Update model

README.md

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_file: model.pkl
+widget:
+  structuredData:
+    word:
+    - lathem
+    - meer
+    - slaen
+---
+
+# Model description
+
+Middle Dutch NER with PassiveAgressiveClassifier
+
+## Intended uses & limitations
+
+This model is not ready to be used in production.
+
+## Training Procedure
+
+TESTING
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter            | Value                                                                |
+|---------------------------|----------------------------------------------------------------------|
+| memory                    |                                                                      |
+| steps                     | [('vectorizer', CountVectorizer()), ('classifier', MultinomialNB())] |
+| verbose                   | False                                                                |
+| vectorizer                | CountVectorizer()                                                    |
+| classifier                | MultinomialNB()                                                      |
+| vectorizer__analyzer      | word                                                                 |
+| vectorizer__binary        | False                                                                |
+| vectorizer__decode_error  | strict                                                               |
+| vectorizer__dtype         | <class 'numpy.int64'>                                                |
+| vectorizer__encoding      | utf-8                                                                |
+| vectorizer__input         | content                                                              |
+| vectorizer__lowercase     | True                                                                 |
+| vectorizer__max_df        | 1.0                                                                  |
+| vectorizer__max_features  |                                                                      |
+| vectorizer__min_df        | 1                                                                    |
+| vectorizer__ngram_range   | (1, 1)                                                               |
+| vectorizer__preprocessor  |                                                                      |
+| vectorizer__stop_words    |                                                                      |
+| vectorizer__strip_accents |                                                                      |
+| vectorizer__token_pattern | (?u)\b\w\w+\b                                                        |
+| vectorizer__tokenizer     |                                                                      |
+| vectorizer__vocabulary    |                                                                      |
+| classifier__alpha         | 1.0                                                                  |
+| classifier__class_prior   |                                                                      |
+| classifier__fit_prior     | True                                                                 |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;, CountVectorizer()),(&#x27;classifier&#x27;, MultinomialNB())])</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-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;, CountVectorizer()),(&#x27;classifier&#x27;, MultinomialNB())])</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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer()</pre></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-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+| Metric                  |    Value |
+|-------------------------|----------|
+| accuracy including 'O'  | 0.905322 |
+| f1 score including 'O   | 0.905322 |
+| precision excluding 'O' | 0.892857 |
+| recall excluding 'O'    | 0.404732 |
+| f1 excluding 'O'        | 0.556984 |
+
+### Confusion Matrix
+
+![Confusion Matrix](confusion_matrix.png)
+
+# How to Get Started with the Model
+
+[More Information Needed]
+
+# Model Card Authors
+
+Alassea TEST
+
+# Model Card Contact
+
+You can contact the model card authors through following channels:
+[More Information Needed]
+
+# Citation
+
+**BibTeX**
+
+```
+@inproceedings{...,year={2022}}
+```

config.json

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+{
+    "sklearn": {
+        "columns": [
+            "word"
+        ],
+        "environment": [
+            "scikit-learn=1.1.3"
+        ],
+        "example_input": {
+            "word": [
+                "lathem",
+                "meer",
+                "slaen"
+            ]
+        },
+        "model": {
+            "file": "model.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-classification"
+    }
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a75348eb1531248a0d2c352dee87b6cf592a5d22ba52d208589e7b694d499423
+size 836005