nlp-preprocessing

Installation

pip install nlp_preprocessing

Tutorial

1. Text Cleaning

from nlp_preprocessing import clean
texts = ["Hi I am's nakdur"]
cleaned_texts = clean.clean_v1(texts)

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 Prepration

from 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 generator

texts = ['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 creator

from 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_generator

from 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)