xsum_108_5000000_2500000_validation
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_108_5000000_2500000_validation")
topic_model.get_topic_info()
Topic overview
- Number of topics: 9
- Number of training documents: 11332
<details> <summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | said - world - first - one - time | 41 | -1_said_world_first_one |
| 0 | said - mr - would - people - also | 813 | 0_said_mr_would_people |
| 1 | win - game - league - club - player | 7931 | 1_win_game_league_club |
| 2 | sport - olympic - race - gold - world | 2105 | 2_sport_olympic_race_gold |
| 3 | round - world - champion - open - golf | 219 | 3_round_world_champion_open |
| 4 | murray - match - tennis - set - number | 70 | 4_murray_match_tennis_set |
| 5 | race - hamilton - f1 - rosberg - mercedes | 60 | 5_race_hamilton_f1_rosberg |
| 6 | yn - ar - ei - yr - wedi | 50 | 6_yn_ar_ei_yr |
| 7 | fight - title - boxing - champion - im | 43 | 7_fight_title_boxing_champion |
</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
