Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# BERTopic_andattakstruk_2
+
+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("GiganticLemon/BERTopic_andattakstruk_2")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 62
+* Number of training documents: 16559
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | the - and - to - of - in | 21 | -1_the_and_to_of | 
+| 0 | the - to - of - and - is | 8983 | 0_the_to_of_and | 
+| 1 | the - to - that - he - and | 1232 | 1_the_to_that_he | 
+| 2 | her - she - to - and - is | 605 | 2_her_she_to_and | 
+| 3 | and - the - of - to - in | 506 | 3_and_the_of_to | 
+| 4 | the - of - earth - to - and | 473 | 4_the_of_earth_to | 
+| 5 | the - and - to - he - his | 459 | 5_the_and_to_he | 
+| 6 | the - and - to - of - ship | 416 | 6_the_and_to_of | 
+| 7 | the - to - of - and - his | 370 | 7_the_to_of_and | 
+| 8 | de - his - he - to - the | 306 | 8_de_his_he_to | 
+| 9 | her - she - to - and - is | 192 | 9_her_she_to_and | 
+| 10 | chinese - the - and - of - to | 160 | 10_chinese_the_and_of | 
+| 11 | the - president - soviet - of - us | 150 | 11_the_president_soviet_of | 
+| 12 | russian - the - his - to - of | 145 | 12_russian_the_his_to | 
+| 13 | asterix - roman - obelix - the - rome | 141 | 13_asterix_roman_obelix_the | 
+| 14 | doctor - tardis - the - ace - to | 140 | 14_doctor_tardis_the_ace | 
+| 15 | of - that - the - in - or | 138 | 15_of_that_the_in | 
+| 16 | socrates - theseus - the - of - and | 130 | 16_socrates_theseus_the_of | 
+| 17 | vampire - vampires - darren - sookie - to | 111 | 17_vampire_vampires_darren_sookie | 
+| 18 | kirk - enterprise - spock - federation - klingon | 111 | 18_kirk_enterprise_spock_federation | 
+| 19 | reacher - hardy - frank - boys - hardys | 101 | 19_reacher_hardy_frank_boys | 
+| 20 | cadfael - his - the - to - of | 99 | 20_cadfael_his_the_to | 
+| 21 | jedi - vong - luke - leia - han | 87 | 21_jedi_vong_luke_leia | 
+| 22 | german - szpilman - hitler - was - the | 78 | 22_german_szpilman_hitler_was | 
+| 23 | jesus - judah - god - of - the | 78 | 23_jesus_judah_god_of | 
+| 24 | animorphs - jake - visser - ax - cassie | 67 | 24_animorphs_jake_visser_ax | 
+| 25 | spirou - fantasio - champignac - count - marsupilami | 66 | 25_spirou_fantasio_champignac_count | 
+| 26 | henson - white - black - the - slaves | 57 | 26_henson_white_black_the | 
+| 27 | novel - of - his - in - book | 56 | 27_novel_of_his_in | 
+| 28 | dawkins - of - that - science - religion | 55 | 28_dawkins_of_that_science | 
+| 29 | obiwan - jedi - quigon - kenobi - anakin | 52 | 29_obiwan_jedi_quigon_kenobi | 
+| 30 | cats - clan - thunderclan - kits - firestar | 48 | 30_cats_clan_thunderclan_kits | 
+| 31 | redwall - abbey - the - and - vermin | 48 | 31_redwall_abbey_the_and | 
+| 32 | virus - the - to - is - of | 47 | 32_virus_the_to_is | 
+| 33 | buffy - sunnydale - willow - slayer - giles | 46 | 33_buffy_sunnydale_willow_slayer | 
+| 34 | time - machine - traveller - in - the | 44 | 34_time_machine_traveller_in | 
+| 35 | confederate - lee - scarlett - rhett - the | 38 | 35_confederate_lee_scarlett_rhett | 
+| 36 | bond - bonds - to - leiter - by | 37 | 36_bond_bonds_to_leiter | 
+| 37 | baseball - hobbs - game - team - belichick | 37 | 37_baseball_hobbs_game_team | 
+| 38 | sharpe - scene - french - sharpes - harper | 36 | 38_sharpe_scene_french_sharpes | 
+| 39 | nancy - bess - nancys - george - mystery | 33 | 39_nancy_bess_nancys_george | 
+| 40 | women - of - ellador - men - in | 33 | 40_women_of_ellador_men | 
+| 41 | manticore - sten - haven - fleet - honor | 32 | 41_manticore_sten_haven_fleet | 
+| 42 | billy - john - horse - ranch - harold | 31 | 42_billy_john_horse_ranch | 
+| 43 | global - warming - climate - energy - carbon | 30 | 43_global_warming_climate_energy | 
+| 44 | christmas - claus - santa - roger - mimi | 30 | 44_christmas_claus_santa_roger | 
+| 45 | holmes - sherlock - watson - douglas - that | 29 | 45_holmes_sherlock_watson_douglas | 
+| 46 | tarzan - ape - lion - tarzans - opar | 28 | 46_tarzan_ape_lion_tarzans | 
+| 47 | conan - conans - dake - aquilonia - raseri | 28 | 47_conan_conans_dake_aquilonia | 
+| 48 | angel - angels - quillon - archangel - alleluia | 27 | 48_angel_angels_quillon_archangel | 
+| 49 | lone - wolf - kai - magnamund - darklords | 27 | 49_lone_wolf_kai_magnamund | 
+| 50 | helm - matt - helms - mac - agency | 27 | 50_helm_matt_helms_mac | 
+| 51 | dorothy - oz - elphaba - wizard - ozma | 27 | 51_dorothy_oz_elphaba_wizard | 
+| 52 | max - fang - flock - roland - victor | 26 | 52_max_fang_flock_roland | 
+| 53 | tom - swift - mr - airship - toms | 25 | 53_tom_swift_mr_airship | 
+| 54 | tintin - haddock - calculus - snowy - the | 25 | 54_tintin_haddock_calculus_snowy | 
+| 55 | robot - robots - derec - ariel - city | 23 | 55_robot_robots_derec_ariel | 
+| 56 | bertie - jeeves - emsworth - gally - freddie | 23 | 56_bertie_jeeves_emsworth_gally | 
+| 57 | alex - sarov - alexs - mi6 - to | 23 | 57_alex_sarov_alexs_mi6 | 
+| 58 | carson - rayford - tribulation - carpathia - buck | 22 | 58_carson_rayford_tribulation_carpathia | 
+| 59 | dresden - harry - thomas - murphy - dresdens | 22 | 59_dresden_harry_thomas_murphy | 
+| 60 | brigitta - major - life - novel - of | 22 | 60_brigitta_major_life_novel |
+
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* 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: True
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 2.0.2
+* HDBSCAN: 0.8.40
+* UMAP: 0.5.7
+* Pandas: 2.2.2
+* Scikit-Learn: 1.6.1
+* Sentence-transformers: 3.4.1
+* Transformers: 4.51.3
+* Numba: 0.60.0
+* Plotly: 5.24.1
+* Python: 3.11.12

config.json

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+{
+  "calculate_probabilities": false,
+  "language": "english",
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": null,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": true,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null,
+  "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
+}

ctfidf.safetensors

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+size 7247376

topic_embeddings.safetensors

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+oid sha256:46ba0fea440cb0d9e6dde84706adc879861607286bce5c831055765cb4c74cf0
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