---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# topic_docs5000

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("Kamaljp/topic_docs5000")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 30
* Number of training documents: 5000

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | the - to - of - and - is | 12 | -1_the_to_of_and | 
| 0 | the - in - to - he - game | 1606 | 0_the_in_to_he | 
| 1 | the - drive - to - with - for | 450 | 1_the_drive_to_with | 
| 2 | the - to - that - of - and | 344 | 2_the_to_that_of | 
| 3 | the - of - and - in - to | 246 | 3_the_of_and_in | 
| 4 | of - to - the - is - and | 220 | 4_of_to_the_is | 
| 5 | the - car - and - it - for | 203 | 5_the_car_and_it | 
| 6 | the - of - that - to - is | 186 | 6_the_of_that_to | 
| 7 | call - three - bittrolff - uhhhh - test | 172 | 7_call_three_bittrolff_uhhhh | 
| 8 | the - to - be - of - key | 172 | 8_the_to_be_of | 
| 9 | the - space - of - and - to | 169 | 9_the_space_of_and | 
| 10 | the - openwindows - to - window - and | 169 | 10_the_openwindows_to_window | 
| 11 | for - and - 100 - to - the | 146 | 11_for_and_100_to | 
| 12 | windows - dos - the - and - to | 132 | 12_windows_dos_the_and | 
| 13 | the - bike - to - my - was | 105 | 13_the_bike_to_my | 
| 14 | you - that - to - of - your | 100 | 14_you_that_to_of | 
| 15 | for - and - to - mail - send | 100 | 15_for_and_to_mail | 
| 16 | to - that - homosexual - of - is | 94 | 16_to_that_homosexual_of | 
| 17 | is - that - objective - of - science | 66 | 17_is_that_objective_of | 
| 18 | printer - fonts - deskjet - hp - the | 56 | 18_printer_fonts_deskjet_hp | 
| 19 | jpeg - image - gif - file - format | 45 | 19_jpeg_image_gif_file | 
| 20 | points - graeme - polygon - the - lines | 44 | 20_points_graeme_polygon_the | 
| 21 | radar - detector - detectors - is - the | 28 | 21_radar_detector_detectors_is | 
| 22 | hotel - dj - for - ticket - price | 27 | 22_hotel_dj_for_ticket | 
| 23 | insurance - health - private - the - and | 26 | 23_insurance_health_private_the | 
| 24 | water - battery - temperature - the - discharge | 21 | 24_water_battery_temperature_the | 
| 25 | oil - paint - it - wax - and | 17 | 25_oil_paint_it_wax | 
| 26 | drugs - cocaine - lsd - drug - license | 16 | 26_drugs_cocaine_lsd_drug | 
| 27 | motif - toolkit - cosecomplient - api - mean | 15 | 27_motif_toolkit_cosecomplient_api | 
| 28 | maxaxaxaxaxaxaxaxaxaxaxaxaxaxax - entry - entries - rules - we | 13 | 28_maxaxaxaxaxaxaxaxaxaxaxaxaxaxax_entry_entries_rules |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: 30
* seed_topic_list: None
* top_n_words: 10
* verbose: True

## Framework versions

* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.30.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.12