pushing model to the Hugging Face Hub

README.md

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_format: pickle
+model_file: skops-s5qwyvyp.pkl
+widget:
+- structuredData:
+    Bene_Country:
+    - COMOROS
+    - CANADA
+    - MOROCCO
+    Sender_Country:
+    - SRI-LANKA
+    - USA
+    - USA
+    Transaction_Type:
+    - MOVE-FUNDS
+    - PAY-CHECK
+    - MAKE-PAYMENT
+    USD_amount:
+    - 598.31
+    - 398.72
+    - 87.03
+---
+
+# Model description
+
+[More Information Needed]
+
+## Intended uses & limitations
+
+[More Information Needed]
+
+## Training Procedure
+
+[More Information Needed]
+
+### Hyperparameters
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter                               | Value                                                                     |
+|----------------------------------------------|---------------------------------------------------------------------------|
+| memory                                       |                                                                           |
+| steps                                        | [('preprocessorAll', ColumnTransformer(remainder='passthrough',<br />                  transformers=[('cat',<br />                                 Pipeline(steps=[('onehot',<br />                                                  OneHotEncoder(handle_unknown='ignore',<br />                                                                sparse_output=False))]),<br />                                 ['Sender_Country', 'Bene_Country',<br />                                  'Transaction_Type']),<br />                                ('num',<br />                                 Pipeline(steps=[('scale', StandardScaler())]),<br />                                 Index(['USD_amount'], dtype='object'))])), ('classifier', GaussianNB())]                                                                           |
+| verbose                                      | False                                                                     |
+| preprocessorAll                              | ColumnTransformer(remainder='passthrough',<br />                  transformers=[('cat',<br />                                 Pipeline(steps=[('onehot',<br />                                                  OneHotEncoder(handle_unknown='ignore',<br />                                                                sparse_output=False))]),<br />                                 ['Sender_Country', 'Bene_Country',<br />                                  'Transaction_Type']),<br />                                ('num',<br />                                 Pipeline(steps=[('scale', StandardScaler())]),<br />                                 Index(['USD_amount'], dtype='object'))])                                                                           |
+| classifier                                   | GaussianNB()                                                              |
+| preprocessorAll__n_jobs                      |                                                                           |
+| preprocessorAll__remainder                   | passthrough                                                               |
+| preprocessorAll__sparse_threshold            | 0.3                                                                       |
+| preprocessorAll__transformer_weights         |                                                                           |
+| preprocessorAll__transformers                | [('cat', Pipeline(steps=[('onehot',<br />                 OneHotEncoder(handle_unknown='ignore', sparse_output=False))]), ['Sender_Country', 'Bene_Country', 'Transaction_Type']), ('num', Pipeline(steps=[('scale', StandardScaler())]), Index(['USD_amount'], dtype='object'))]                                                                           |
+| preprocessorAll__verbose                     | False                                                                     |
+| preprocessorAll__verbose_feature_names_out   | True                                                                      |
+| preprocessorAll__cat                         | Pipeline(steps=[('onehot',<br />                 OneHotEncoder(handle_unknown='ignore', sparse_output=False))])                                                                           |
+| preprocessorAll__num                         | Pipeline(steps=[('scale', StandardScaler())])                             |
+| preprocessorAll__cat__memory                 |                                                                           |
+| preprocessorAll__cat__steps                  | [('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))] |
+| preprocessorAll__cat__verbose                | False                                                                     |
+| preprocessorAll__cat__onehot                 | OneHotEncoder(handle_unknown='ignore', sparse_output=False)               |
+| preprocessorAll__cat__onehot__categories     | auto                                                                      |
+| preprocessorAll__cat__onehot__drop           |                                                                           |
+| preprocessorAll__cat__onehot__dtype          | <class 'numpy.float64'>                                                   |
+| preprocessorAll__cat__onehot__handle_unknown | ignore                                                                    |
