xsum_22457_3000_1500_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_22457_3000_1500_validation")
topic_model.get_topic_info()
Topic overview
- Number of topics: 26
- Number of training documents: 1500
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | said - people - would - one - year | 5 | -1_said_people_would_one |
| 0 | said - police - court - mr - heard | 646 | 0_said_police_court_mr |
| 1 | labour - party - mr - scotland - vote | 242 | 1_labour_party_mr_scotland |
| 2 | race - olympic - gold - team - medal | 56 | 2_race_olympic_gold_team |
| 3 | president - un - mr - south - said | 51 | 3_president_un_mr_south |
| 4 | united - foul - half - kick - win | 48 | 4_united_foul_half_kick |
| 5 | price - bank - rose - share - said | 44 | 5_price_bank_rose_share |
| 6 | attack - taliban - militant - killed - said | 41 | 6_attack_taliban_militant_killed |
| 7 | care - health - nhs - hospital - patient | 32 | 7_care_health_nhs_hospital |
| 8 | england - cricket - wicket - test - ball | 27 | 8_england_cricket_wicket_test |
| 9 | specie - tiger - bird - said - breeding | 27 | 9_specie_tiger_bird_said |
| 10 | rugby - wales - player - coach - world | 27 | 10_rugby_wales_player_coach |
| 11 | celtic - league - season - game - rangers | 26 | 11_celtic_league_season_game |
| 12 | album - music - song - show - singer | 26 | 12_album_music_song_show |
| 13 | open - round - world - play - american | 25 | 13_open_round_world_play |
| 14 | school - education - schools - said - child | 24 | 14_school_education_schools_said |
| 15 | film - best - actor - star - actress | 21 | 15_film_best_actor_star |
| 16 | eu - uk - brexit - trade - would | 21 | 16_eu_uk_brexit_trade |
| 17 | data - us - internet - said - information | 21 | 17_data_us_internet_said |
| 18 | league - transfer - season - club - appearance | 20 | 18_league_transfer_season_club |
| 19 | parking - council - said - road - ringgo | 19 | 19_parking_council_said_road |
| 20 | trump - mr - clinton - republican - president | 15 | 20_trump_mr_clinton_republican |
| 21 | water - supply - affected - flooding - customer | 12 | 21_water_supply_affected_flooding |
| 22 | fifa - corruption - scala - also - president | 12 | 22_fifa_corruption_scala_also |
| 23 | testimonial - match - tevez - united - player | 6 | 23_testimonial_match_tevez_united |
| 24 | hiv - outbreak - disease - kong - hong | 6 | 24_hiv_outbreak_disease_kong |
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
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