Package: nlp-preprocessing
nlp-preprocessingnlp-preprocessingprovides text preprocessing functions i.e. text cleaning, dataset preprocessing, tokenization etcInstallationpip install nlp_preprocessingTutorial1. Text Cleaningfrom nlp_preprocessing import clean

texts = ["Hi I am's nakdur"]
cleaned_texts = clean.clean_v1(texts)There are multiple cleaning functions:data_list = to_lower(data_list)
data_list = to_normalize(data_list)
data_list = remove_href(data_list)
data_list = remove_control_char(data_list)
data_list = remove_duplicate(data_list)
data_list = remove_underscore(data_list)
data_list = seperate_spam_chars(data_list)
data_list = seperate_brakets_quotes(data_list)
data_list = break_short_words(data_list)
data_list = break_long_words(data_list)
data_list = remove_ending_underscore(data_list)
data_list = remove_starting_underscore(data_list)
data_list = seperate_end_word_punctuations(data_list)
data_list = seperate_start_word_punctuations(data_list)
data_list = clean_contractions(data_list)
data_list = remove_s(data_list)
data_list = isolate_numbers(data_list)
data_list = regex_split_word(data_list)
data_list = leet_clean(data_list)
data_list = clean_open_holded_words(data_list)
data_list = clean_multiple_form(data_list)2. Dataset Preprationfrom nlp_preprocessing import dataset as ds
import pandas as pd

text = ['I am Test 1','I am Test 2']
label = ['A','B']
aspect = ['C','D']
data = pd.DataFrame({'text':text*5,'label':label*5,'aspect':aspect*5})
data

data_config = {
            'data_class':'multi-label',
            'x_columns':['text'],
            'y_columns':['label','aspect'],
            'one_hot_encoded_columns':[],
            'label_encoded_columns':['label','aspect'],
            'data':data,
            'split_ratio':0.1
          }

dataset = ds.Dataset(data_config)
train, test = dataset.get_train_test_data()

print(train['Y_train'],train['X_train'])
print(test['Y_test'],test['X_test'])
print(dataset.data_config)3.  Seq token generatortexts = ['I am Test 2', 'I am Test 1', 'I am Test 1', 'I am Test 1','I am Test 1', 'I am Test 2', 'I am Test 1', 'I am Test 2','I am Test 2']

tokens = seq_gen.get_word_sequences(texts)
print(tokens)4. Token embedding creatorfrom nlp_preprocessing import token_embedding_creator

vector_file='../input/fasttext-crawl-300d-2m-with-subword/crawl-300d-2m-subword/crawl-300d-2M-subword.vec'
input_file='../input/complete-tweet-sentiment-extraction-data/tweet_dataset.csv'
column_name='text'

processor = token_embedding_creator.Processor(vector_file, input_file, column_name)
output_dir = '.'
special_tokens = ['[UNK]','[SEP]']

processor.process(output_dir, special_tokens)

#Loading vectors from  ../input/fasttext-crawl-300d-2m-with-subword/crawl-300d-2m-subword/crawl-300d-2M-subword.vec  type:  index
#Writing vocab at  ./full_vocab.txt
#1%|          | 218/40000 [00:00<00:18, 2176.72it/s]
#Generating unique tokens ...
#100%|██████████| 40000/40000 [00:18<00:00, 2180.53it/s]
#Writing vocab at  ./vocab.txt
#Loading vectors from  ../input/fasttext-crawl-300d-2m-with-subword/crawl-300d-2m-subword/crawl-300d-2M-subword.vec  type:  embedding
#Writing vocab at  ./vocab.txt
#Making Final Embedding ...
#Writing embedding at  ./embeddings.npy
#Processing Done !
#Vocab stored at : ./vocab.txt  of size:  25475
#Embedding stored at : ./embeddings.npy of shape:  (25475, 300)5. seq_parser_token_generatorfrom nlp_preprocessing import seq_parser_token_generator

text = ['hi how are you']

pos_token, tag_token, dep_token = get_tokens(text[0])

pos_tokens, tag_tokens, dep_tokens = get_tokens_plus(text, 120)