Add BERTopic model

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

	# cnn_dailymail_123_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_123_3000_1500_train")

	topic_model.get_topic_info()
	```

	## Topic overview

	* Number of topics: 57
	* 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 - police - people - year \| 10 \| -1_said_one_police_people \|
	\| 0 \| league - player - cup - goal - game \| 1070 \| 0_league_player_cup_goal \|
	\| 1 \| police - said - home - murder - found \| 320 \| 1_police_said_home_murder \|
	\| 2 \| court - mr - said - year - sex \| 142 \| 2_court_mr_said_year \|
	\| 3 \| obama - president - republicans - house - republican \| 113 \| 3_obama_president_republicans_house \|
	\| 4 \| plane - flight - passenger - airport - aircraft \| 89 \| 4_plane_flight_passenger_airport \|
	\| 5 \| hospital - care - family - baby - mr \| 59 \| 5_hospital_care_family_baby \|
	\| 6 \| fashion - dress - style - look - collection \| 57 \| 6_fashion_dress_style_look \|
	\| 7 \| mr - minister - cameron - party - labour \| 50 \| 7_mr_minister_cameron_party \|
	\| 8 \| weight - diet - food - fat - school \| 49 \| 8_weight_diet_food_fat \|
	\| 9 \| mars - space - climate - nasa - mission \| 43 \| 9_mars_space_climate_nasa \|
	\| 10 \| apple - ipad - iphone - app - apples \| 41 \| 10_apple_ipad_iphone_app \|
	\| 11 \| shark - dolphin - fish - coast - water \| 39 \| 11_shark_dolphin_fish_coast \|
	\| 12 \| teacher - school - student - said - state \| 37 \| 12_teacher_school_student_said \|
	\| 13 \| murray - wimbledon - win - champion - match \| 36 \| 13_murray_wimbledon_win_champion \|
	\| 14 \| race - prix - hamilton - gold - world \| 33 \| 14_race_prix_hamilton_gold \|
	\| 15 \| dog - animal - owner - dogs - tiger \| 32 \| 15_dog_animal_owner_dogs \|
	\| 16 \| syrian - syria - isis - islamic - force \| 32 \| 16_syrian_syria_isis_islamic \|
	\| 17 \| storm - weather - lava - snow - said \| 32 \| 17_storm_weather_lava_snow \|
	\| 18 \| chocolate - sale - cent - online - caramel \| 32 \| 18_chocolate_sale_cent_online \|
	\| 19 \| afghanistan - afghan - pakistan - herat - taliban \| 32 \| 19_afghanistan_afghan_pakistan_herat \|
	\| 20 \| music - band - halen - song - album \| 30 \| 20_music_band_halen_song \|
	\| 21 \| beach - island - resort - park - hotel \| 29 \| 21_beach_island_resort_park \|
	\| 22 \| mcilroy - golf - round - shot - hole \| 27 \| 22_mcilroy_golf_round_shot \|
	\| 23 \| text - data - nsa - credit - email \| 26 \| 23_text_data_nsa_credit \|
	\| 24 \| show - film - movie - actor - griffiths \| 26 \| 24_show_film_movie_actor \|
	\| 25 \| putin - russian - russia - ukraine - moscow \| 26 \| 25_putin_russian_russia_ukraine \|
	\| 26 \| art - artist - work - painting - pinata \| 25 \| 26_art_artist_work_painting \|
