commit files to HF hub

README.md

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-regression
+model_file: linreg.pkl
+widget:
+  structuredData:
+    x0:
+    - -0.3839236795902252
+    - -0.9788183569908142
+    - 1.0937178134918213
+    x1:
+    - -0.5319488644599915
+    - -1.108436107635498
+    - 0.9354732036590576
+    x2:
+    - -0.38279563188552856
+    - -1.3128694295883179
+    - 1.4773520231246948
+    x3:
+    - 0.2815782427787781
+    - -0.11783809214830399
+    - -0.9529813528060913
+    x4:
+    - 1.0
+    - 1.0
+    - 0.0
+    x5:
+    - 0.0
+    - 0.0
+    - 0.0
+    x6:
+    - 0.0
+    - 0.0
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+    x7:
+    - 0.0
+    - 0.0
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+    x8:
+    - 0.0
+    - 1.0
+    - 0.0
+    x9:
+    - 0.0
+    - 0.0
+    - 0.0
+---
+
+# Model description
+
+This is a regression model on MPG dataset trained.
+
+## Intended uses & limitations
+
+This model is not ready to be used in production.
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter   | Value      |
+|------------------|------------|
+| copy_X           | True       |
+| fit_intercept    | True       |
+| n_jobs           |            |
+| normalize        | deprecated |
+| positive         | False      |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearRegression()</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">LinearRegression</label><div class="sk-toggleable__content"><pre>LinearRegression()</pre></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric             |    Value |
+|--------------------|----------|
+| Mean Squared Error | 5.01069  |
+| R-Squared          | 0.883503 |
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import joblib
+import json
+import pandas as pd
+clf = joblib.load(linreg.pkl)
+with open("config.json") as f:
+    config = json.load(f)
+clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
+```
+
+
+# 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 +1,76 @@
+{
+    "sklearn": {
+        "columns": [
+            "x0",
+            "x1",
+            "x2",
+            "x3",
+            "x4",
+            "x5",
+            "x6",
+            "x7",
+            "x8",
+            "x9"
+        ],
+        "environment": [
+            "scikit-learn==1.2.0",
+            "skops==0.3.0"
+        ],
+        "example_input": {
+            "x0": [
+                -0.3839236795902252,
+                -0.9788183569908142,
+                1.0937178134918213
+            ],
+            "x1": [
+                -0.5319488644599915,
+                -1.108436107635498,
+                0.9354732036590576
+            ],
+            "x2": [
+                -0.38279563188552856,
+                -1.3128694295883179,
+                1.4773520231246948
+            ],
+            "x3": [
+                0.2815782427787781,
+                -0.11783809214830399,
+                -0.9529813528060913
+            ],
+            "x4": [
+                1.0,
+                1.0,
+                0.0
+            ],
+            "x5": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "x6": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "x7": [
+                0.0,
+                0.0,
+                1.0
+            ],
+            "x8": [
+                0.0,
+                1.0,
+                0.0
+            ],
+            "x9": [
+                0.0,
+                0.0,
+                0.0
+            ]
+        },
+        "model": {
+            "file": "linreg.pkl"
+        },
+        "task": "tabular-regression"
+    }
+}

linreg.pkl

@@ -0,0 +1,3 @@
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+size 651