
# label_model

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")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 252
* Number of training documents: 14986

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | date - city - pre - heavy - fur | 5 | -1_date_city_pre_heavy | 
| 0 | label_1 label_2 - label_0 label_1 label_2 - label_0 label_1 - label_1 - label_2 | 1333 | 0_label_1 label_2_label_0 label_1 label_2_label_0 label_1_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 | 1043 | 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 - loc misc - misc org - misc - org loc | 651 | 3_loc misc org_loc misc_misc org_misc | 
| 4 | neutral positive - neutral - positive negative - negative - positive | 479 | 4_neutral positive_neutral_positive negative_negative | 
| 5 | label_0 -  -  -  -  | 357 | 5_label_0___ | 
| 6 | contradiction - entailment - neutral - ambiguous -  | 348 | 6_contradiction_entailment_neutral_ambiguous | 
| 7 | label_0 -  -  -  -  | 334 | 7_label_0___ | 
| 8 | 99 -  -  -  -  | 326 | 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 | 300 | 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 | snake - dog - bear - wolf - sea | 245 | 11_snake_dog_bear_wolf | 
| 12 | label_5 label_6 label_7 - label_6 label_7 - label_4 label_5 label_6 - label_5 label_6 - label_6 label_7 label_8 | 241 | 12_label_5 label_6 label_7_label_6 label_7_label_4 label_5 label_6_label_5 label_6 | 
| 13 | loc misc org - loc misc - misc org - misc - org loc | 229 | 13_loc misc org_loc misc_misc org_misc | 
| 14 | weather - transfer - alarm - text - time | 228 | 14_weather_transfer_alarm_text | 
| 15 | label_1 label_2 label_3 - label_2 label_3 - label_3 - label_1 label_2 - label_0 label_1 label_2 | 222 | 15_label_1 label_2 label_3_label_2 label_3_label_3_label_1 label_2 | 
| 16 | delete - different - bad - related - rel | 207 | 16_delete_different_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 |  -  -  -  -  | 166 | 18____ | 
| 19 | loc org loc - loc org - org loc - org - loc | 142 | 19_loc org loc_loc org_org loc_org | 
| 20 | label_6 label_60 label_61 - label_60 label_61 - label_62 label_63 - label_61 label_62 label_63 - label_61 label_62 | 126 | 20_label_6 label_60 label_61_label_60 label_61_label_62 label_63_label_61 label_62 label_63 | 
| 21 | label_4 label_5 label_6 - label_5 label_6 - label_6 - label_1 label_2 label_3 - label_3 label_4 label_5 | 117 | 21_label_4 label_5 label_6_label_5 label_6_label_6_label_1 label_2 label_3 | 
| 22 | test - second -  -  -  | 106 | 22_test_second__ | 
| 23 | forest - industrial - transport - low - bamboo | 104 | 23_forest_industrial_transport_low | 
| 24 | answer - header - question - quantity -  | 104 | 24_answer_header_question_quantity | 
| 25 | healthy - leaf - rust - plant - spot | 103 | 25_healthy_leaf_rust_plant | 
| 26 | left - right - stop - yes - unknown | 100 | 26_left_right_stop_yes | 
| 27 | en - na - alpha - fan - lifestyle | 93 | 27_en_na_alpha_fan | 
| 28 | label_13 label_14 label_15 - label_14 label_15 - label_15 - label_12 label_13 label_14 - label_11 label_12 label_13 | 92 | 28_label_13 label_14 label_15_label_14 label_15_label_15_label_12 label_13 label_14 | 
| 29 | disease - bio - disorder - healthy -  | 86 | 29_disease_bio_disorder_healthy | 
| 30 | work - group - person product - product - location | 86 | 30_work_group_person product_product | 
| 31 | fear joy - sadness surprise - anger fear - joy love - surprise | 82 | 31_fear joy_sadness surprise_anger fear_joy love | 
| 32 | common - non - different -  -  | 78 | 32_common_non_different_ | 
| 33 | dis -  -  -  -  | 76 | 33_dis___ | 
| 34 |  -  -  -  -  | 73 | 34____ | 
