Sequence labeling for sentences and not tokens
I have sentences that belong to a paragraph. Each sentence has a label.
[s1,s2,s3,…], [l1,l2,l3,…]
I understand that I have to encode each sentence using an encoder, and then use sequence labeling. Could you guide me on how I could do that, combining them?

If i understand your question correctly, you are looking for encoding of your sentences into numeric representation.

let's say you have data like :

data = ["Sarah, is that you? Hahahahahaha  Todd give you another black eye??"
        "Well, being slick comes with the job of being a propagandist, Andi..."
        "Sad to lose a young person who was earnestly working for the common good and public safety when so many are in the basement smoking pot and playing computer games."]

labels = [0,1,0]


Now you want to build a classifier, for training classifier data should be in numeric format so here we will transfer text data into numeric structure for that we will use tf-idf  vectorizer which will create matrix for text data, then apply any algorithm.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline

vectorizerPipe = Pipeline([
                     ('tfidf', TfidfVectorizer(lowercase=True,stop_words='english')),
                     ('classification', LinearSVC(penalty='l2',loss='hinge'))])

trained_model = vectorizerPipe.fit(data,labels)


Here pipeline is constructed where first step is feature vector extraction (converting text data into numeric format) and in next step we are applying algorithm to it. There are lot of parameters in both steps you can try.
later we fir the pipeline with .fit method and passing data and labels.
