
# label_model_merged

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("davanstrien/label_model_merged")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 247
* Number of training documents: 14986

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | pre - roll - heavy - farm - health | 5 | -1_pre_roll_heavy_farm | 
| 0 | label_1 label_2 - label_0 label_1 label_2 - label_1 - label_0 label_1 - label_2 | 1386 | 0_label_1 label_2_label_0 label_1 label_2_label_1_label_0 label_1 | 
| 1 | label_1 label_2 label_3 - label_3 label_4 label_5 - label_4 label_5 - label_2 label_3 label_4 - label_5 | 1042 | 1_label_1 label_2 label_3_label_3 label_4 label_5_label_4 label_5_label_2 label_3 label_4 | 
| 2 | negative positive - positive negative - negative - positive - target | 803 | 2_negative positive_positive negative_negative_positive | 
| 3 | loc misc org - misc org - loc misc - misc - org loc | 652 | 3_loc misc org_misc org_loc misc_misc | 
| 4 | neutral positive - neutral - positive negative - negative - positive | 509 | 4_neutral positive_neutral_positive negative_negative | 
| 5 | label_0 - country - city - label_1 - label_0 label_1 | 357 | 5_label_0_country_city_label_1 | 
| 6 | contradiction - entailment - neutral -  -  | 351 | 6_contradiction_entailment_neutral_ | 
| 7 | label_0 - positive -  -  -  | 335 | 7_label_0_positive__ | 
| 8 | 99 -  -  -  -  | 327 | 8_99___ | 
| 9 | label_1 label_2 label_3 - label_2 label_3 label_4 - label_3 label_4 - label_2 label_3 - label_4 | 302 | 9_label_1 label_2 label_3_label_2 label_3 label_4_label_3 label_4_label_2 label_3 | 
| 10 | entailment - true - child - related - non | 257 | 10_entailment_true_child_related | 
| 11 | terrier - snake - dog - bear - wolf | 245 | 11_terrier_snake_dog_bear | 
| 12 | loc misc org - loc misc - misc org - misc - org loc | 240 | 12_loc misc org_loc misc_misc org_misc | 
| 13 | label_5 label_6 label_7 - label_6 label_7 - label_4 label_5 label_6 - label_5 label_6 - label_7 | 231 | 13_label_5 label_6 label_7_label_6 label_7_label_4 label_5 label_6_label_5 label_6 | 
| 14 | calendar - greeting - weather - transfer - calculator | 229 | 14_calendar_greeting_weather_transfer | 
| 15 | label_1 label_2 label_3 - label_2 label_3 - label_3 - label_1 label_2 - label_0 label_1 label_2 | 226 | 15_label_1 label_2 label_3_label_2 label_3_label_3_label_1 label_2 | 
| 16 | delete - unrelated - bad - related - rel | 207 | 16_delete_unrelated_bad_related | 
| 17 | label_12 label_13 label_14 - label_11 label_12 label_13 - label_13 label_14 - label_12 label_13 - label_10 label_11 label_12 | 172 | 17_label_12 label_13 label_14_label_11 label_12 label_13_label_13 label_14_label_12 label_13 | 
| 18 | loc org - org loc - org - loc - loc loc | 166 | 18_loc org_org loc_org_loc | 
| 19 | left - right - stop - yes - zero | 130 | 19_left_right_stop_yes | 
| 20 | label_6 label_60 label_61 - label_60 label_61 - label_60 label_61 label_62 - label_62 label_63 - label_59 label_6 label_60 | 123 | 20_label_6 label_60 label_61_label_60 label_61_label_60 label_61 label_62_label_62 label_63 | 
| 21 | unrelated -  -  -  -  | 117 | 21_unrelated___ | 
| 22 | forest - industrial - river - transport - disaster | 110 | 22_forest_industrial_river_transport | 
| 23 | label_4 label_5 label_6 - label_5 label_6 - label_6 - label_1 label_2 label_3 - label_3 label_4 label_5 | 107 | 23_label_4 label_5 label_6_label_5 label_6_label_6_label_1 label_2 label_3 | 
