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

# xsum_55555_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_55555_3000_1500_test")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 26
* Number of training documents: 1500

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | said - mr - also - would - people | 5 | -1_said_mr_also_would | 
| 0 | police - said - mr - court - heard | 716 | 0_police_said_mr_court | 
| 1 | syria - turkey - syrian - military - said | 112 | 1_syria_turkey_syrian_military | 
| 2 | foul - win - kick - half - shot | 72 | 2_foul_win_kick_half | 
| 3 | growth - year - bank - business - economy | 68 | 3_growth_year_bank_business | 
| 4 | council - said - building - development - new | 63 | 4_council_said_building_development | 
| 5 | england - cricket - captain - test - wicket | 48 | 5_england_cricket_captain_test | 
| 6 | league - club - season - loan - transfer | 42 | 6_league_club_season_loan | 
| 7 | sport - gold - world - athlete - olympic | 38 | 7_sport_gold_world_athlete | 
| 8 | film - music - best - star - song | 36 | 8_film_music_best_star | 
| 9 | party - labour - mr - leader - said | 33 | 9_party_labour_mr_leader | 
| 10 | ireland - wales - leinster - rugby - player | 32 | 10_ireland_wales_leinster_rugby | 
| 11 | care - nhs - hospital - patient - said | 27 | 11_care_nhs_hospital_patient | 
| 12 | road - crash - police - collision - car | 26 | 12_road_crash_police_collision | 
| 13 | dog - animal - greyhound - racing - owner | 23 | 13_dog_animal_greyhound_racing | 
| 14 | ship - beach - said - lifeguard - rnli | 22 | 14_ship_beach_said_lifeguard | 
| 15 | school - education - child - council - said | 20 | 15_school_education_child_council | 
| 16 | wales - bill - welsh - labour - assembly | 19 | 16_wales_bill_welsh_labour | 
| 17 | eu - uk - european - europe - referendum | 18 | 17_eu_uk_european_europe | 
| 18 | fire - blaze - bus - flame - said | 18 | 18_fire_blaze_bus_flame | 
| 19 | mr - president - besigye - maduro - election | 16 | 19_mr_president_besigye_maduro | 
| 20 | race - froome - stage - second - lap | 13 | 20_race_froome_stage_second | 
| 21 | rail - train - rmt - scotrail - transport | 10 | 21_rail_train_rmt_scotrail | 
| 22 | planet - earth - electron - theory - mars | 10 | 22_planet_earth_electron_theory | 
| 23 | ryder - cup - tour - pga - mcilroy | 7 | 23_ryder_cup_tour_pga | 
| 24 | email - lazar - fbi - guccifer - ferizi | 6 | 24_email_lazar_fbi_guccifer |
  
</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