CoolFace
Modelpublic

merve/test-code-generation

sourceHugging Faceupdated 4y agoView on Hugging Face
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Model Card

Model description

[More Information Needed]

Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

<details> <summary> Click to expand </summary>

HyperparameterValue
ccp_alpha0.0
class_weight
criteriongini
max_depth
max_features
maxleafnodes
minimpuritydecrease0.0
minsamplesleaf1
minsamplessplit2
minweightfraction_leaf0.0
random_state
splitterbest

</details>

Model Plot

The model plot is below.

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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-e8a885f1-cbc4-4c9a-b46b-c755fe7af8fe div.sk-text-repr-fallback {display: none;}</style><div id="sk-e8a885f1-cbc4-4c9a-b46b-c755fe7af8fe" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</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-toggleablecontrol sk-hidden--visually" id="baa9fb41-2382-4981-824f-a69815f63fd3" type="checkbox" checked><label for="baa9fb41-2382-4981-824f-a69815f63fd3" class="sk-toggleablelabel sk-toggleablelabel-arrow">DecisionTreeClassifier</label><div class="sk-toggleable_content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>

Evaluation Results

You can find the details about evaluation process and the evaluation results.

MetricValue

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(example.pkl)
with open("config.json") as f:
    config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))

Model Card Authors

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Citation

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