| 24 | question - quantity -  -  -  | 106 | 24_question_quantity__ | 
| 25 | healthy - leaf - rust - plant - mildew | 103 | 25_healthy_leaf_rust_plant | 
| 26 | disease - blood - bio - healthy - sexual | 100 | 26_disease_blood_bio_healthy | 
| 27 | work - group - corporation - person product - product | 92 | 27_work_group_corporation_person product | 
| 28 | surprise anger - sadness surprise - fear joy - anger fear - joy love | 80 | 28_surprise anger_sadness surprise_fear joy_anger fear | 
| 29 | duplicate - common - non -  -  | 78 | 29_duplicate_common_non_ | 
| 30 | steak - hamburger - restaurant - pizza - joint | 76 | 30_steak_hamburger_restaurant_pizza | 
| 31 | room - service - transport - product - forest | 74 | 31_room_service_transport_product | 
| 32 | dis -  -  -  -  | 74 | 32_dis___ | 
| 33 |  -  -  -  -  | 73 | 33____ | 
| 34 | loc org - org - date - loc - set | 70 | 34_loc org_org_date_loc | 
| 35 | label_17 label_18 label_19 - label_18 label_19 - label_18 label_19 label_2 - label_19 label_2 - label_16 label_17 label_18 | 70 | 35_label_17 label_18 label_19_label_18 label_19_label_18 label_19 label_2_label_19 label_2 | 
| 36 | 03 - 02 - second -  -  | 65 | 36_03_02_second_ | 
| 37 | anger fear - joy love - surprise - joy - love | 65 | 37_anger fear_joy love_surprise_joy | 
| 38 | real - true - image - news -  | 64 | 38_real_true_image_news | 
| 39 |  -  -  -  -  | 63 | 39____ | 
| 40 | pos - neg - neu -  -  | 62 | 40_pos_neg_neu_ | 
| 41 | 45 - 30 - 55 - 35 - 10 | 61 | 41_45_30_55_35 | 
| 42 | ge - wifi - na - alpha - fan | 61 | 42_ge_wifi_na_alpha | 
| 43 | label_1 label_10 label_11 - label_10 label_11 - label_8 label_9 label_0 - label_9 label_0 label_1 - label_9 label_0 | 61 | 43_label_1 label_10 label_11_label_10 label_11_label_8 label_9 label_0_label_9 label_0 label_1 | 
| 44 | event - group - corporation - person product - product | 61 | 44_event_group_corporation_person product | 
| 45 | label_19 label_2 label_20 - label_2 label_20 - label_20 - label_18 label_19 label_2 - label_18 label_19 | 60 | 45_label_19 label_2 label_20_label_2 label_20_label_20_label_18 label_19 label_2 | 
| 46 | fear happy - sad - happy - disgust fear - angry | 58 | 46_fear happy_sad_happy_disgust fear | 
| 47 | battery - volume - chinese - juice - socks | 58 | 47_battery_volume_chinese_juice | 
| 48 | prep - nn - bio - cc - pro | 56 | 48_prep_nn_bio_cc | 
| 49 | good - poor - ok - great - bad | 56 | 49_good_poor_ok_great | 
| 50 | date - city - fur - day - ar | 54 | 50_date_city_fur_day | 
| 51 | 15 - 18 19 20 - 19 20 - 17 18 19 - 18 19 | 54 | 51_15_18 19 20_19 20_17 18 19 | 
| 52 | menu - price - num -  -  | 52 | 52_menu_price_num_ | 
| 53 | common - fat - loose - small - sugar | 52 | 53_common_fat_loose_small | 
| 54 | append_ - replace_ - append_ append_ - replace_ replace_ - append_ append_ append_ | 49 | 54_append__replace__append_ append__replace_ replace_ | 
| 55 | append_ - replace_ - append_ append_ - replace_ replace_ - append_ append_ append_ | 48 | 55_append__replace__append_ append__replace_ replace_ | 
| 56 | animals - flying - tech - dance - tiger | 48 | 56_animals_flying_tech_dance | 
| 57 | self - question - neutral - yes - greeting | 47 | 57_self_question_neutral_yes | 
| 58 | mt - cv - tr - tm - drug | 47 | 58_mt_cv_tr_tm | 
| 59 | organization person - location organization - organization - location - person | 46 | 59_organization person_location organization_organization_location | 
