Как эффективно подсчитывать биграммы по нескольким документам в Python

Вы можете попробовать следующее:

from collections import Counter
from nltk import word_tokenize 
from nltk.util import ngrams
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords

stop_words = set(stopwords.words('english'))

doc_1 = 'Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter'
doc_2 = 'Convolutional Neural Networks take advantage of the fact that the input consists of images and they constrain the architecture in a more sensible way.'
doc_3 = 'In particular, unlike a regular Neural Network, the layers of a ConvNet have neurons arranged in 3 dimensions: width, height, depth.'
docs = [doc_1, doc_2, doc_3]
docs = (' '.join(filter(None, docs))).lower()

tokens = word_tokenize(docs)
tokens = [t for t in tokens if t not in stop_words]
word_l = WordNetLemmatizer()
tokens = [word_l.lemmatize(t) for t in tokens if t.isalpha()]

bi_grams = list(ngrams(tokens, 2)) 
counter = Counter(bi_grams)
counter.most_common(5)

Out[82]: 
[(('neural', 'network'), 4),
 (('convolutional', 'neural'), 2),
 (('network', 'similar'), 1),
 (('similar', 'ordinary'), 1),
 (('ordinary', 'neural'), 1)]