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

# xsum_22457_3000_1500_train

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_22457_3000_1500_train")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 45
* Number of training documents: 3000

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | said - mr - would - people - also | 6 | -1_said_mr_would_people | 
| 0 | win - kick - game - foul - united | 1243 | 0_win_kick_game_foul | 
| 1 | health - patient - nhs - hospital - cancer | 453 | 1_health_patient_nhs_hospital | 
| 2 | film - actor - music - song - star | 119 | 2_film_actor_music_song | 
| 3 | bank - business - share - market - sale | 83 | 3_bank_business_share_market | 
| 4 | police - northern - ireland - said - crime | 80 | 4_police_northern_ireland_said | 
| 5 | wicket - england - cricket - test - bowler | 67 | 5_wicket_england_cricket_test | 
| 6 | president - mr - election - government - farc | 67 | 6_president_mr_election_government | 
| 7 | labour - party - mr - election - corbyn | 62 | 7_labour_party_mr_election | 
| 8 | bird - specie - animal - zoo - dna | 58 | 8_bird_specie_animal_zoo | 
| 9 | school - education - student - teacher - schools | 48 | 9_school_education_student_teacher | 
| 10 | murder - court - mr - police - said | 42 | 10_murder_court_mr_police | 
| 11 | crash - police - road - died - collision | 42 | 11_crash_police_road_died | 
| 12 | rail - transport - said - passenger - train | 38 | 12_rail_transport_said_passenger | 
| 13 | facebook - console - broadband - game - company | 38 | 13_facebook_console_broadband_game | 
| 14 | lifeboat - rnli - water - sea - hms | 37 | 14_lifeboat_rnli_water_sea | 
| 15 | fire - blaze - said - cladding - building | 35 | 15_fire_blaze_said_cladding | 
| 16 | russia - syria - russian - syrian - military | 34 | 16_russia_syria_russian_syrian | 
| 17 | girl - child - abuse - court - sexual | 32 | 17_girl_child_abuse_court | 
| 18 | trump - mr - president - trumps - clinton | 29 | 18_trump_mr_president_trumps | 
| 19 | man - police - arrested - suspicion - hospital | 27 | 19_man_police_arrested_suspicion | 
| 20 | murray - tennis - djokovic - wimbledon - grand | 26 | 20_murray_tennis_djokovic_wimbledon | 
| 21 | medal - gold - olympic - games - world | 25 | 21_medal_gold_olympic_games | 
| 22 | india - indian - crop - modi - hindu | 24 | 22_india_indian_crop_modi | 
| 23 | birdie - open - round - golf - mcilroy | 23 | 23_birdie_open_round_golf | 
| 24 | earth - particle - space - moon - dark | 20 | 24_earth_particle_space_moon | 
| 25 | madrid - barcelona - foul - assisted - corner | 20 | 25_madrid_barcelona_foul_assisted | 
| 26 | eu - uk - brexit - european - would | 20 | 26_eu_uk_brexit_european | 
| 27 | athlete - doping - ioc - olympic - medal | 19 | 27_athlete_doping_ioc_olympic | 
| 28 | wales - welsh - government - waste - money | 18 | 28_wales_welsh_government_waste | 
| 29 | race - rosberg - hamilton - mercedes - engine | 16 | 29_race_rosberg_hamilton_mercedes | 
| 30 | plane - flight - mh370 - aircraft - airlines | 16 | 30_plane_flight_mh370_aircraft | 
| 31 | fight - pacquiao - mayweather - champion - whyte | 14 | 31_fight_pacquiao_mayweather_champion | 
| 32 | attack - us - security - bin - killed | 14 | 32_attack_us_security_bin | 
| 33 | virus - ebola - outbreak - disease - infected | 12 | 33_virus_ebola_outbreak_disease | 
| 34 | greece - migrant - eu - greek - crisis | 12 | 34_greece_migrant_eu_greek | 
| 35 | hie - farm - enterprise - energy - funicular | 12 | 35_hie_farm_enterprise_energy | 
| 36 | inflation - growth - rate - economist - manufacturing | 11 | 36_inflation_growth_rate_economist | 
| 37 | yn - ar - bod - ei - wedi | 11 | 37_yn_ar_bod_ei | 
| 38 | cup - group - sredojevic - al - mazembe | 11 | 38_cup_group_sredojevic_al | 
| 39 | picasso - picture - image - collection - cameron | 9 | 39_picasso_picture_image_collection | 
| 40 | froome - sky - tour - wiggins - team | 8 | 40_froome_sky_tour_wiggins | 
| 41 | carnival - event - pride - lgbt - notting | 7 | 41_carnival_event_pride_lgbt | 
| 42 | cocaine - corkindale - supply - connelly - drug | 6 | 42_cocaine_corkindale_supply_connelly | 
| 43 | meal - child - school - family - scheme | 6 | 43_meal_child_school_family |
  
</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
