Update readme

README.md

@@ -10,6 +10,12 @@ library_name: bertopic
 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. 
 
+This model was trained on 8000 images from Flickr **without** the captions. This demonstrates how BERTopic can be used for topic modeling using images as input only. 
+
+A few examples of generated topics:
+
+!["multimodal.png"](multimodal.png)
+
 ## Usage 
 
 To use this model, please install BERTopic:
@@ -27,6 +33,12 @@ topic_model = BERTopic.load("MaartenGr/BERTopic_Multimodal")
 topic_model.get_topic_info()
 ```
 
+You can view all information about a topic as follows:
+
+```python
+topic_model.get_topic(topic_id, full=True)
+```
+
 ## Topic overview
 
 * Number of topics: 29
@@ -69,6 +81,60 @@ topic_model.get_topic_info()
   
 </details>
 
+## Training Procedure
+
+The data was retrieved as follows:
+
+```python
+import os
+import glob
+import zipfile
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from sentence_transformers import util
+
+# Flickr 8k images
+img_folder = 'photos/'
+caps_folder = 'captions/'
+if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
+    os.makedirs(img_folder, exist_ok=True)
+
+    if not os.path.exists('Flickr8k_Dataset.zip'):   #Download dataset if does not exist
+        util.http_get('https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip', 'Flickr8k_Dataset.zip')
+        util.http_get('https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_text.zip', 'Flickr8k_text.zip')
+
+    for folder, file in [(img_folder, 'Flickr8k_Dataset.zip'), (caps_folder, 'Flickr8k_text.zip')]:
+        with zipfile.ZipFile(file, 'r') as zf:
+            for member in tqdm(zf.infolist(), desc='Extracting'):
+                zf.extract(member, folder)
+images = list(glob.glob('photos/Flicker8k_Dataset/*.jpg'))
+``` 
+
+Then, to perform topic modeling on multimodal data with BERTopic:
+
+```python
+from bertopic import BERTopic
+from bertopic.backend import MultiModalBackend
+from bertopic.representation import VisualRepresentation, KeyBERTInspired
+
+# Image embedding model
+embedding_model = MultiModalBackend('clip-ViT-B-32', batch_size=32)
+
+# Image to text representation model
+representation_model = {
+    "Visual_Aspect": VisualRepresentation(image_to_text_model="nlpconnect/vit-gpt2-image-captioning", image_squares=True),
+    "KeyBERT": KeyBERTInspired()
+}
+
+# Train our model with images only
+topic_model = BERTopic(representation_model=representation_model, verbose=True, embedding_model=embedding_model, min_topic_size=30)
+topics, probs = topic_model.fit_transform(documents=None, images=images)
+```
+
+The above demonstrates that the input were only images. These images are clustered and from those clusters a small subset of representative images are extracted. The representative images are captioned using `"nlpconnect/vit-gpt2-image-captioning"` to generate a small textual dataset over which we can run c-TF-IDF and the additional 
+`KeyBERTInspired` representation model. 
+
 ## Training hyperparameters
 
 * calculate_probabilities: False