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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# xsum_22457_3000_1500_train |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. |
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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("KingKazma/xsum_22457_3000_1500_train") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 45 |
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* Number of training documents: 3000 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | said - mr - would - people - also | 6 | -1_said_mr_would_people | |
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| 0 | win - kick - game - foul - united | 1243 | 0_win_kick_game_foul | |
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| 1 | health - patient - nhs - hospital - cancer | 453 | 1_health_patient_nhs_hospital | |
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| 2 | film - actor - music - song - star | 119 | 2_film_actor_music_song | |
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| 3 | bank - business - share - market - sale | 83 | 3_bank_business_share_market | |
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| 4 | police - northern - ireland - said - crime | 80 | 4_police_northern_ireland_said | |
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| 5 | wicket - england - cricket - test - bowler | 67 | 5_wicket_england_cricket_test | |
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| 6 | president - mr - election - government - farc | 67 | 6_president_mr_election_government | |
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| 7 | labour - party - mr - election - corbyn | 62 | 7_labour_party_mr_election | |
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| 8 | bird - specie - animal - zoo - dna | 58 | 8_bird_specie_animal_zoo | |
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| 9 | school - education - student - teacher - schools | 48 | 9_school_education_student_teacher | |
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| 10 | murder - court - mr - police - said | 42 | 10_murder_court_mr_police | |
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| 11 | crash - police - road - died - collision | 42 | 11_crash_police_road_died | |
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| 12 | rail - transport - said - passenger - train | 38 | 12_rail_transport_said_passenger | |
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| 13 | facebook - console - broadband - game - company | 38 | 13_facebook_console_broadband_game | |
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| 14 | lifeboat - rnli - water - sea - hms | 37 | 14_lifeboat_rnli_water_sea | |
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| 15 | fire - blaze - said - cladding - building | 35 | 15_fire_blaze_said_cladding | |
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| 16 | russia - syria - russian - syrian - military | 34 | 16_russia_syria_russian_syrian | |
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| 17 | girl - child - abuse - court - sexual | 32 | 17_girl_child_abuse_court | |
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| 18 | trump - mr - president - trumps - clinton | 29 | 18_trump_mr_president_trumps | |
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| 19 | man - police - arrested - suspicion - hospital | 27 | 19_man_police_arrested_suspicion | |
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| 20 | murray - tennis - djokovic - wimbledon - grand | 26 | 20_murray_tennis_djokovic_wimbledon | |
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| 21 | medal - gold - olympic - games - world | 25 | 21_medal_gold_olympic_games | |
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| 22 | india - indian - crop - modi - hindu | 24 | 22_india_indian_crop_modi | |
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| 23 | birdie - open - round - golf - mcilroy | 23 | 23_birdie_open_round_golf | |
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| 24 | earth - particle - space - moon - dark | 20 | 24_earth_particle_space_moon | |
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| 25 | madrid - barcelona - foul - assisted - corner | 20 | 25_madrid_barcelona_foul_assisted | |
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| 26 | eu - uk - brexit - european - would | 20 | 26_eu_uk_brexit_european | |
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| 27 | athlete - doping - ioc - olympic - medal | 19 | 27_athlete_doping_ioc_olympic | |
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| 28 | wales - welsh - government - waste - money | 18 | 28_wales_welsh_government_waste | |
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| 29 | race - rosberg - hamilton - mercedes - engine | 16 | 29_race_rosberg_hamilton_mercedes | |
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| 30 | plane - flight - mh370 - aircraft - airlines | 16 | 30_plane_flight_mh370_aircraft | |
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| 31 | fight - pacquiao - mayweather - champion - whyte | 14 | 31_fight_pacquiao_mayweather_champion | |
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| 32 | attack - us - security - bin - killed | 14 | 32_attack_us_security_bin | |
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| 33 | virus - ebola - outbreak - disease - infected | 12 | 33_virus_ebola_outbreak_disease | |
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| 34 | greece - migrant - eu - greek - crisis | 12 | 34_greece_migrant_eu_greek | |
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| 35 | hie - farm - enterprise - energy - funicular | 12 | 35_hie_farm_enterprise_energy | |
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| 36 | inflation - growth - rate - economist - manufacturing | 11 | 36_inflation_growth_rate_economist | |
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| 37 | yn - ar - bod - ei - wedi | 11 | 37_yn_ar_bod_ei | |
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| 38 | cup - group - sredojevic - al - mazembe | 11 | 38_cup_group_sredojevic_al | |
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| 39 | picasso - picture - image - collection - cameron | 9 | 39_picasso_picture_image_collection | |
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| 40 | froome - sky - tour - wiggins - team | 8 | 40_froome_sky_tour_wiggins | |
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| 41 | carnival - event - pride - lgbt - notting | 7 | 41_carnival_event_pride_lgbt | |
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| 42 | cocaine - corkindale - supply - connelly - drug | 6 | 42_cocaine_corkindale_supply_connelly | |
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| 43 | meal - child - school - family - scheme | 6 | 43_meal_child_school_family | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: True |
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* language: english |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: False |
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## Framework versions |
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* Numpy: 1.22.4 |
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* HDBSCAN: 0.8.33 |
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* UMAP: 0.5.3 |
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* Pandas: 1.5.3 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 2.2.2 |
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* Transformers: 4.31.0 |
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* Numba: 0.57.1 |
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* Plotly: 5.13.1 |
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* Python: 3.10.12 |
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