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README.md
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- text-classification
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model_format: pickle
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model_file: legalis-scikit.pkl
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---
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# Model description
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## Intended uses & limitations
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##
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<details>
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<summary> Click to expand </summary>
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### Model Plot
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<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding:
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## Evaluation Results
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| accuracy | 0.664286 |
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| f1 score | 0.664286 |
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# How to Get Started with the Model
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[More Information Needed]
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# Model Card Authors
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This model card
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[More Information Needed]
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# Model Card Contact
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# Citation
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**BibTeX:**
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```
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[More Information Needed]
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```
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# get_started_code
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import pickle
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with open(dtc_pkl_filename, 'rb') as file:
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clf = pickle.load(file)
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# model_card_authors
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LennardZuendorf
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# limitations
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This model is not ready to be used in production.
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# model_description
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This is a RandomForestClassifier model trained a dataset of legal cases to predict the outcome as a binary value.
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# eval_method
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The model is evaluated using test split, on accuracy and F1 score with macro average.
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- text-classification
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model_format: pickle
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model_file: legalis-scikit.pkl
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datasets:
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- LennardZuendorf/legalis
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language:
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- de
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metrics:
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- accuracy
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- f1
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---
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# Model description
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This is a tuned random forest classifiert, trained on a processed dataset of 2800 German court cases (see [legalis dataset](https://huggingface.co/datasets/LennardZuendorf/legalis)). It predicts the winner (defended/"Verklagt*r" or plaintiff/"Kläger*in") of a court case based on facts provided (in German).
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## Intended uses & limitations
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- This model was created as part of a university project and should be considered highly experimental.
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## get started with the model
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Try out the hosted Interference UI or the [Huggingface Space](https://huggingface.co/spaces/LennardZuendorf/legalis)
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```
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import pickle
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with open(dtc_pkl_filename, 'rb') as file:
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clf = pickle.load(file)
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```
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### The modelHyperparameters
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- The Classifier was tuned with scikit's cv search method, the pipeline used a CountVectorizer with common German stopwords.
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<details>
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<summary> Click to expand </summary>
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### Model Plot
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<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 5;}#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=[('count',CountVectorizer(ngram_range=(1, 3),stop_words=['aber', 'alle', 'allem', 'allen','aller', 'alles', 'als', 'also','am', 'an', 'ander', 'andere','anderem', 'anderen', 'anderer','anderes', 'anderm', 'andern','anderr', 'anders', 'auch', 'auf','aus', 'bei', 'bin', 'bis', 'bist','da', 'damit', 'dann', ...])),('clf',RandomForestClassifier(min_samples_split=5, random_state=0))])</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=[('count',CountVectorizer(ngram_range=(1, 3),stop_words=['aber', 'alle', 'allem', 'allen','aller', 'alles', 'als', 'also','am', 'an', 'ander', 'andere','anderem', 'anderen', 'anderer','anderes', 'anderm', 'andern','anderr', 'anders', 'auch', 'auf','aus', 'bei', 'bin', 'bis', 'bist','da', 'damit', 'dann', ...])),('clf',RandomForestClassifier(min_samples_split=5, random_state=0))])</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(ngram_range=(1, 3),stop_words=['aber', 'alle', 'allem', 'allen', 'aller', 'alles','als', 'also', 'am', 'an', 'ander', 'andere','anderem', 'anderen', 'anderer', 'anderes','anderm', 'andern', 'anderr', 'anders', 'auch','auf', 'aus', 'bei', 'bin', 'bis', 'bist', 'da','damit', 'dann', ...])</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">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier(min_samples_split=5, random_state=0)</pre></div></div></div></div></div></div></div>
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## Evaluation Results
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| accuracy | 0.664286 |
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| f1 score | 0.664286 |
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# Model Card Authors
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This model card and the model itself are written by following authors:
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[@LennardZuendorf -HGF](https://huggingface.co/LennardZuendorf)
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[@LennardZuendorf - Github](https://github.com/LennardZuendorf)
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# Citation
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See Dataset for Sources and refer to [Github](https://github.com/LennardZuendorf/uniArchive-legalis) for collection of all files.
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