Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
 license: apache-2.0
+datasets:
+- AyoubChLin/CNN_News_Articles_2011-2022
+language:
+- en
+tags:
+- topic modeling
+- BERT
+- CNN news articles
 ---
+# BERTopic Model for CNN News Articles
+
+This model is a BERTopic model fine-tuned on CNN news articles. It uses the sentence transformer model "all-MiniLM-L6-v2" to encode the sentences and UMAP for dimensionality reduction.
+
+## Usage
+
+First, install the required packages:
+
+```console
+pip install sentence_transformers umap-learn bertopic
+```
+
+``` python
+
+Then, load the model and encode your documents:
+
+```python
+from sentence_transformers import SentenceTransformer
+from umap import UMAP
+from bertopic import BERTopic
+
+# Load the sentence transformer model
+sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Set the random state in the UMAP model to prevent stochastic behavior 
+umap_model = UMAP(n_neighbors=15, n_components=5,  min_dist=0.0, metric='cosine', random_state=42)
+
+# Load the BERTopic model
+my_model = BERTopic.load("from/path/model.bin")
+
+# Encode your documents
+document_embeddings = sentence_model.encode(documents)
+```
+
+
+# predict :
+
+
+```python
+
+sentences = "my sentence"
+
+embeddings = sentence_model.encode([sentences])
+
+topic , _ =my_model.transform([sentences],embeddings)
+
+```
+
+
+For more information on how to use the BERTopic model, see the (BERTopic documentation)[https://maartengr.github.io/BERTopic/index.html].