metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
cnn_dailymail_55555_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/cnn_dailymail_55555_3000_1500_train")
topic_model.get_topic_info()
Topic overview
- Number of topics: 61
- Number of training documents: 3000
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | said - one - year - people - mr | 10 | -1_said_one_year_people |
| 0 | league - game - player - cup - goal | 961 | 0_league_game_player_cup |
| 1 | police - death - said - murder - family | 313 | 1_police_death_said_murder |
| 2 | obama - republican - senate - president - republicans | 182 | 2_obama_republican_senate_president |
| 3 | fashion - hair - look - makeup - brand | 91 | 3_fashion_hair_look_makeup |
| 4 | dog - animal - cat - bird - pet | 69 | 4_dog_animal_cat_bird |
| 5 | syria - isis - syrian - iraq - fighter | 54 | 5_syria_isis_syrian_iraq |
| 6 | mexico - said - cuba - president - cartel | 53 | 6_mexico_said_cuba_president |
| 7 | police - court - cash - jailed - said | 53 | 7_police_court_cash_jailed |
| 8 | space - nasa - mars - planet - earth | 51 | 8_space_nasa_mars_planet |
| 9 | property - house - price - room - london | 48 | 9_property_house_price_room |
| 10 | patient - hospital - nhs - doctor - cancer | 48 | 10_patient_hospital_nhs_doctor |
| 11 | tax - bank - minister - mr - pay | 46 | 11_tax_bank_minister_mr |
| 12 | car - fire - crash - bus - train | 45 | 12_car_fire_crash_bus |
| 13 | milk - food - raw - restaurant - chocolate | 44 | 13_milk_food_raw_restaurant |
| 14 | gold - olympic - horse - race - medal | 36 | 14_gold_olympic_horse_race |
| 15 | album - song - joel - music - show | 35 | 15_album_song_joel_music |
| 16 | show - film - movie - award - les | 35 | 16_show_film_movie_award |
| 17 | baby - born - hospital - birth - pregnancy | 34 | 17_baby_born_hospital_birth |
| 18 | prince - queen - royal - william - duchess | 31 | 18_prince_queen_royal_william |
| 19 | chinese - china - bo - beijing - chen | 30 | 19_chinese_china_bo_beijing |
| 20 | labour - mr - party - ukip - miliband | 30 | 20_labour_mr_party_ukip |
| 21 | school - student - teacher - book - fraternity | 29 | 21_school_student_teacher_book |
| 22 | somalia - dala - african - alshabaab - mali | 28 | 22_somalia_dala_african_alshabaab |
| 23 | ukraine - russian - russia - putin - moscow | 26 | 23_ukraine_russian_russia_putin |
| 24 | woods - golf - golfer - hole - round | 26 | 24_woods_golf_golfer_hole |
| 25 | sterling - nba - clippers - donald - said | 26 | 25_sterling_nba_clippers_donald |
| 26 | found - scientist - stonehenge - researcher - frog | 26 | 26_found_scientist_stonehenge_researcher |
| 27 | apple - iphone - apples - phone - device | 24 | 27_apple_iphone_apples_phone |
| 28 | formula - race - schumacher - prix - ecclestone | 23 | 28_formula_race_schumacher_prix |
| 29 | ebola - virus - outbreak - health - vaccine | 22 | 29_ebola_virus_outbreak_health |
| 30 | church - pope - priest - francis - vatican | 21 | 30_church_pope_priest_francis |
| 31 | sharapova - open - wimbledon - tennis - slam | 21 | 31_sharapova_open_wimbledon_tennis |
| 32 | pakistani - pakistan - taliban - musharraf - afghanistan | 21 | 32_pakistani_pakistan_taliban_musharraf |
| 33 | storm - weather - tornado - water - rain | 21 | 33_storm_weather_tornado_water |
| 34 | north - korea - korean - kim - south | 21 | 34_north_korea_korean_kim |
| 35 | war - medal - soldier - army - afghanistan | 21 | 35_war_medal_soldier_army |
| 36 | marijuana - cigarette - alcohol - drug - smoking | 20 | 36_marijuana_cigarette_alcohol_drug |
| 37 | internet - google - user - facebook - online | 19 | 37_internet_google_user_facebook |
| 38 | plane - flight - crash - passenger - airport | 19 | 38_plane_flight_crash_passenger |
| 39 | weight - diet - fat - stone - food | 18 | 39_weight_diet_fat_stone |
| 40 | israeli - israel - gaza - hamas - palestinian | 17 | 40_israeli_israel_gaza_hamas |
| 41 | beach - art - resort - festival - painting | 17 | 41_beach_art_resort_festival |
| 42 | petraeus - cia - broadwell - justice - fbi | 17 | 42_petraeus_cia_broadwell_justice |
| 43 | garner - wilson - officer - police - black | 16 | 43_garner_wilson_officer_police |
| 44 | ship - cruise - ships - crew - pirate | 16 | 44_ship_cruise_ships_crew |
| 45 | nfl - patriots - rice - seahawks - chris | 15 | 45_nfl_patriots_rice_seahawks |
| 46 | dolphin - sea - creature - cuttlefish - fisherman | 14 | 46_dolphin_sea_creature_cuttlefish |
| 47 | weather - rain - winter - temperature - warm | 14 | 47_weather_rain_winter_temperature |
| 48 | mandela - african - africa - south - mandelas | 14 | 48_mandela_african_africa_south |
| 49 | disney - snow - million - wars - movie | 14 | 49_disney_snow_million_wars |
| 50 | price - bag - plastic - cent - energy | 13 | 50_price_bag_plastic_cent |
| 51 | spartan - cliff - parachute - matthew - obstacle | 12 | 51_spartan_cliff_parachute_matthew |
| 52 | zoo - panda - cub - giraffe - park | 12 | 52_zoo_panda_cub_giraffe |
| 53 | iran - iranian - irans - ahmadinejad - nuclear | 12 | 53_iran_iranian_irans_ahmadinejad |
| 54 | bin - laden - us - qaeda - al | 12 | 54_bin_laden_us_qaeda |
| 55 | crocodile - snake - python - bascoules - alligator | 12 | 55_crocodile_snake_python_bascoules |
| 56 | woman - ivf - men - dna - fertility | 11 | 56_woman_ivf_men_dna |
| 57 | driver - driving - police - meracle - text | 11 | 57_driver_driving_police_meracle |
| 58 | mitchell - mr - evans - mp - gate | 10 | 58_mitchell_mr_evans_mp |
| 59 | france - police - mosque - salah - donetsk | 10 | 59_france_police_mosque_salah |
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