| 35 | restaurant - pizza - place - salad - food | 69 | 35_restaurant_pizza_place_salad | 
| 36 | cconj det intj - adj adp adv - det intj noun - det intj - noun num pron | 66 | 36_cconj det intj_adj adp adv_det intj noun_det intj | 
| 37 | label_17 label_18 label_19 - label_18 label_19 label_2 - label_18 label_19 - label_19 label_2 - label_16 label_17 label_18 | 66 | 37_label_17 label_18 label_19_label_18 label_19 label_2_label_18 label_19_label_19 label_2 | 
| 38 | ll - year - related - cause - delete | 65 | 38_ll_year_related_cause | 
| 39 | anger fear - joy love - surprise - joy - love | 64 | 39_anger fear_joy love_surprise_joy | 
| 40 | true - news - partial -  -  | 64 | 40_true_news_partial_ | 
| 41 |  -  -  -  -  | 63 | 41____ | 
| 42 | label_1 label_10 label_11 - label_10 label_11 - label_8 label_9 label_0 - label_7 label_8 label_9 - label_8 label_9 | 62 | 42_label_1 label_10 label_11_label_10 label_11_label_8 label_9 label_0_label_7 label_8 label_9 | 
| 43 | pos - neg -  -  -  | 62 | 43_pos_neg__ | 
| 44 | loc org - org - loc - date - sex | 61 | 44_loc org_org_loc_date | 
| 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 | event - group - person product - product - location | 57 | 46_event_group_person product_product | 
| 47 | bio - chemical - disease - effect - food | 57 | 47_bio_chemical_disease_effect | 
| 48 | 234 - 19 20 21 - 20 21 22 - 22 23 24 - 23 24 | 57 | 48_234_19 20 21_20 21 22_22 23 24 | 
| 49 | fear happy neutral - happy neutral - fear happy - sad - happy | 53 | 49_fear happy neutral_happy neutral_fear happy_sad | 
| 50 | battery - volume - juice - chinese - korean | 53 | 50_battery_volume_juice_chinese | 
| 51 | menu - price - num -  -  | 52 | 51_menu_price_num_ | 
| 52 | poor - ok - good - bad - great | 52 | 52_poor_ok_good_bad | 
| 53 | ll - cause - delete - unknown -  | 51 | 53_ll_cause_delete_unknown | 
| 54 | hospital - unknown - en - material - digital | 48 | 54_hospital_unknown_en_material | 
| 55 | ll - cause - delete - unknown -  | 48 | 55_ll_cause_delete_unknown | 
| 56 | self - question - neutral - yes - statement | 48 | 56_self_question_neutral_yes | 
| 57 | fat - loose - small - sugar - common | 47 | 57_fat_loose_small_sugar | 
| 58 | true -  -  -  -  | 47 | 58_true___ | 
| 59 | cream - drinks - seafood - fruit - ice cream | 46 | 59_cream_drinks_seafood_fruit | 
| 60 | tr - ru - pers - pt - prod | 46 | 60_tr_ru_pers_pt | 
| 61 |  -  -  -  -  | 45 | 61____ | 
| 62 | clothing - care - kitchen - personal - health | 44 | 62_clothing_care_kitchen_personal | 
| 63 | business - news - tech - entertainment - sport | 43 | 63_business_news_tech_entertainment | 
| 64 | non - partial - neutral - yes - ok | 43 | 64_non_partial_neutral_yes | 
| 65 | organization person - location organization - organization - location - person | 43 | 65_organization person_location organization_organization_location | 
| 66 | daisy - tulip - rose -  -  | 43 | 66_daisy_tulip_rose_ | 
| 67 | joy - sadness - anger - angry - happy | 42 | 67_joy_sadness_anger_angry | 
| 68 | samoyed - corgi - husky - pomeranian - golden | 41 | 68_samoyed_corgi_husky_pomeranian | 
| 69 | music - instrument - engine - wind - animals | 41 | 69_music_instrument_engine_wind | 
| 70 | hate - language - reporting - non - normal | 41 | 70_hate_language_reporting_non | 
| 71 | 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 | 41 | 71_label_23 label_24 label_25_label_24 label_25_label_22 label_23 label_24_label_23 label_24 | 
| 72 | id -  -  -  -  | 40 | 72_id___ | 
| 73 | animals - tech - dance - tiger - sport | 40 | 73_animals_tech_dance_tiger | 
| 74 | org org - loc loc - org - misc - loc | 40 | 74_org org_loc loc_org_misc | 
