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README.md

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----
-license: mit
-library_name: sklearn
-tags:
-- sklearn
-- skops
-- tabular-classification
-model_file: HME_pickle
-widget:
-  structuredData:
-    anger:
-    - 0.13340177
-    - 0.26429585
-    - 0.75805366
-    disgust:
-    - 0.07661828
-    - 0.14570697
-    - 0.21044387
-    fear:
-    - 0.094705686
-    - 0.057977196
-    - 0.003689876
-    joy:
-    - 0.006762238
-    - 0.2627153
-    - 0.001755206
-    neutral:
-    - 0.03295978
-    - 0.019884355
-    - 0.013996695
-    sadness:
-    - 0.6507381
-    - 0.24445744
-    - 0.011482558
-    surprise:
-    - 0.004814104
-    - 0.00496282
-    - 0.000578273
----
-
-# 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 |
-|------------------|---------|
-| alpha            |       1 |
-| class_prior      |         |
-| fit_prior        |       1 |
-| norm             |       0 |
-
-</details>
-
-### Model Plot
-
-The model plot is below.
-
-<style>#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 {color: black;background-color: white;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 pre{padding: 0;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-toggleable {background-color: white;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 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-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 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-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-item {z-index: 1;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-parallel-item:only-child::after {width: 0;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 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-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 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-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8d305fec-9b3e-4d4c-960e-2d386ab08acc" type="checkbox" checked><label for="8d305fec-9b3e-4d4c-960e-2d386ab08acc" class="sk-toggleable__label sk-toggleable__label-arrow">ComplementNB</label><div class="sk-toggleable__content"><pre>ComplementNB()</pre></div></div></div></div></div>
-
-## Evaluation Results
-
-You can find the details about evaluation process and the evaluation results.
-
-| Metric   |    Value |
-|----------|----------|
-| accuracy | 0.536424 |
-| f1 score | 0.536424 |
-
-# 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]
-```
-
-# citation_bibtex
-
-bibtex
-@inproceedings{...,year={2022}}
-
-# model_card_authors
-
-skops_user
-
-# limitations
-
-This model is purely for academic purposes.
-
-# model_description
-
-This is a Complement NB model trained on a poetry dataset.
-
-# eval_method
-
-The model is evaluated using test split, on accuracy and F1 score with macro average.