4- Multilabel classification with OneVsOne Classifier

Is one multi-label classification possible with OneVsOneClassifier?
I made the classification of OneVsRestClassifier as follows:

Sample code:

model = preprocessing.MultiLabelBinarizer()

Y = model.fit_transform(y_train)

classifier = Pipeline([
    ('vectorizer',CountVectorizer()), 
    ('tfidf',TfidfTransformer()),
    ('clf',OneVsRestClassifier(SVC(kernel='linear')))
])

classifier.fit(X_train,Y)
predicted = classifier.predict(X_test)
labels = model.inverse_transform(predicted)

However, OneVsOneClassifier returns an error indicating an excessive number of labels.
Any help?

Can you clarify exactly what you are trying to do?
You haven’t used the OneVsOneClassifier anywhere so how are you getting an error for it?