topic_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("KritiShahi/topic_model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 9
- Number of training documents: 597
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | echo - tap - speaker - alexa - amazon | 21 | Amazon Echo and Bluetooth Speakers |
| 0 | gift - love - great - bought - product | 227 | Gift ideas for loved ones |
| 1 | sound - great - speaker - music - good | 75 | Portable speakers |
| 2 | im - ive - bud - apple - well | 67 | Headphone Reviews |
| 3 | earbuds - magnet - design - ear - headphone | 60 | Earbuds and Noise Cancellation |
| 4 | remote - fire - work - phone - protector | 44 | Remote troubleshooting and solutions |
| 5 | headphone - work - like - earpods - around | 43 | Headphone Reviews |
| 6 | alexa - speaker - great - tap - button | 31 | Portable Alexa Speakers |
| 7 | bluetooth - connect - speaker - wifi - via | 29 | Portable Bluetooth Speakers |
Training hyperparameters
- calculate_probabilities: False
- language: None
- low_memory: False
- 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: 1.26.4
- HDBSCAN: 0.8.40
- UMAP: 0.5.7
- Pandas: 2.2.3
- Scikit-Learn: 1.6.1
- Sentence-transformers: 3.3.1
- Transformers: 4.48.0
- Numba: 0.60.0
- Plotly: 5.24.1
- Python: 3.9.1
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the model is not deployed on the HF Inference API.