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topic_modelling_football
riccardopresti99/topic_modelling_football
topic_modelling_football is a text classification model from riccardopresti99. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Jul 5, 2023
- Updated
- Jul 5, 2023
- SHA
- f61e45e5
Try a prompt
Model card
From the Hugging Face model README
topic_modelling_football
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("riccardopresti99/topic_modelling_football")
topic_model.get_topic_info()
Topic overview
- Number of topics: 14
- Number of training documents: 350
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | tournament - competition - leaving - final - compete | 16 | -1_tournament_competition_leaving_final |
| 0 | video - games - football - players - experience | 10 | 0_video_games_football_players |
| 1 | supporters - atmosphere - stadiums - football - create | 48 | 1_supporters_atmosphere_stadiums_football |
| 2 | physiotherapists - injury - injuries - players - prevention | 32 | 2_physiotherapists_injury_injuries_players |
| 3 | united - film - football - war - story | 29 | 3_united_film_football_war |
| 4 | ronaldo - ability - scoring - aspiring - one | 26 | 4_ronaldo_ability_scoring_aspiring |
| 5 | scandals - illegal - officials - within - concerns | 26 | 5_scandals_illegal_officials_within |
| 6 | healthy - footballers - energy - supports - performance | 25 | 6_healthy_footballers_energy_supports |
| 7 | strikers - striker - scoring - teammates - goals | 25 | 7_strikers_striker_scoring_teammates |
| 8 | investors - stock - stocks - market - club | 25 | 8_investors_stock_stocks_market |
| 9 | women - football - girls - equal - sport | 25 | 9_women_football_girls_equal |
| 10 | serie - milan - league - inter - italian | 23 | 10_serie_milan_league_inter |
| 11 | champions - league - european - club - uefa | 22 | 11_champions_league_european_club |
| 12 | cup - world - fifa - held - trophy | 18 | 12_cup_world_fifa_held |
Training hyperparameters
- calculate_probabilities: False
- language: None
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 10
- verbose: True
Framework versions
- Numpy: 1.23.5
- HDBSCAN: 0.8.29
- UMAP: 0.5.3
- Pandas: 1.5.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 2.2.2
- Transformers: 4.26.1
- Numba: 0.56.4
- Plotly: 5.13.1
- Python: 3.10.10
- Task
- Text Classification
- Library
- bertopic
- Created
- Jul 5, 2023
- Updated
- Jul 5, 2023