;tokenizer&#x27;,CountVectorizer(binary=True,lowercase=False,tokenizer=&lt;function tokenize at 0x7f38e46cb700&gt;),0)])),(&#x27;classifier&#x27;, 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="008a36e9-520c-4037-85bc-861bce722ea9" type="checkbox" ><label for="008a36e9-520c-4037-85bc-861bce722ea9" class="sk-toggleable__label sk-toggleable__label-arrow">feature_extraction: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;abbreviations&#x27;,&lt;__main__.ELFAbbreviationTransformer object at 0x7f38e5329160&gt;,0),(&#x27;tokenizer&#x27;,CountVectorizer(binary=True, lowercase=False,tokenizer=&lt;function tokenize at 0x7f38e46cb700&gt;),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="fc8382d0-2ecc-4ffe-9551-4fd3f22ab155" type="checkbox" ><label for="fc8382d0-2ecc-4ffe-9551-4fd3f22ab155" 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="ae011248-bf0c-4a79-a2d0-2816108a637c" type="checkbox" ><label for="ae011248-bf0c-4a79-a2d0-2816108a637c" class="sk-toggleable__label sk-toggleable__label-arrow">ELFAbbreviationTransformer</label><div class="sk-toggleable__content"><pre>&lt;__main__.ELFAbbreviationTransformer object at 0x7f38e5329160&gt;</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="bdd4bd27-45c9-466f-93a2-63d412751505" type="checkbox" ><label for="bdd4bd27-45c9-466f-93a2-63d412751505" 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="1a132d4a-141d-4f64-a7a1-5e2de5a882ec" type="checkbox" ><label for="1a132d4a-141d-4f64-a7a1-5e2de5a882ec" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer(binary=True, lowercase=False,tokenizer=&lt;function tokenize at 0x7f38e46cb700&gt;)</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="d8b42ef3-dbfe-4f5b-a6b9-a09031f4b76c" type="checkbox" ><label for="d8b42ef3-dbfe-4f5b-a6b9-a09031f4b76c" 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]
+```

config.json ADDED Viewed

	@@ -0,0 +1,25 @@

+{
+    "sklearn": {
+        "columns": [
+            "LegalName"
+        ],
+        "environment": [
+            "scikit-learn=1.0.2",
+            "joblib=1.2.0",
+            "pandas=1.3.5"
+        ],
+        "example_input": {
+            "LegalName": [
+                "United Partners Sp. z o.o.",
+                "RAFA\u0141 SZYMA\u0143SKI : IMPORT- EXPORT \"RAFAEL\"; STUDIO TA\u0143CA \"PASJA\" ,RAFAEL LOGISTICS, VILLA LUANDA",
+                "Fabryka Pier\u015bcieni T\u0142okowych \"Prima\" S.A. w \u0141odzi"
+            ]
+        },
+        "model": {
+            "file": "model.joblib"
+        },
+        "model_format": "pickle",
+        "task": "tabular-classification",
+        "use_intelex": false
+    }
+}

model.joblib ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:cfdd13517f6b615f898eeef17f64d966147e5d6af553e2d7f0c194526f6e0209
+size 10283757