UrduNLP

In the last article, I have used the extractive method to get the summary of text. Now in this article I'm going to show you how you can get the abstractive summary using deep learning model based on transformers. Using Pre-trained model. There is a model which performs best as compared to other models available on `hugginface` site for Urdu language. Here is how can you use it to generate the abstractive summary. import re from transformers import AutoTokenizer, AutoModelForSeq2SeqLM WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip())) article_text = """ چھٹیوں کے دن ختم ہونے میں آخری دس دن باقی تھے اور میں ہمیشہ کی طرح کتابوں کا پہاڑ سامنے رکھ کر بیٹھ گئی تھی اور ہر بار کی طرح اس بار بھی میں نے چھٹیوں کے آغاز میں سوچا تھا کہ سارا کام پہلے ہی کر لوں گی،مگر ہر بار کی طرح اس بار بھی اس پر عمل نہیں کر سکی اور پھر وہی ہوا کہ آخری کے دس دنوں میں کتابوں کا پہاڑ سامنے رکھ کر بیٹھ گئی اور اپنی قسمت کو کوستی اور خو

 Text Summarization is an important task for large documents to get the idea of the document. There are two main summarization techniques used in NLP for text summarization. Extractive Text Summarization :  This approach's name is self-explanatory. Most important sentences or phrases are extracted from the original text and a short summary provided with these important sentences. See the figure for the explanation. Abstractive Text Summarization : This approach uses more advanced deep learning techniques to generate new sentences by learning from the original text. It is a complex task and requires heavy computing power such as GPU. Let's dive into the code for generating the text summary. I'm using Arabic as a parameter because the contributor did an excellent job of handling a lot of things like stemming, Urdu characters support, etc. from summa.summarizer import summarize text = """ اسلام آباد : صدر مملکت ڈاکٹر عارف علوی بھی کورونا وائرس کا شکار ہوگئے۔ سما

Question Answer Bot   The Question-Answer System is the latest trend in NLP.  There are currently two main techniques used for the Question-Answer system. 1 -  Open Domain: It is a wast land of NLP applications to build a QA system. A huge amount of data and text used to build such a system. I will write a blog post later about using the Open-Domain QA system. 2 - Closed Domain:  A closed domain question system is a narrow domain and strictly answers the questions which can be found in the domain. One example of a Closed Domain question system is a Knowledge-Based system. In this tutorial, I will explain the steps to build a Knowledge-Based QA system. Knowledge Base (KB) question answers are mostly used for FAQs. Where the user asks the questions and the model returns the best-matched answer based on the question. It's easy to implement and easy to integrate with chatbots and websites.  It is better to use the KB system for small datasets or narrow domains like questions about a c

News Classification is the latest buzz word in NLP for identifying the type of news and figuring out its a fake or not. There is a dataset available Urdu News extracted from web and has multiple classes and can be used for news classificaiton and other purposes. Preprocessing: News dataset is in multiple excel files, for the sake of classification, we need to convert it to single csv file. Here is how I did it import pandas as pd import glob files = glob.glob( "data/*.xlsx" ) df = pd.DataFrame() # if you want to use xlrd then 1.2.0 is good to go, openpyxl has a lot of issues. for file in files: excel_file = pd.read_excel(file , index_col= None , na_values=[ 'NA' ] , usecols=[ "category" , "summery" , "title" ] , engine= "xlrd" ) df = df.append(excel_file , ignore_index= True ) df.drop_duplicates(inplace= True ) # use single word for classification. df.category = df.category.str.replace( "weird ne

 This blog post is about the dataset and sentiment analysis of the products and services provided in Pakistan. It's the first step in sentiment analysis for manufacturing industry related reviews of people. It took me some time to build this dataset with the help of a few students.  We have used the following products and services provided by the company for analysis. Let's begin with the implementation of SVM for sentiment analysis. Import necessary packages. import re import pickle import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import precision_recall_fscore_support , accuracy_score from sklearn import svm Read the dataset and convert it to a list for further processing. raw_data = pd.read_csv( '../data/products_sentiment_urdu.csv' ) raw_data.head() # check the size of the data and its class d

Word cloud is a nice library for generating fun plots with names. Here is an example I've used to generate the cloud with my name. You need to install python-arabic-reshaper,    word cloud, matplotlib, python-bidi and numpy  libraries to generate the plots. You need to install the font  NotoNaskhArabic-Regular.ttf ( https://github.com/frappe/fonts/blob/master/usr_share_fonts/noto/NotoNaskhArabic-Regular.ttf ). This example is used in macOS. import numpy as np import matplotlib.pyplot as plt from wordcloud import WordCloud from bidi.algorithm import get_display from arabic_reshaper import ArabicReshaper configuration = { "language": "Urdu" } reshaper = ArabicReshaper(configuration=configuration) text = reshaper.reshape("عرفان") text = get_display(text) x, y = np.ogrid[:300, :300] mask = (x - 150) ** 2 + (y - 150) ** 2 > 130 ** 2 mask = 255 * mask.astype(int) wc = WordCloud(background_color="white",font_path='/Users/mirfan/Lib