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

# xsum_108_5000000_2500000_validation

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_108_5000000_2500000_validation")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 9
* Number of training documents: 11332

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | said - world - first - one - time | 41 | -1_said_world_first_one | 
| 0 | said - mr - would - people - also | 813 | 0_said_mr_would_people | 
| 1 | win - game - league - club - player | 7931 | 1_win_game_league_club | 
| 2 | sport - olympic - race - gold - world | 2105 | 2_sport_olympic_race_gold | 
| 3 | round - world - champion - open - golf | 219 | 3_round_world_champion_open | 
| 4 | murray - match - tennis - set - number | 70 | 4_murray_match_tennis_set | 
| 5 | race - hamilton - f1 - rosberg - mercedes | 60 | 5_race_hamilton_f1_rosberg | 
| 6 | yn - ar - ei - yr - wedi | 50 | 6_yn_ar_ei_yr | 
| 7 | fight - title - boxing - champion - im | 43 | 7_fight_title_boxing_champion |
  
</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