	\| 27 \| economy - eurozone - european - euro - debt \| 24 \| 27_economy_eurozone_european_euro \|
	\| 28 \| north - kim - korea - korean - jong \| 24 \| 28_north_kim_korea_korean \|
	\| 29 \| ebola - virus - liberia - africa - outbreak \| 22 \| 29_ebola_virus_liberia_africa \|
	\| 30 \| bike - speed - road - driver - cyclist \| 22 \| 30_bike_speed_road_driver \|
	\| 31 \| car - accident - driver - scene - crash \| 20 \| 31_car_accident_driver_scene \|
	\| 32 \| price - london - house - home - property \| 20 \| 32_price_london_house_home \|
	\| 33 \| al - qaeda - yemen - us - yemeni \| 20 \| 33_al_qaeda_yemen_us \|
	\| 34 \| mrs - police - murder - greaves - mr \| 20 \| 34_mrs_police_murder_greaves \|
	\| 35 \| per - cent - people - age - average \| 19 \| 35_per_cent_people_age \|
	\| 36 \| philpott - court - berry - husband - dewani \| 18 \| 36_philpott_court_berry_husband \|
	\| 37 \| facebook - photo - user - instagram - cuddle \| 17 \| 37_facebook_photo_user_instagram \|
	\| 38 \| vaccine - meningitis - disease - flu - princeton \| 17 \| 38_vaccine_meningitis_disease_flu \|
	\| 39 \| bear - lion - gorilla - cub - zoo \| 16 \| 39_bear_lion_gorilla_cub \|
	\| 40 \| brain - drug - alzheimers - memory - patient \| 16 \| 40_brain_drug_alzheimers_memory \|
	\| 41 \| prince - royal - queen - duchess - duke \| 16 \| 41_prince_royal_queen_duchess \|
	\| 42 \| boat - ship - river - vessel - ferry \| 15 \| 42_boat_ship_river_vessel \|
	\| 43 \| china - chinese - chinas - organ - hong \| 14 \| 43_china_chinese_chinas_organ \|
	\| 44 \| egypt - election - egyptian - mubarak - protest \| 13 \| 44_egypt_election_egyptian_mubarak \|
	\| 45 \| mexico - mexican - cartel - mexicos - drug \| 13 \| 45_mexico_mexican_cartel_mexicos \|
	\| 46 \| cia - assange - snowden - us - interrogation \| 13 \| 46_cia_assange_snowden_us \|
	\| 47 \| police - hartman - hore - store - maitua \| 13 \| 47_police_hartman_hore_store \|
	\| 48 \| israeli - israel - palestinian - gaza - hamas \| 12 \| 48_israeli_israel_palestinian_gaza \|
	\| 49 \| pension - tax - scheme - energy - cent \| 12 \| 49_pension_tax_scheme_energy \|
	\| 50 \| council - neighbour - village - site - shed \| 12 \| 50_council_neighbour_village_site \|
	\| 51 \| occupy - protester - york - cosby - mayor \| 11 \| 51_occupy_protester_york_cosby \|
	\| 52 \| mould - allergic - allergy - reaction - hand \| 11 \| 52_mould_allergic_allergy_reaction \|
	\| 53 \| boko - haram - nigeria - sudan - isis \| 11 \| 53_boko_haram_nigeria_sudan \|
	\| 54 \| disaster - building - tsunami - people - quake \| 11 \| 54_disaster_building_tsunami_people \|
	\| 55 \| castro - sloot - der - ariel - aruba \| 11 \| 55_castro_sloot_der_ariel \|

	</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_123_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_123_3000_1500_train")

	topic_model.get_topic_info()
	```

