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

# xsum_123_3000_1500_test

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("KingKazma/xsum_123_3000_1500_test")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 9
* Number of training documents: 1500

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | yn - game - win - player - league | 12 | -1_yn_game_win_player | 
| 0 | said - mr - would - people - also | 142 | 0_said_mr_would_people | 
| 1 | right - box - win - foul - half | 1033 | 1_right_box_win_foul | 
| 2 | race - world - sport - champion - team | 118 | 2_race_world_sport_champion | 
| 3 | film - prize - album - book - said | 60 | 3_film_prize_album_book | 
| 4 | league - season - appearance - club - transfer | 49 | 4_league_season_appearance_club | 
| 5 | cricket - england - test - wicket - captain | 41 | 5_cricket_england_test_wicket | 
| 6 | wales - rugby - side - ospreys - team | 27 | 6_wales_rugby_side_ospreys | 
| 7 | egypt - morocco - cup - uganda - football | 18 | 7_egypt_morocco_cup_uganda |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* language: english
* 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

## Framework versions

* Numpy: 1.22.4
* HDBSCAN: 0.8.33
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.31.0
* Numba: 0.57.1
* Plotly: 5.13.1
* Python: 3.10.12