metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
rag-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("3lv27/rag-topic-model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 5
- Number of training documents: 201
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | my - for - was - payment - it | 17 | -1_my_for_was_payment |
| 0 | refund - nike - my - store - for | 41 | 0_refund_nike_my_store |
| 1 | my - the - payment - app - balance | 72 | 1_my_the_payment_app |
| 2 | to - email - my - account - the | 37 | 2_to_email_my_account |
| 3 | card - klarna - details - to - do | 34 | 3_card_klarna_details_to |
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: False
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 2.0.2
- HDBSCAN: 0.8.40
- UMAP: 0.5.7
- Pandas: 2.2.3
- Scikit-Learn: 1.6.1
- Sentence-transformers: 3.1.1
- Transformers: 4.45.2
- Numba: 0.60.0
- Plotly: 6.0.0
- Python: 3.9.6