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

# xsum_22457_3000_1500_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_22457_3000_1500_validation")

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 - people - would - one - year | 5 | -1_said_people_would_one | 
| 0 | said - police - court - mr - heard | 646 | 0_said_police_court_mr | 
| 1 | labour - party - mr - scotland - vote | 242 | 1_labour_party_mr_scotland | 
| 2 | race - olympic - gold - team - medal | 56 | 2_race_olympic_gold_team | 
| 3 | president - un - mr - south - said | 51 | 3_president_un_mr_south | 
| 4 | united - foul - half - kick - win | 48 | 4_united_foul_half_kick | 
| 5 | price - bank - rose - share - said | 44 | 5_price_bank_rose_share | 
| 6 | attack - taliban - militant - killed - said | 41 | 6_attack_taliban_militant_killed | 
| 7 | care - health - nhs - hospital - patient | 32 | 7_care_health_nhs_hospital | 
| 8 | england - cricket - wicket - test - ball | 27 | 8_england_cricket_wicket_test | 
| 9 | specie - tiger - bird - said - breeding | 27 | 9_specie_tiger_bird_said | 
| 10 | rugby - wales - player - coach - world | 27 | 10_rugby_wales_player_coach | 
| 11 | celtic - league - season - game - rangers | 26 | 11_celtic_league_season_game | 
| 12 | album - music - song - show - singer | 26 | 12_album_music_song_show | 
| 13 | open - round - world - play - american | 25 | 13_open_round_world_play | 
| 14 | school - education - schools - said - child | 24 | 14_school_education_schools_said | 
| 15 | film - best - actor - star - actress | 21 | 15_film_best_actor_star | 
| 16 | eu - uk - brexit - trade - would | 21 | 16_eu_uk_brexit_trade | 
| 17 | data - us - internet - said - information | 21 | 17_data_us_internet_said | 
| 18 | league - transfer - season - club - appearance | 20 | 18_league_transfer_season_club | 
| 19 | parking - council - said - road - ringgo | 19 | 19_parking_council_said_road | 
| 20 | trump - mr - clinton - republican - president | 15 | 20_trump_mr_clinton_republican | 
| 21 | water - supply - affected - flooding - customer | 12 | 21_water_supply_affected_flooding | 
| 22 | fifa - corruption - scala - also - president | 12 | 22_fifa_corruption_scala_also | 
| 23 | testimonial - match - tevez - united - player | 6 | 23_testimonial_match_tevez_united | 
| 24 | hiv - outbreak - disease - kong - hong | 6 | 24_hiv_outbreak_disease_kong |
  
</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
