Add challenge readme

Author: SalehTechLab

README.md

@@ -1 +1,48 @@
-# text-summarizer-with-python
+# Machine Learning Internship Challenge
+Create a text summarization system using transformers with Python.
+
+### Main Goal: 
+The system's goal is to quickly create short summaries that give all the important information from long articles / Paragraphs.
+
+### Technology / methodology :
+* Create a webpage using HTML/CSS where users can type a lot of text.
+* Write a program in Python that can summarize text using a library called Transformers.
+*  Instead of starting from scratch, use a model that's already been trained on a lot of text data. Example Models like BERT or GPT are really smart and can help us summarize text effectively.
+* Making use of API is a Plus point.
+
+### Requirement: 
+User Interface for Summarization: Develop a user interface (webpage with a form field) allowing users to input long-form text or documents.
+
+### Data Collection:
+Collect a dataset of long-form articles or documents across diverse topics for training and testing the summarization model.
+
+### Data Preprocessing:
+Preprocess the text data, including tokenization, removing unnecessary formatting, and handling special characters. (breaking text into words or subwords).
+
+### Train-Test Split:
+Divide the dataset into training and testing sets to evaluate the model's performance accurately.
+
+### Pre-trained Transformer Model:
+Choose a pre-trained transformer model, such as BERT or GPT, suitable for text summarization tasks.
+
+### Fine-tuning:
+Fine-tune the chosen pre-trained model on the training dataset using the summarization task objective, adjusting the model for the specific summarization context.
+
+
+### Integration with External Tools (Optional): 
+Integrate the summarization system with external tools or applications, allowing users to access summaries seamlessly within their preferred platforms.
+
+### Continuous Learning:
+Implement a mechanism to fine-tune the model periodically with new summarization data, adapting to evolving language patterns and improving summarization quality.
+
+### Natural Language Understanding (Optional):
+Enhance the summarization system with a natural language understanding module, using transformers for entity recognition or extracting key phrases.
+
+### Security and Privacy:
+Implement measures to secure user data and ensure privacy, especially when handling sensitive information within documents.
+
+### Testing and Evaluation:
+Test the summarization system with a variety of articles, assessing the quality of generated summaries. Evaluate the system's performance using metrics such as ROUGE scores for summarization tasks.
+
+### Deployment / Release:
+Deploy the text summarization system, making it accessible through an API or a web interface, allowing users to efficiently extract key information from lengthy documents using the power of transformer-based models.