Update README.md

README.md

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 ---
-license: mit
+license: apache-2.0
 task_categories:
 - text-classification
 language:
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 - url
 - html
 - text
----
+---
+# Phishing Dataset
+
+Phishing dataset compiled from various resources for classification and phishing detection tasks.
+
+## Dataset Details
+
+The dataset has two columns: `text` and `label`. Text field contains samples of:
+
+- URL
+- SMS messages
+- Email messages
+- HTML code
+
+Which are labeled as **1 (Phishing)** or **0(Benign)**.
+
+### Source Data
+
+This dataset is a compilation of 4 sources, which are described below:
+
+- [Mail dataset](https://www.kaggle.com/datasets/subhajournal/phishingemails) that specifies the body text of various emails that can be used to detect phishing emails,
+  through extensive text analysis and classification with machine learning. Contains over 18,000 emails
+  generated by Enron Corporation employees.
+  
+- [SMS message dataset](https://data.mendeley.com/datasets/f45bkkt8pr/1) of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages
+  and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used
+  to classify messages as malicious or legitimate. The data was collected by converting images obtained from
+  the Internet into text using Python code.
+
+- [URL dataset](https://www.kaggle.com/datasets/harisudhan411/phishing-and-legitimate-urls) with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are
+  phishing domains. It is a collection of data samples from various sources, the URLs were collected from the
+  JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and
+  some open source databases, including Excel files.
+
+- [Website dataset](https://data.mendeley.com/datasets/n96ncsr5g4/1) of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each
+  instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple
+  keyword search on the Google search engine was used and the first 5 URLs of each search were collected.
+  Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse
+  collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset
+  repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo.
+
+#### Dataset Processing
+
+Primarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has 
+not been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification. 
+
+_Is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_
+
+In general, **NO**. Preprocessing will not change the output predictions. In fact, removing empty words (which 
+are considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will
+worsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these "stop words" 
+are valuable information for BERT. The same goes for punctuation: a question mark can certainly change the 
+overall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean 
+eliminating the context that BERT could have used to get better results.
+
+However, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should 
+be considered. That is at the discretion of whoever wishes to employ this dataset. 
+
+For more information check these links:
+
+- https://stackoverflow.com/a/70700145
+- https://datascience.stackexchange.com/a/113366