| 60 |  -  -  -  -  | 45 | 60____ | 
| 61 | joy - anger - sadness - sad - happy | 44 | 61_joy_anger_sadness_sad | 
| 62 | daisy - tulip - rose -  -  | 43 | 62_daisy_tulip_rose_ | 
| 63 | positive - negative - neutral - neutral positive - positive negative | 42 | 63_positive_negative_neutral_neutral positive | 
| 64 | windows - pm - 21 - office - 20 | 42 | 64_windows_pm_21_office | 
| 65 | label_14 label_15 - label_13 label_14 label_15 - label_15 - label_12 label_13 label_14 - label_11 label_12 label_13 | 42 | 65_label_14 label_15_label_13 label_14 label_15_label_15_label_12 label_13 label_14 | 
| 66 | position - statement - lead - request - study | 42 | 66_position_statement_lead_request | 
| 67 | business - news - entertainment - tech - sport | 41 | 67_business_news_entertainment_tech | 
| 68 | hate - speech - language - reporting - non | 41 | 68_hate_speech_language_reporting | 
| 69 | bd - nan - id - bg -  | 41 | 69_bd_nan_id_bg | 
| 70 | cream - burger - carrot - ice cream - salad | 41 | 70_cream_burger_carrot_ice cream | 
| 71 | human - machine - ai - artificial - art | 40 | 71_human_machine_ai_artificial | 
| 72 | open - high - tie - abstract - button | 40 | 72_open_high_tie_abstract | 
| 73 | label_23 label_24 label_25 - label_24 label_25 - label_22 label_23 label_24 - label_23 label_24 - label_21 label_22 label_23 | 40 | 73_label_23 label_24 label_25_label_24 label_25_label_22 label_23 label_24_label_23 label_24 | 
| 74 | label_8 label_9 label_0 - label_9 label_0 label_1 - label_9 label_0 - label_7 label_8 label_9 - label_8 label_9 | 39 | 74_label_8 label_9 label_0_label_9 label_0 label_1_label_9 label_0_label_7 label_8 label_9 | 
| 75 | cat - dog - cats - dogs - drinking | 39 | 75_cat_dog_cats_dogs | 
| 76 | org org - loc loc - org - misc - loc | 38 | 76_org org_loc loc_org_misc | 
| 77 | airplane - deer - bird - ship - frog | 38 | 77_airplane_deer_bird_ship | 
| 78 | label_32 label_33 label_34 - label_33 label_34 - label_31 label_32 label_33 - label_32 label_33 - label_30 label_31 label_32 | 38 | 78_label_32 label_33 label_34_label_33 label_34_label_31 label_32 label_33_label_32 label_33 | 
| 79 | true -  -  -  -  | 38 | 79_true___ | 
| 80 | family - sports - music - related - health | 38 | 80_family_sports_music_related | 
| 81 | star - positive - negative - amazon - negative positive | 37 | 81_star_positive_negative_amazon | 
| 82 | hospital - unknown - description - material - pad | 37 | 82_hospital_unknown_description_material | 
| 83 | threat - hate - reward - quality - content | 36 | 83_threat_hate_reward_quality | 
| 84 | music - speech - instrument - engine - wind | 35 | 84_music_speech_instrument_engine | 
| 85 | closure - annual - statement - issues - reward | 35 | 85_closure_annual_statement_issues | 
| 86 | adp - aux - sconj - pron - noun | 35 | 86_adp_aux_sconj_pron | 
| 87 | experience - location - skill - address - result | 35 | 87_experience_location_skill_address | 
| 88 |  -  -  -  -  | 34 | 88____ | 
| 89 | test - train - risk - non - high | 34 | 89_test_train_risk_non | 
| 90 | samoyed - corgi - husky - golden retriever - golden | 34 | 90_samoyed_corgi_husky_golden retriever | 
| 91 | unk - zero - 10 - 12 13 14 - 13 14 15 | 33 | 91_unk_zero_10_12 13 14 | 
| 92 | non - neutral - ok - lead -  | 33 | 92_non_neutral_ok_lead | 
| 93 | normal - covid - virus - regular - disorder | 33 | 93_normal_covid_virus_regular | 
| 94 | test - help - app - risk - joke | 32 | 94_test_help_app_risk | 
