Update README.md

README.md

@@ -6,10 +6,21 @@ library_name: bertopic
 pipeline_tag: text-classification
 ---
 
-# BERTopic_ArXiv
+# -BERTopic_Arab_news
+
+A modular implementation of BERTopic for topic modeling, specifically trained on `Arabic news articles`. This implementation allows for flexible component selection at each layer of the topic modeling pipeline.
+
+![image](https://github.com/user-attachments/assets/0bdfdba4-2f76-4857-8467-51cc8018bed0)
+
+
+## The core of this project is BERTopic, which is used to perform topic modeling on the processed text. The following steps are performed:
+
+#### - Topic Modeling: BERTopic is trained on the cleaned dataset to identify topics in the articles.
+#### - Fine-tuning with KeyBERT: We use KeyBERT-inspired representations to improve the clarity and interpretability of the topics.
+#### - Topic Extraction: The most frequent topics are extracted, and each document is assigned a topic.
+#### - Topic Updates: The model can be fine-tuned by updating topics with n-grams for more domain-specific phrases.
+
 
-This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. 
-BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. 
 
 ## Usage 
 
@@ -177,3 +188,11 @@ Source: [Hugging Face dataset page](https://huggingface.co/datasets/inparallel/s
 * Numba: 0.60.0
 * Plotly: 5.24.1
 * Python: 3.10.12
+
+
+
+
+# Visualization: Displays topic distribution and document-level information
+
+
+![image](https://github.com/user-attachments/assets/96ab4297-c8cc-4227-9164-eb129d45bffc)