
# urdu_topic_modeling

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("shaistaDev7/urdu_topic_modeling")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 5
* Number of training documents: 1008

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | کینسر - استعمال - جسم - علاج - افراد | 315 | 0_کینسر_استعمال_جسم_علاج | 
| 1 | ٹیم - کرکٹ - محمد - میڈل - انگلینڈ | 240 | 1_ٹیم_کرکٹ_محمد_میڈل | 
| 2 | روپے - ارب - فیصد - ٹیکس - حکومت | 238 | 2_روپے_ارب_فیصد_ٹیکس | 
| 3 | فلم - خان - ووڈ - بالی - اداکارہ | 205 | 3_فلم_خان_ووڈ_بالی | 
| 4 | ظفر - میشا - شفیع - علی - جنسی | 10 | 4_ظفر_میشا_شفیع_علی |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* language: urdu
* low_memory: True
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.2
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12

{"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"}
bertopic text-classification region:us