legalis-scikit

LennardZuendorf/legalis-scikit

legalis-scikit is a text classification model from LennardZuendorf. Use it when you need a label for a piece of text. It is set up for sklearn. The card lists the license as mit.

This is a tuned random forest classifiert, trained on a processed dataset of 2800 German court cases (see legalis dataset). It predicts the winner (defended/"Verklagtr" or plaintiff/"Klägerin") of a court case based o…

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Public

Updated Oct 7, 2023

Repo size

91.7 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.pkl83.5 MB · 91%

At a glance

Task: Text Classification
Library: sklearn
License: mit
Access: Public
Created: Oct 7, 2023
Updated: Oct 7, 2023
SHA: 278b2362

Datasets

LennardZuendorf/legalis

Spaces

LennardZuendorf/legalis

Model card

From the Hugging Face model README

Model description

Intended uses & limitations

This model was created as part of a university project and should be considered highly experimental.

get started with the model

Try out the hosted Interference UI or the Huggingface Space

import pickle 
with open(dtc_pkl_filename, 'rb') as file: 
    clf = pickle.load(file)

The modelHyperparameters

The Classifier was tuned with scikit's cv search method, the pipeline used a CountVectorizer with common German stopwords.

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

Hyperparameter	Value
memory
steps	[('count', CountVectorizer(ngram_range=(1, 3),<br /> stop_words=['aber', 'alle', 'allem', 'allen', 'aller', 'alles',<br /> 'als', 'also', 'am', 'an', 'ander', 'andere',<br /> 'anderem', 'anderen', 'anderer', 'anderes',<br /> 'anderm', 'andern', 'anderr', 'anders', 'auch',<br /> 'auf', 'aus', 'bei', 'bin', 'bis', 'bist', 'da',<br /> 'damit', 'dann', ...])), ('clf', RandomForestClassifier(min_samples_split=5, random_state=0))]
verbose	False
count	CountVectorizer(ngram_range=(1, 3),<br /> stop_words=['aber', 'alle', 'allem', 'allen', 'aller', 'alles',<br /> 'als', 'also', 'am', 'an', 'ander', 'andere',<br /> 'anderem', 'anderen', 'anderer', 'anderes',<br /> 'anderm', 'andern', 'anderr', 'anders', 'auch',<br /> 'auf', 'aus', 'bei', 'bin', 'bis', 'bist', 'da',<br /> 'damit', 'dann', ...])
clf	RandomForestClassifier(min_samples_split=5, random_state=0)
count__analyzer	word
count__binary	False
count__decode_error	strict
count__dtype	<class 'numpy.int64'>
count__encoding	utf-8
count__input	content
count__lowercase	True
count__max_df	1.0
count__max_features
count__min_df	1
count__ngram_range	(1, 3)
count__preprocessor
count__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', 'der', 'den', 'des', 'dem', 'die', 'das', 'dass', 'daß', 'derselbe', 'derselben', 'denselben', 'desselben', 'demselben', 'dieselbe', 'dieselben', 'dasselbe', 'dazu', 'dein', 'deine', 'deinem', 'deinen', 'deiner', 'deines', 'denn', 'derer', 'dessen', 'dich', 'dir', 'du', 'dies', 'diese', 'diesem', 'diesen', 'dieser', 'dieses', 'doch', 'dort', 'durch', 'ein', 'eine', 'einem', 'einen', 'einer', 'eines', 'einig', 'einige', 'einigem', 'einigen', 'einiger', 'einiges', 'einmal', 'er', 'ihn', 'ihm', 'es', 'etwas', 'euer', 'eure', 'eurem', 'euren', 'eurer', 'eures', 'für', 'gegen', 'gewesen', 'hab', 'habe', 'haben', 'hat', 'hatte', 'hatten', 'hier', 'hin', 'hinter', 'ich', 'mich', 'mir', 'ihr', 'ihre', 'ihrem', 'ihren', 'ihrer', 'ihres', 'euch', 'im', 'in', 'indem', 'ins', 'ist', 'jede', 'jedem', 'jeden', 'jeder', 'jedes', 'jene', 'jenem', 'jenen', 'jener', 'jenes', 'jetzt', 'kann', 'kein', 'keine', 'keinem', 'keinen', 'keiner', 'keines', 'können', 'könnte', 'machen', 'man', 'manche', 'manchem', 'manchen', 'mancher', 'manches', 'mein', 'meine', 'meinem', 'meinen', 'meiner', 'meines', 'mit', 'muss', 'musste', 'nach', 'nicht', 'nichts', 'noch', 'nun', 'nur', 'ob', 'oder', 'ohne', 'sehr', 'sein', 'seine', 'seinem', 'seinen', 'seiner', 'seines', 'selbst', 'sich', 'sie', 'ihnen', 'sind', 'so', 'solche', 'solchem', 'solchen', 'solcher', 'solches', 'soll', 'sollte', 'sondern', 'sonst', 'über', 'um', 'und', 'uns', 'unsere', 'unserem', 'unseren', 'unser', 'unseres', 'unter', 'viel', 'vom', 'von', 'vor', 'während', 'war', 'waren', 'warst', 'was', 'weg', 'weil', 'weiter', 'welche', 'welchem', 'welchen', 'welcher', 'welches', 'wenn', 'werde', 'werden', 'wie', 'wieder', 'will', 'wir', 'wird', 'wirst', 'wo', 'wollen', 'wollte', 'würde', 'würden', 'zu', 'zum', 'zur', 'zwar', 'zwischen']
count__strip_accents
count__token_pattern	(?u)\b\w\w+\b
count__tokenizer
count__vocabulary
clf__bootstrap	True
clf__ccp_alpha	0.0
clf__class_weight
clf__criterion	gini
clf__max_depth
clf__max_features	sqrt
clf__max_leaf_nodes
clf__max_samples
clf__min_impurity_decrease	0.0
clf__min_samples_leaf	1
clf__min_samples_split	5
clf__min_weight_fraction_leaf	0.0
clf__n_estimators	100
clf__n_jobs
clf__oob_score	False
clf__random_state	0
clf__verbose	0
clf__warm_start	False

</details>

Model Plot

Evaluation Results

Metric	Value
accuracy	0.664286
f1 score	0.664286

Model Card Authors

This model card and the model itself are written by following authors:

@LennardZuendorf -HGF @LennardZuendorf - Github

Citation

See Dataset for Sources and refer to Github for collection of all files.

Task: Text Classification
Library: sklearn
License: mit
Languages: de
Created: Oct 7, 2023
Updated: Oct 7, 2023