| 95 | replace_ - append_ - replace_ replace_ - append_ append_ - replace_ replace_ replace_ | 32 | 95_replace__append__replace_ replace__append_ append_ | 
| 96 | disease - issues - pressure - drug - blood | 31 | 96_disease_issues_pressure_drug | 
| 97 | women - casual - sexual - individual - use | 31 | 97_women_casual_sexual_individual | 
| 98 | address - balance - code - second - currency | 30 | 98_address_balance_code_second | 
| 99 | hate - non - neutral -  -  | 30 | 99_hate_non_neutral_ | 
| 100 | normal - cell - large - clean - healthy | 29 | 100_normal_cell_large_clean | 
| 101 | neutral - se -  -  -  | 29 | 101_neutral_se__ | 
| 102 | male - female - hair - skin - men | 29 | 102_male_female_hair_skin | 
| 103 | title - page - section - abstract - table | 28 | 103_title_page_section_abstract | 
| 104 | number - gender - case - person - fin | 28 | 104_number_gender_case_person | 
| 105 | man - bird - flower - long - double | 28 | 105_man_bird_flower_long | 
| 106 | contradiction - entailment - neutral -  -  | 28 | 106_contradiction_entailment_neutral_ | 
| 107 | non -  -  -  -  | 27 | 107_non___ | 
| 108 | tim - fac - org - pro - loc | 27 | 108_tim_fac_org_pro | 
| 109 | lincoln - jaguar - visual - audio - sony | 27 | 109_lincoln_jaguar_visual_audio | 
| 110 | statement - info - check - ad - news | 27 | 110_statement_info_check_ad | 
| 111 | ben - ext - root - exp - loc | 26 | 111_ben_ext_root_exp | 
| 112 | yes -  -  -  -  | 26 | 112_yes___ | 
| 113 | queen - jack - king - south - war | 26 | 113_queen_jack_king_south | 
| 114 |  -  -  -  -  | 26 | 114____ | 
| 115 | ft - cardinal - act - loc - loc loc | 25 | 115_ft_cardinal_act_loc | 
| 116 | bio - chemical - food -  -  | 25 | 116_bio_chemical_food_ | 
| 117 | ft - cardinal - act - loc - loc misc org | 25 | 117_ft_cardinal_act_loc | 
| 118 | metric - task -  -  -  | 25 | 118_metric_task__ | 
| 119 | email - age - patient - zip - organization | 25 | 119_email_age_patient_zip | 
| 120 | ent - im - ru - mat - art | 25 | 120_ent_im_ru_mat | 
| 121 | ex - pt - galaxy - moon - 8888 | 24 | 121_ex_pt_galaxy_moon | 
| 122 |  -  -  -  -  | 24 | 122____ | 
| 123 | neu - sad - dis - joy -  | 24 | 123_neu_sad_dis_joy | 
| 124 | label_122 - label_121 - label_120 - label_123 - label_119 | 24 | 124_label_122_label_121_label_120_label_123 | 
| 125 | mixed - positive - negative - neutral positive - neutral | 24 | 125_mixed_positive_negative_neutral positive | 
| 126 | date event - percent person - quantity - money - percent | 24 | 126_date event_percent person_quantity_money | 
| 127 | fear joy - sadness surprise - surprise - joy - sadness | 24 | 127_fear joy_sadness surprise_surprise_joy | 
| 128 | disgust - sadness surprise - joy love - surprise - joy | 24 | 128_disgust_sadness surprise_joy love_surprise | 
| 129 | magnet - motor - hello - undefined - start | 24 | 129_magnet_motor_hello_undefined | 
| 130 | loc loc - loc - pers - hi - en | 24 | 130_loc loc_loc_pers_hi | 
| 131 | event - pers - fac - pro - loc org | 24 | 131_event_pers_fac_pro | 
| 132 | disorder - body - patient - age - disease | 23 | 132_disorder_body_patient_age | 
| 133 | happiness - fear - anger disgust - disgust - sadness | 23 | 133_happiness_fear_anger disgust_disgust | 
| 134 | control - la - social - sin - civil | 23 | 134_control_la_social_sin | 
| 135 | label_98 label_99 - label_97 label_98 label_99 - label_97 label_98 - label_95 label_96 - label_96 label_97 label_98 | 23 | 135_label_98 label_99_label_97 label_98 label_99_label_97 label_98_label_95 label_96 | 