+| preprocessorAll__cat__onehot__max_categories |                                                                           |
+| preprocessorAll__cat__onehot__min_frequency  |                                                                           |
+| preprocessorAll__cat__onehot__sparse         | deprecated                                                                |
+| preprocessorAll__cat__onehot__sparse_output  | False                                                                     |
+| preprocessorAll__num__memory                 |                                                                           |
+| preprocessorAll__num__steps                  | [('scale', StandardScaler())]                                             |
+| preprocessorAll__num__verbose                | False                                                                     |
+| preprocessorAll__num__scale                  | StandardScaler()                                                          |
+| preprocessorAll__num__scale__copy            | True                                                                      |
+| preprocessorAll__num__scale__with_mean       | True                                                                      |
+| preprocessorAll__num__scale__with_std        | True                                                                      |
+| classifier__priors                           |                                                                           |
+| classifier__var_smoothing                    | 1e-09                                                                     |
+
+</details>
+
+### Model Plot
+
+<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-container-id-4 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-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessorAll&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,sparse_output=False))]),[&#x27;Sender_Country&#x27;,&#x27;Bene_Country&#x27;,&#x27;Transaction_Type&#x27;]),(&#x27;num&#x27;,Pipeline(steps=[(&#x27;scale&#x27;,StandardScaler())]),Index([&#x27;USD_amount&#x27;], dtype=&#x27;object&#x27;))])),(&#x27;classifier&#x27;, GaussianNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</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="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;preprocessorAll&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,sparse_output=False))]),[&#x27;Sender_Country&#x27;,&#x27;Bene_Country&#x27;,&#x27;Transaction_Type&#x27;]),(&#x27;num&#x27;,Pipeline(steps=[(&#x27;scale&#x27;,StandardScaler())]),Index([&#x27;USD_amount&#x27;], dtype=&#x27;object&#x27;))])),(&#x27;classifier&#x27;, GaussianNB())])</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="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessorAll: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,sparse_output=False))]),[&#x27;Sender_Country&#x27;, &#x27;Bene_Country&#x27;,&#x27;Transaction_Type&#x27;]),(&#x27;num&#x27;,Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler())]),Index([&#x27;USD_amount&#x27;], dtype=&#x27;object&#x27;))])</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="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>[&#x27;Sender_Country&#x27;, &#x27;Bene_Country&#x27;, &#x27;Transaction_Type&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></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="sk-estimator-id-32" type="checkbox" ><label for="sk-estimator-id-32" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>Index([&#x27;USD_amount&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></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="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</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="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</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="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label sk-toggleable__label-arrow">GaussianNB</label><div class="sk-toggleable__content"><pre>GaussianNB()</pre></div></div></div></div></div></div></div>
+
+## Evaluation Results
+
+| Metric   |    Value |
+|----------|----------|
+| accuracy | 0.794582 |
+
+### Confusion Matrix
+
+![Confusion Matrix](confusion_matrix.png)
+
+# 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

@@ -0,0 +1,41 @@
+{
+    "sklearn": {
+        "columns": [
+            "Sender_Country",
+            "Bene_Country",
+            "USD_amount",
+            "Transaction_Type"
+        ],
+        "environment": [
+            "scikit-learn=1.2.2"
+        ],
+        "example_input": {
+            "Bene_Country": [
+                "COMOROS",
+                "CANADA",
+                "MOROCCO"
+            ],
+            "Sender_Country": [
+                "SRI-LANKA",
+                "USA",
+                "USA"
+            ],
+            "Transaction_Type": [
+                "MOVE-FUNDS",
+                "PAY-CHECK",
+                "MAKE-PAYMENT"
+            ],
+            "USD_amount": [
+                598.31,
+                398.72,
+                87.03
+            ]
+        },
+        "model": {
+            "file": "skops-s5qwyvyp.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-classification",
+        "use_intelex": false
+    }
+}

skops-s5qwyvyp.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eacb88ad36b78a96d1ce37e304646a5b1a5f26eb1c25c9798f7c18eff304a915
+size 25153