xsum_6789_3000_1500_train
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_6789_3000_1500_train")
topic_model.get_topic_info()
Topic overview
- Number of topics: 16
- Number of training documents: 3000
<details> <summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | league - club - game - win - player | 5 | -1_league_club_game_win |
| 0 | said - mr - would - people - year | 382 | 0_said_mr_would_people |
| 1 | sport - medal - gold - team - olympic | 2143 | 1_sport_medal_gold_team |
| 2 | cricket - wicket - test - england - match | 72 | 2_cricket_wicket_test_england |
| 3 | arsenal - league - liverpool - chelsea - kick | 56 | 3_arsenal_league_liverpool_chelsea |
| 4 | world - open - round - mcilroy - golf | 55 | 4_world_open_round_mcilroy |
| 5 | foul - town - half - kick - win | 52 | 5_foul_town_half_kick |
| 6 | season - club - dedicated - transfer - appearance | 46 | 6_season_club_dedicated_transfer |
| 7 | celtic - game - aberdeen - rangers - player | 42 | 7_celtic_game_aberdeen_rangers |
| 8 | madrid - atltico - win - real - barcelona | 36 | 8_madrid_atltico_win_real |
| 9 | race - hamilton - team - prix - grand | 26 | 9_race_hamilton_team_prix |
| 10 | rugby - wales - game - coach - england | 22 | 10_rugby_wales_game_coach |
| 11 | fight - champion - boxing - amateur - world | 19 | 11_fight_champion_boxing_amateur |
| 12 | yn - wedi - ei - ar - bod | 17 | 12_yn_wedi_ei_ar |
| 13 | fan - club - bet - stadium - standing | 14 | 13_fan_club_bet_stadium |
| 14 | connacht - ronaldson - blade - penalty - ulster | 13 | 14_connacht_ronaldson_blade_penalty |
</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