| 136 | greek - chinese - italian - japanese - dutch | 23 | 136_greek_chinese_italian_japanese | 
| 137 | clean -  -  -  -  | 23 | 137_clean___ | 
| 138 | protein - chemical - cell -  -  | 22 | 138_protein_chemical_cell_ | 
| 139 | treatment - disease - location organization - organization person - organization | 22 | 139_treatment_disease_location organization_organization person | 
| 140 | institution - tools - org - loc - organization | 22 | 140_institution_tools_org_loc | 
| 141 | statement - question -  -  -  | 22 | 141_statement_question__ | 
| 142 | period - question - noun - number -  | 21 | 142_period_question_noun_number | 
| 143 | regular -  -  -  -  | 21 | 143_regular___ | 
| 144 | rna -  -  -  -  | 21 | 144_rna___ | 
| 145 | rs -  -  -  -  | 21 | 145_rs___ | 
| 146 | address - id - job - email - country | 21 | 146_address_id_job_email | 
| 147 | neg - neu - good -  -  | 21 | 147_neg_neu_good_ | 
| 148 | label_122 label_123 - label_123 - label_122 - label_121 - label_120 | 20 | 148_label_122 label_123_label_123_label_122_label_121 | 
| 149 | drink - tea - wine - coffee - soft | 20 | 149_drink_tea_wine_coffee | 
| 150 | miscellaneous - organization - percent - money - percent person | 20 | 150_miscellaneous_organization_percent_money | 
| 151 | description - invoice - zip - state - city | 20 | 151_description_invoice_zip_state | 
| 152 | sports - tech - business - sport -  | 20 | 152_sports_tech_business_sport | 
| 153 | ok - vin - rl - ft - year | 20 | 153_ok_vin_rl_ft | 
| 154 | healthy -  -  -  -  | 20 | 154_healthy___ | 
| 155 | association - event - ticket - disaster - map | 20 | 155_association_event_ticket_disaster | 
| 156 | 10 11 - 10 11 12 - 11 12 - 11 - 11 12 13 | 19 | 156_10 11_10 11 12_11 12_11 | 
| 157 | noun num pron - num pron propn - num pron - pron propn punct - pron propn | 19 | 157_noun num pron_num pron propn_num pron_pron propn punct | 
| 158 | cell - organ - organism - multi - tissue | 18 | 158_cell_organ_organism_multi | 
| 159 | 02 - ent - express - act - delete | 18 | 159_02_ent_express_act | 
| 160 | sym verb adj - verb adj adp - intj noun num - det intj noun - adj adp adv | 18 | 160_sym verb adj_verb adj adp_intj noun num_det intj noun | 
| 161 | 12 -  -  -  -  | 18 | 161_12___ | 
| 162 | org org - org - drug -  -  | 18 | 162_org org_org_drug_ | 
| 163 | short - long - sl - ac - pad | 18 | 163_short_long_sl_ac | 
| 164 | plastic - paper - glass - metal - sheet | 18 | 164_plastic_paper_glass_metal | 
| 165 | ii - blank - iii - vi - et | 17 | 165_ii_blank_iii_vi | 
| 166 | normal - virus - desert - smoke - pressure | 17 | 166_normal_virus_desert_smoke | 
| 167 | skill - skills -  -  -  | 17 | 167_skill_skills__ | 
| 168 | protein - rna - cell - line - type | 17 | 168_protein_rna_cell_line | 
| 169 | korean - russian - dutch - french - thai | 17 | 169_korean_russian_dutch_french | 
| 170 | rainbow - rain - snow - color - green | 17 | 170_rainbow_rain_snow_color | 
| 171 | company - role - institution - skill - loc org | 16 | 171_company_role_institution_skill | 
| 172 | exp - pp - intj - punc - prep | 16 | 172_exp_pp_intj_punc | 
| 173 | key - menu -  -  -  | 16 | 173_key_menu__ | 
| 174 | adult - young - child - male - female | 16 | 174_adult_young_child_male | 
| 175 | normal -  -  -  -  | 16 | 175_normal___ | 
| 176 | mask - bright - sharp - head - normal | 16 | 176_mask_bright_sharp_head | 
