metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
xsum_123_3000_1500_test
This is a 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:
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
Click here for an overview of all topics.
| 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 |
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