cnn_dailymail_22457_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/cnn_dailymail_22457_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 | mccoy - jockey - ap - champion - winner | 15 | -1_mccoy_jockey_ap_champion |
| 0 | said - one - year - also - told | 9 | 0_said_one_year_also |
| 1 | league - season - player - goal - game | 994 | 1_league_season_player_goal |
| 2 | labour - mr - said - miliband - leader | 290 | 2_labour_mr_said_miliband |
| 3 | race - hamilton - rosberg - mercedes - marathon | 84 | 3_race_hamilton_rosberg_mercedes |
| 4 | england - cricket - test - pietersen - anderson | 32 | 4_england_cricket_test_pietersen |
| 5 | ncaa - first - game - college - basketball | 30 | 5_ncaa_first_game_college |
| 6 | masters - spieth - augusta - hole - round | 28 | 6_masters_spieth_augusta_hole |
| 7 | mayweather - fight - pacquiao - boxing - vegas | 18 | 7_mayweather_fight_pacquiao_boxing |
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: 0.56.4
- Plotly: 5.13.1
- Python: 3.10.6
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