How to save custom taggers with pickle using nltk

This recipe helps you save custom taggers with pickle using nltk

Recipe Objective

This recipe explains how to save custom taggers with pickle using NLTK.

Step 1: Importing library

Let us first import the necessary libraries. We'll import Pickle, UnigramTagger, DefaultTagger, and treebank from nltk.tag and nltk.corpus respectively.

from nltk.tag import BigramTagger
from nltk.tag import DefaultTagger
from nltk.corpus import treebank
import pickle

Step 2: Pickle

We can use the pickle operation to serialize our machine learning algorithms and save the serialized format to a file. Training a tagger is very heavy and it takes time. To save time we have to pickle a trainer. In the example below, we are going to do this to a previously trained tagger named ‘Bi_tagger’.

train = treebank.tagged_sents()[:1000]
tag = DefaultTagger('NN')
Bi_tagger = BigramTagger(train, backoff = tag)
test = treebank.tagged_sents()[1000:]
Bi_tagger.evaluate(test)
Bi_tagger.evaluate(test)
g = open('Bi_tagger.pickle','wb')
pickle.dump(Bi_tagger, g)
g.close()
g = open('Bi_tagger.pickle','rb')
Bi_tagger = pickle.load(g)

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