xsum_108_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_108_3000_1500_train")
topic_model.get_topic_info()
Topic overview
- Number of topics: 32
- Number of training documents: 3000
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | said - mr - people - would - also | 7 | -1_said_mr_people_would |
| 0 | win - game - right - goal - shot | 841 | 0_win_game_right_goal |
| 1 | police - said - court - mr - told | 815 | 1_police_said_court_mr |
| 2 | party - labour - mr - election - vote | 438 | 2_party_labour_mr_election |
| 3 | care - nhs - patient - health - cancer | 111 | 3_care_nhs_patient_health |
| 4 | rate - bank - growth - market - price | 77 | 4_rate_bank_growth_market |
| 5 | film - song - show - story - one | 76 | 5_film_song_show_story |
| 6 | school - education - student - teacher - child | 71 | 6_school_education_student_teacher |
| 7 | syria - syrian - said - killed - force | 46 | 7_syria_syrian_said_killed |
| 8 | trump - mr - clinton - russian - campaign | 45 | 8_trump_mr_clinton_russian |
| 9 | rescue - helicopter - ship - search - crew | 37 | 9_rescue_helicopter_ship_search |
| 10 | google - apple - mobile - said - company | 37 | 10_google_apple_mobile_said |
| 11 | fire - torch - building - burner - blaze | 35 | 11_fire_torch_building_burner |
| 12 | museum - coin - art - museums - work | 32 | 12_museum_coin_art_museums |
| 13 | rail - train - network - service - passenger | 32 | 13_rail_train_network_service |
| 14 | energy - gas - coal - fracking - industry | 26 | 14_energy_gas_coal_fracking |
| 15 | wales - welsh - assembly - uk - government | 25 | 15_wales_welsh_assembly_uk |
| 16 | facebook - company - social - said - site | 24 | 16_facebook_company_social_said |
| 17 | president - maduro - mr - macri - venezuelan | 23 | 17_president_maduro_mr_macri |
| 18 | president - mr - crocodile - boko - haram | 22 | 18_president_mr_crocodile_boko |
| 19 | union - strike - rmt - staff - said | 21 | 19_union_strike_rmt_staff |
| 20 | earthquake - quake - kathmandu - people - nepal | 20 | 20_earthquake_quake_kathmandu_people |
| 21 | migrant - asylum - le - pen - hungary | 18 | 21_migrant_asylum_le_pen |
| 22 | virus - disease - health - ebola - malaria | 18 | 22_virus_disease_health_ebola |
| 23 | cat - animal - rspca - dog - said | 17 | 23_cat_animal_rspca_dog |
| 24 | species - forest - frog - specie - tree | 16 | 24_species_forest_frog_specie |
| 25 | space - earth - surface - mars - mission | 15 | 25_space_earth_surface_mars |
| 26 | site - council - centre - pool - plan | 14 | 26_site_council_centre_pool |
| 27 | mr - gandhi - minister - indias - state | 13 | 27_mr_gandhi_minister_indias |
| 28 | plaque - memorial - died - war - akikusa | 12 | 28_plaque_memorial_died_war |
| 29 | korea - north - missile - china - us | 8 | 29_korea_north_missile_china |
| 30 | tax - rate - 50p - budget - chancellor | 8 | 30_tax_rate_50p_budget |
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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