ISSR_Visual_Model

This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.

Usage

To use this model, please install BERTopic:

pip install -U bertopic

You can use the model as follows:

from bertopic import BERTopic
topic_model = BERTopic.load("D0men1c0/ISSR_Visual_Model")

topic_model.get_topic_info()

You can make predictions as follows:

val_labels = [...] # list of caption
val_images = [...] # list of images

topic, _ = topic_model.transform(val_labels, images=val_images)
all_topic_info = [topic_model.get_topic_info(t) for t in topic]
all_prediction_info = pd.concat(all_topic_info, ignore_index=True)

# Visualize predictions:
sample_images = 100
n_images = min(sample_images, len(val_images))
n_cols = 4
n_rows = math.ceil(n_images / n_cols)

fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, n_rows * 3))
axes = axes.flatten()

for i, (path, (_, row)) in enumerate(zip(val_images[:n_images], all_prediction_info.iterrows())):
    ax = axes[i]
    ax.imshow(Image.open(path))
    ax.axis('off')
    ax.set_title(f"Topic {row['Topic']}: {row['KeyBERTInspired'][0]}")

# Hide unused axes
for j in range(n_images, len(axes)):
    axes[j].axis('off')

plt.tight_layout()
plt.show()

Topic overview

• Number of topics: 5
• Number of training documents: 2997

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	drug - people - gun - -	151	-1_drug_people_gun_
0	gun - people - drug - -	2152	0_gun_people_drug_
1	drug - gun - - -	342	1_drug_gun__
2	people - gun - - -	287	2_people_gun__
3	people - gun - drug - -	65	3_people_gun_drug_

Training hyperparameters

• calculate_probabilities: False
• language: None
• low_memory: False
• min_topic_size: 50
• n_gram_range: (1, 3)
• nr_topics: None
• seed_topic_list: None
• top_n_words: 5
• verbose: True
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 1.26.4
• HDBSCAN: 0.8.36
• UMAP: 0.5.6
• Pandas: 2.2.2
• Scikit-Learn: 1.4.1.post1
• Sentence-transformers: 3.0.1
• Transformers: 4.39.3
• Numba: 0.60.0
• Plotly: 5.22.0
• Python: 3.12.4