config-example.toml

version = "1.9"
debug = false

[path]
input = "data/input"
output = "data/output"

[nlp]
language = "de" # de, en
embeddings = "spacy"
nltk_model = "german" # english, german
spacy_model = "de_core_news_md" # en_core_web_lg, de_core_news_md

[flask]
name = "ReCAP-Argument-Mining"
host = "0.0.0.0"
port = 80

[export]
picture = true
json = true

[evaluation]
similarity = "edit"

[relation]
en_model = "data/relation/logreg_en.sav"
de_model = "data/relation/logreg_de.sav"

method = "adu_position" # adu_position, flat_tree, pairwise_comparison
threshold = 0.6
fallback = "support" # none, support, attack

[adu]
#["RandomForest","SVC","LogisticRegression","AdaBoost","XGBoost","XGBRF","AutoML","Stacking"]
#["standard","GridSearch", "RandomSearch"]
model = "Stacking"
train_method = "RandomSearch"
n_splits = 3

[adu.MC]
method = "pairwise" # pairwise, centroid, first, relations

[adu.path]
input = "data/ADU/in/PE/en"
output = "data/out/out.txt"
label = "data/ADU/in/PE/en"

[adu.stacking]
estimator_stack = ["rf","ada","log"]
final_estimator = "xgbrf"
passthrough = true

[adu.auxiliary]
dir = "data/ADU/auxiliary"
vocab = "vocab.pkl"
tfidf = "TF-IDF.pkl"
freqdist = "FreqDist.pkl"

[adu.feature_selection]
#["VT","scale","PCA","select_kbest","PCA_selectk_union"]
methods = ["VT","scale"]
dir =  "data/ADU/feature_selection"
scaler = "scaler"
#["Robust","Power","MinMax","Standard","QuantileUniform","QuantileGaussian"]
scaling_method = "Robust"