Add BERTopic model

440b54c over 1 year ago

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	---
	tags:
	- bertopic
	library_name: bertopic
	pipeline_tag: text-classification
	---

	# cnn_dailymail_55555_3000_1500_train

	This is a [BERTopic](https://github.com/MaartenGr/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:

	```python
	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

	<details>
	<summary>Click here for an overview of all topics.</summary>

	\| 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 \|

	</details>

	## 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


	---
	tags:
	- bertopic
	library_name: bertopic
	pipeline_tag: text-classification
	---

	# cnn_dailymail_55555_3000_1500_train

	This is a [BERTopic](https://github.com/MaartenGr/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:

	```python
	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

	<details>
	<summary>Click here for an overview of all topics.</summary>

	\| 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 \|

	</details>

	## 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