
# urdu_news_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_news_topic_modeling")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 7
* Number of training documents: 7991

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | پولیو - ڈاکٹر - مہم - بتایا - صحت | 1626 | 0_پولیو_ڈاکٹر_مہم_بتایا | 
| 1 | فلم - اداکارہ - اداکار - شادی - بالی | 1290 | 1_فلم_اداکارہ_اداکار_شادی | 
| 2 | عمران - خان - تحریک - حکومت - لیگ | 1263 | 2_عمران_خان_تحریک_حکومت | 
| 3 | روپے - مالی - ملین - سال - ڈالر | 1062 | 3_روپے_مالی_ملین_سال | 
| 4 | فون - صارفین - ویوو - موبائل - بک | 928 | 4_فون_صارفین_ویوو_موبائل | 
| 5 | ٹیم - میچ - کرکٹ - رنز - ٹورنامنٹ | 916 | 5_ٹیم_میچ_کرکٹ_رنز | 
| 6 | کورونا - وائرس - کیسز - مریض - ہزار | 906 | 6_کورونا_وائرس_کیسز_مریض |
  
</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: True
* 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