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ISSR_Visual_Model

BERTopic
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ISSR_Visual_Model
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---
tags:
- bertopic
library_name: bertopic
---

# ISSR_Visual_Model

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 

To use this model, please install BERTopic:

```
pip install -U bertopic
```

You can use the model as follows:

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

topic_model.get_topic_info()
```

You can make predictions as follows:
```python
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

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | 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_ |
  
</details>

## 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