| 177 | anger disgust fear - anger disgust - disgust fear - disgust - surprise anger | 16 | 177_anger disgust fear_anger disgust_disgust fear_disgust | 
| 178 |  -  -  -  -  | 16 | 178____ | 
| 179 | objective - non - neutral -  -  | 16 | 179_objective_non_neutral_ | 
| 180 | cr - sd - db -  -  | 16 | 180_cr_sd_db_ | 
| 181 |  -  -  -  -  | 16 | 181____ | 
| 182 | label_29 label_3 label_30 - label_27 label_28 label_29 - label_26 label_27 label_28 - label_28 label_29 label_3 - label_29 label_3 | 16 | 182_label_29 label_3 label_30_label_27 label_28 label_29_label_26 label_27 label_28_label_28 label_29 label_3 | 
| 183 | test -  -  -  -  | 15 | 183_test___ | 
| 184 | good - bad - non -  -  | 15 | 184_good_bad_non_ | 
| 185 | local - por - art - da - em | 15 | 185_local_por_art_da | 
| 186 | label_122 label_123 - label_97 label_98 label_99 - label_97 label_98 - label_96 label_97 label_98 - label_98 label_99 | 15 | 186_label_122 label_123_label_97 label_98 label_99_label_97 label_98_label_96 label_97 label_98 | 
| 187 | prod - loc - evt - misc - org org | 15 | 187_prod_loc_evt_misc | 
| 188 | invoice - email - form - letter - report | 15 | 188_invoice_email_form_letter | 
| 189 | end - head - cross -  -  | 15 | 189_end_head_cross_ | 
| 190 | target - instrument - opinion - question - price | 15 | 190_target_instrument_opinion_question | 
| 191 | unrelated - support -  -  -  | 15 | 191_unrelated_support__ | 
| 192 | ru - pl - bg - en - es | 14 | 192_ru_pl_bg_en | 
| 193 | road - good - bike -  -  | 14 | 193_road_good_bike_ | 
| 194 | human - organism - plants -  -  | 14 | 194_human_organism_plants_ | 
| 195 | label_7 label_8 label_9 - label_8 label_9 - label_0 label_1 label_10 - label_1 label_10 - label_10 | 14 | 195_label_7 label_8 label_9_label_8 label_9_label_0 label_1 label_10_label_1 label_10 | 
| 196 | replace_ - append_ - replace_ replace_ - append_ append_ - replace_ replace_ replace_ | 13 | 196_replace__append__replace_ replace__append_ append_ | 
| 197 | brand - company - tm - color - item | 13 | 197_brand_company_tm_color | 
| 198 | pro - neutral - russian - support - attack | 13 | 198_pro_neutral_russian_support | 
| 199 | 18 19 20 - 19 20 - 23 - 17 18 19 - 21 | 13 | 199_18 19 20_19 20_23_17 18 19 | 
| 200 | crime - pers - time - book - day | 13 | 200_crime_pers_time_book | 
| 201 | neutral - positive - negative - positive negative - neutral positive | 13 | 201_neutral_positive_negative_positive negative | 
| 202 |  -  -  -  -  | 13 | 202____ | 
| 203 | chemical - disease - bio -  -  | 13 | 203_chemical_disease_bio_ | 
| 204 | angry - happy - sad - neutral - 60 | 12 | 204_angry_happy_sad_neutral | 
| 205 | organisation - task - country - location - product | 12 | 205_organisation_task_country_location | 
| 206 | iv - iii - vi - ii - unknown | 12 | 206_iv_iii_vi_ii | 
| 207 | neutral - risk -  -  -  | 12 | 207_neutral_risk__ | 
| 208 | container - id - type - person - number | 12 | 208_container_id_type_person | 
| 209 | target -  -  -  -  | 12 | 209_target___ | 
| 210 | pop - metal - country - song - rock | 12 | 210_pop_metal_country_song | 
| 211 | email - os - language - method - function | 12 | 211_email_os_language_method | 
| 212 | contradiction - non - entailment -  -  | 12 | 212_contradiction_non_entailment_ | 
| 213 | background - objective - method - result -  | 12 | 213_background_objective_method_result | 
