Update README.md

README.md

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 ## Dataset Description
 
 - **Homepage:** 
-- **Repository:** 
-- **Paper:** 
+- **Repository:**
+- [Toxic-Comment-Detection-BN](https://github.com/imbodrulalam/Toxic-Comment-Detection-BN)  
+- **Paper:**
+- [Bangla Toxic Comment Classification and Severity Measure Using Deep Learning](https://www.researchgate.net/publication/368895245_Bangla_Toxic_Comment_Classification_and_Severity_Measure_Using_Deep_Learning)
 - **Leaderboard:** 
 - **Point of Contact:** 
 
 ### Dataset Summary
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+Since the deep learning approach needs a huge number
+of data for model training so it was a major challenge for
+us to collect a large amount of data to train our model.
+Some sample comments that we have collected are given
+below:
+ছাগেলর বাƐা ছাগল
+েদেখ পুড়াই িহজড়ার মেতা েদখেত
+পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
+We have collected almost 4141 labeled data from the
+previous work of Bangla toxic comment by Jubaer et al.
+[6], which are described in table 1. For more data, we have
+collected a total of 22, 000 comments have been collected
+from Tiktok, the majority of which are toxic comments.
+Our experts labeled these comments based on 6 categories
+that are not mutually exclusive. All the annotators are
+given clear guidelines on how to rate these comments.
+The guidelines can be summarized in Table I.
+![Alt text](Capture.PNG)
+The annotated comments are cleaned by removing
+emoticons, unnecessary punctuation marks, characters,
+digits, and other symbols as they contribute very little
+to the context of the comments.
 
 ### Supported Tasks and Leaderboards