urdu_topic_modeling
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("shaistaDev7/urdu_topic_modeling")
topic_model.get_topic_info()
Topic overview
- Number of topics: 5
- Number of training documents: 1008
Click here for an overview of all topics.
| 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 |
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
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