diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -5,8 +5,8 @@ tags:
- sklearn
- skops
- text-classification
-model_format: skops
-model_file: legalis-scikit.skops
+model_format: pickle
+model_file: legalis-scikit.pkl
---
# Model description
@@ -26,39 +26,61 @@ model_file: legalis-scikit.skops
Click to expand
-| Hyperparameter | Value |
-|--------------------------|---------|
-| bootstrap | True |
-| ccp_alpha | 0.0 |
-| class_weight | |
-| criterion | gini |
-| max_depth | |
-| max_features | sqrt |
-| max_leaf_nodes | |
-| max_samples | |
-| min_impurity_decrease | 0.0 |
-| min_samples_leaf | 1 |
-| min_samples_split | 5 |
-| min_weight_fraction_leaf | 0.0 |
-| n_estimators | 100 |
-| n_jobs | |
-| oob_score | False |
-| random_state | 0 |
-| verbose | 0 |
-| warm_start | False |
+| Hyperparameter | Value |
+|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| memory | |
+| 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))] |
+| verbose | False |
+| 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) |
+| count__analyzer | word |
+| count__binary | False |
+| count__decode_error | strict |
+| count__dtype |
RandomForestClassifier(min_samples_split=5, random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(min_samples_split=5, random_state=0)
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))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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))])
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', ...])
RandomForestClassifier(min_samples_split=5, random_state=0)