def train_sgdc(training_list):
footnotes=[]
cate=[]
for i in training_list:
footnotes.append(i[0])
cate.append(i[1])
text_clf=Pipeline([('vect',CountVectorizer()),('tfidf',TfidfTransformer()),('clf',SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3,n_iter=5, random_state=42)),])
_ = text_clf.fit(footnotes,cate)
return text_clf
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