Update README.md

README.md

@@ -40,9 +40,10 @@ pipeline_tag: text-classification
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# bert-finetuned-phishing
+# BERT FINETUNED ON PHISHING DETECTION
 
-This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an [phishing dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset). 
+This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an [phishing dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset),
+capable of detecting phishing in its four most common forms: URLs, Emails, SMS messages and even websites. 
 
 It achieves the following results on the evaluation set:
 
@@ -57,21 +58,7 @@ It achieves the following results on the evaluation set:
 BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 
 This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why 
 it can use lots of publicly available data) with an automatic process to generate inputs and labels from 
-those texts. More precisely, it was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input
-  then run the entire masked sentence through the model and has to predict the masked words. This is different
-  from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from
-  autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a
-  bidirectional representation of the sentence.
-  
-- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining.
-  Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The
-  model then has to predict if the two sentences were following each other or not.
-  
-This way, the model learns an inner representation of the English language that can then be used to extract 
-features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a 
-standard classifier using the features produced by the BERT model as inputs.
+those texts.
 
 This model has the following configuration:
 
@@ -80,15 +67,13 @@ This model has the following configuration:
 - 16 attention heads
 - 336M parameters
 
-## Intended uses & limitations
+## Motivation and Purpose
 
-This is a BERT model finely tuned for phishing detection in text entries.
-
-## Training and evaluation data
-
-This model was finely tuned on a phishing dataset containing samples of: URLs, SMS messages, mail messages, 
-and HTML code. This sample variability broadens the detection range of the model and allows it to be used in 
-various contexts.
+Phishing is one of the most frequent and most expensive cyber-attacks according to several security reports. 
+This model aims to efficiently and accurately prevent phishing attacks against individuals and organizations. 
+To achieve it, BERT was trained on a diverse and robust dataset containing: URLs, SMS Messages, Emails and 
+Websites, which allows the model to extend its detection capability beyond the usual and to be used in various 
+contexts.
 
 ### Training hyperparameters