|
|
|
--- |
|
tags: |
|
- bertopic |
|
library_name: bertopic |
|
pipeline_tag: text-classification |
|
--- |
|
|
|
# rag-topic-model |
|
|
|
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("ivanleomk/rag-topic-model") |
|
|
|
topic_model.get_topic_info() |
|
``` |
|
|
|
## Topic overview |
|
|
|
* Number of topics: 5 |
|
* Number of training documents: 243 |
|
|
|
<details> |
|
<summary>Click here for an overview of all topics.</summary> |
|
|
|
| Topic ID | Topic Keywords | Topic Frequency | Label | |
|
|----------|----------------|-----------------|-------| |
|
| -1 | the - verification - my - for - code | 24 | -1_the_verification_my_for | |
|
| 0 | klarna - to - my - and - the | 19 | 0_klarna_to_my_and | |
|
| 1 | my - the - return - store - still | 98 | 1_my_the_return_store | |
|
| 2 | card - onetime - my - it - for | 69 | 2_card_onetime_my_it | |
|
| 3 | payment - my - due - date - the | 33 | 3_payment_my_due_date | |
|
|
|
</details> |
|
|
|
## 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: 1.5.2 |
|
* Sentence-transformers: 3.3.1 |
|
* Transformers: 4.46.3 |
|
* Numba: 0.60.0 |
|
* Plotly: 5.24.1 |
|
* Python: 3.9.6 |
|
|