	## Topic overview

	* Number of topics: 57
	* 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 - police - people - year \| 10 \| -1_said_one_police_people \|
	\| 0 \| league - player - cup - goal - game \| 1070 \| 0_league_player_cup_goal \|
	\| 1 \| police - said - home - murder - found \| 320 \| 1_police_said_home_murder \|
	\| 2 \| court - mr - said - year - sex \| 142 \| 2_court_mr_said_year \|
	\| 3 \| obama - president - republicans - house - republican \| 113 \| 3_obama_president_republicans_house \|
	\| 4 \| plane - flight - passenger - airport - aircraft \| 89 \| 4_plane_flight_passenger_airport \|
	\| 5 \| hospital - care - family - baby - mr \| 59 \| 5_hospital_care_family_baby \|
	\| 6 \| fashion - dress - style - look - collection \| 57 \| 6_fashion_dress_style_look \|
	\| 7 \| mr - minister - cameron - party - labour \| 50 \| 7_mr_minister_cameron_party \|
	\| 8 \| weight - diet - food - fat - school \| 49 \| 8_weight_diet_food_fat \|
	\| 9 \| mars - space - climate - nasa - mission \| 43 \| 9_mars_space_climate_nasa \|
	\| 10 \| apple - ipad - iphone - app - apples \| 41 \| 10_apple_ipad_iphone_app \|
	\| 11 \| shark - dolphin - fish - coast - water \| 39 \| 11_shark_dolphin_fish_coast \|
	\| 12 \| teacher - school - student - said - state \| 37 \| 12_teacher_school_student_said \|
	\| 13 \| murray - wimbledon - win - champion - match \| 36 \| 13_murray_wimbledon_win_champion \|
	\| 14 \| race - prix - hamilton - gold - world \| 33 \| 14_race_prix_hamilton_gold \|
	\| 15 \| dog - animal - owner - dogs - tiger \| 32 \| 15_dog_animal_owner_dogs \|
	\| 16 \| syrian - syria - isis - islamic - force \| 32 \| 16_syrian_syria_isis_islamic \|
	\| 17 \| storm - weather - lava - snow - said \| 32 \| 17_storm_weather_lava_snow \|
	\| 18 \| chocolate - sale - cent - online - caramel \| 32 \| 18_chocolate_sale_cent_online \|
	\| 19 \| afghanistan - afghan - pakistan - herat - taliban \| 32 \| 19_afghanistan_afghan_pakistan_herat \|
	\| 20 \| music - band - halen - song - album \| 30 \| 20_music_band_halen_song \|
	\| 21 \| beach - island - resort - park - hotel \| 29 \| 21_beach_island_resort_park \|
	\| 22 \| mcilroy - golf - round - shot - hole \| 27 \| 22_mcilroy_golf_round_shot \|
	\| 23 \| text - data - nsa - credit - email \| 26 \| 23_text_data_nsa_credit \|
	\| 24 \| show - film - movie - actor - griffiths \| 26 \| 24_show_film_movie_actor \|
	\| 25 \| putin - russian - russia - ukraine - moscow \| 26 \| 25_putin_russian_russia_ukraine \|
	\| 26 \| art - artist - work - painting - pinata \| 25 \| 26_art_artist_work_painting \|
	\| 27 \| economy - eurozone - european - euro - debt \| 24 \| 27_economy_eurozone_european_euro \|
	\| 28 \| north - kim - korea - korean - jong \| 24 \| 28_north_kim_korea_korean \|
	\| 29 \| ebola - virus - liberia - africa - outbreak \| 22 \| 29_ebola_virus_liberia_africa \|
	\| 30 \| bike - speed - road - driver - cyclist \| 22 \| 30_bike_speed_road_driver \|
	\| 31 \| car - accident - driver - scene - crash \| 20 \| 31_car_accident_driver_scene \|
	\| 32 \| price - london - house - home - property \| 20 \| 32_price_london_house_home \|
	\| 33 \| al - qaeda - yemen - us - yemeni \| 20 \| 33_al_qaeda_yemen_us \|
	\| 34 \| mrs - police - murder - greaves - mr \| 20 \| 34_mrs_police_murder_greaves \|
	\| 35 \| per - cent - people - age - average \| 19 \| 35_per_cent_people_age \|
	\| 36 \| philpott - court - berry - husband - dewani \| 18 \| 36_philpott_court_berry_husband \|
	\| 37 \| facebook - photo - user - instagram - cuddle \| 17 \| 37_facebook_photo_user_instagram \|
	\| 38 \| vaccine - meningitis - disease - flu - princeton \| 17 \| 38_vaccine_meningitis_disease_flu \|
	\| 39 \| bear - lion - gorilla - cub - zoo \| 16 \| 39_bear_lion_gorilla_cub \|
	\| 40 \| brain - drug - alzheimers - memory - patient \| 16 \| 40_brain_drug_alzheimers_memory \|
	\| 41 \| prince - royal - queen - duchess - duke \| 16 \| 41_prince_royal_queen_duchess \|
	\| 42 \| boat - ship - river - vessel - ferry \| 15 \| 42_boat_ship_river_vessel \|
	\| 43 \| china - chinese - chinas - organ - hong \| 14 \| 43_china_chinese_chinas_organ \|
	\| 44 \| egypt - election - egyptian - mubarak - protest \| 13 \| 44_egypt_election_egyptian_mubarak \|
	\| 45 \| mexico - mexican - cartel - mexicos - drug \| 13 \| 45_mexico_mexican_cartel_mexicos \|
	\| 46 \| cia - assange - snowden - us - interrogation \| 13 \| 46_cia_assange_snowden_us \|
	\| 47 \| police - hartman - hore - store - maitua \| 13 \| 47_police_hartman_hore_store \|
	\| 48 \| israeli - israel - palestinian - gaza - hamas \| 12 \| 48_israeli_israel_palestinian_gaza \|
	\| 49 \| pension - tax - scheme - energy - cent \| 12 \| 49_pension_tax_scheme_energy \|
	\| 50 \| council - neighbour - village - site - shed \| 12 \| 50_council_neighbour_village_site \|
	\| 51 \| occupy - protester - york - cosby - mayor \| 11 \| 51_occupy_protester_york_cosby \|
	\| 52 \| mould - allergic - allergy - reaction - hand \| 11 \| 52_mould_allergic_allergy_reaction \|
	\| 53 \| boko - haram - nigeria - sudan - isis \| 11 \| 53_boko_haram_nigeria_sudan \|
	\| 54 \| disaster - building - tsunami - people - quake \| 11 \| 54_disaster_building_tsunami_people \|
	\| 55 \| castro - sloot - der - ariel - aruba \| 11 \| 55_castro_sloot_der_ariel \|

	</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