| 214 | convertible - cab - type - series - martin | 12 | 214_convertible_cab_type_series | 
| 215 | public - smoking - drinking - ambiguous - non | 12 | 215_public_smoking_drinking_ambiguous | 
| 216 | rust -  -  -  -  | 12 | 216_rust___ | 
| 217 | persian - mr - man - flying - ghost | 12 | 217_persian_mr_man_flying | 
| 218 | quote - yes - middle - request -  | 12 | 218_quote_yes_middle_request | 
| 219 | text - mixed -  -  -  | 12 | 219_text_mixed__ | 
| 220 | punc - prep - digit - latin - conj | 12 | 220_punc_prep_digit_latin | 
| 221 | panda - air - mr - ticket - little | 12 | 221_panda_air_mr_ticket | 
| 222 |  -  -  -  -  | 12 | 222____ | 
| 223 | sym verb adj - intj noun num - verb adj adp - cconj det intj - aux cconj det | 12 | 223_sym verb adj_intj noun num_verb adj adp_cconj det intj | 
| 224 | healthy - tomato - plant - pepper - spot | 11 | 224_healthy_tomato_plant_pepper | 
| 225 | sony - lg - tv - galaxy - monitor | 11 | 225_sony_lg_tv_galaxy | 
| 226 | new - city - mid - location - south | 11 | 226_new_city_mid_location | 
| 227 | space -  -  -  -  | 11 | 227_space___ | 
| 228 | cloud - racing - motorcycle - boy - bus | 11 | 228_cloud_racing_motorcycle_boy | 
| 229 | punc - zero - pers - neg - reflex | 11 | 229_punc_zero_pers_neg | 
| 230 | energy - arts - high - systems - computer | 11 | 230_energy_arts_high_systems | 
| 231 | dis - ad - media - site - plant | 11 | 231_dis_ad_media_site | 
| 232 | world - tech - business - sports - female | 11 | 232_world_tech_business_sports | 
| 233 | sadness - anger - anger fear - joy - fear | 10 | 233_sadness_anger_anger fear_joy | 
| 234 | neg - adj - sym - propn - num | 10 | 234_neg_adj_sym_propn | 
| 235 | bulldog - cat - husky - pug - corgi | 9 | 235_bulldog_cat_husky_pug | 
| 236 |  -  -  -  -  | 8 | 236____ | 
| 237 | origin - quote - actor - opinion - language | 7 | 237_origin_quote_actor_opinion | 
| 238 | na - nb - nc - neu - ng | 7 | 238_na_nb_nc_neu | 
| 239 |  -  -  -  -  | 7 | 239____ | 
| 240 | ci - aa - joy - im - ip | 7 | 240_ci_aa_joy_im | 
| 241 |  -  -  -  -  | 6 | 241____ | 
| 242 | skill - email - address - grade - language | 6 | 242_skill_email_address_grade | 
| 243 | sexual - threat - christian - hate - male | 6 | 243_sexual_threat_christian_hate | 
| 244 | transmission - wind - tower - pole -  | 6 | 244_transmission_wind_tower_pole | 
| 245 | label_14 label_15 - label_13 label_14 label_15 - label_15 - label_12 label_13 label_14 - label_11 label_12 label_13 | 6 | 245_label_14 label_15_label_13 label_14 label_15_label_15_label_12 label_13 label_14 |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* 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

## Framework versions

* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11

{"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"}
bertopic text-classification region:us
TAGS
#bertopic #text-classification #region-us 

label\_model\_merged
====================


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:


You can use the model as follows:


Topic overview
--------------


* Number of topics: 247
* Number of training documents: 14986



Click here for an overview of all topics.


Training hyperparameters
------------------------


* calculate\_probabilities: False
* language: None
* 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


Framework versions
------------------


* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11



TAGS
#bertopic #text-classification #region-us 