| 75 | star - positive - negative - negative positive -  | 38 | 75_star_positive_negative_negative positive | 
| 76 | bird - ship - frog - horse - truck | 37 | 76_bird_ship_frog_horse | 
| 77 | cat - cats - dog - dogs - sleeping | 37 | 77_cat_cats_dog_dogs | 
| 78 | family - sports - music - related - health | 37 | 78_family_sports_music_related | 
| 79 | 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 | 37 | 79_label_8 label_9 label_0_label_9 label_0 label_1_label_9 label_0_label_7 label_8 label_9 | 
| 80 | room - service - transport - care - kitchen | 37 | 80_room_service_transport_care | 
| 81 | positive - negative - neutral positive - neutral - positive negative | 37 | 81_positive_negative_neutral positive_neutral | 
| 82 | test - play - train - non - live | 36 | 82_test_play_train_non | 
| 83 | tim - evt - pro - gpe - org | 36 | 83_tim_evt_pro_gpe | 
| 84 | cold - disease - pressure - drug - blood | 36 | 84_cold_disease_pressure_drug | 
| 85 | non - early - late -  -  | 35 | 85_non_early_late_ | 
| 86 | 21 - office - 20 - 17 - 16 | 34 | 86_21_office_20_17 | 
| 87 | prep - nn - cc - pro - ex | 34 | 87_prep_nn_cc_pro | 
| 88 | evidence - position - statement - lead - request | 33 | 88_evidence_position_statement_lead | 
| 89 | adp - aux - sconj - cconj - det noun | 33 | 89_adp_aux_sconj_cconj | 
| 90 | job - start - help - address - quantity | 33 | 90_job_start_help_address | 
| 91 | gender - number - case - ind - person | 33 | 91_gender_number_case_ind | 
| 92 | threat - hate - adult - target - male | 33 | 92_threat_hate_adult_target | 
| 93 | institution - tools - organization - org - agent | 32 | 93_institution_tools_organization_org | 
| 94 |  -  -  -  -  | 32 | 94____ | 
| 95 | email - age - patient - state - zip | 32 | 95_email_age_patient_state | 
| 96 | mixed - positive - negative - neutral - neutral positive | 32 | 96_mixed_positive_negative_neutral | 
| 97 | test - help - joke - contact - report | 32 | 97_test_help_joke_contact | 
| 98 | address - balance - statement - request - second | 31 | 98_address_balance_statement_request | 
| 99 |  -  -  -  -  | 31 | 99____ | 
| 100 | hate - non - neutral -  -  | 30 | 100_hate_non_neutral_ | 
| 101 |  -  -  -  -  | 30 | 101____ | 
| 102 | unk - zero - seven - 10 - blank | 30 | 102_unk_zero_seven_10 | 
| 103 | male - female - young - adult - skin | 30 | 103_male_female_young_adult | 
| 104 | 94 - 59 60 - 49 50 - 81 - 97 | 29 | 104_94_59 60_49 50_81 | 
| 105 | normal - cell - large - clean - lower | 29 | 105_normal_cell_large_clean | 
| 106 | lincoln - jaguar - audio - source - general | 28 | 106_lincoln_jaguar_audio_source | 
| 107 | title - section - header - list - item | 28 | 107_title_section_header_list | 
| 108 |  -  -  -  -  | 28 | 108____ | 
| 109 | yes -  -  -  -  | 27 | 109_yes___ | 
| 110 |  -  -  -  -  | 26 | 110____ | 
| 111 | contradiction - entailment - neutral - non -  | 26 | 111_contradiction_entailment_neutral_non | 
| 112 | instrument - org org - org org org - term - org | 26 | 112_instrument_org org_org org org_term | 
| 113 | ft - cardinal - act - loc - loc loc | 25 | 113_ft_cardinal_act_loc | 
| 114 | event - pro - pers - loc org - prod | 25 | 114_event_pro_pers_loc org | 
| 115 | ben - ext - exp - root - loc | 25 | 115_ben_ext_exp_root | 
| 116 |  -  -  -  -  | 25 | 116____ | 
| 117 | low -  -  -  -  | 25 | 117_low___ | 
| 118 | ft - cardinal - act - loc - loc misc org | 25 | 118_ft_cardinal_act_loc | 
| 119 | statement - question - evidence - experience - answer | 25 | 119_statement_question_evidence_experience | 
| 120 | label_122 - label_121 - label_120 - label_123 - label_119 | 24 | 120_label_122_label_121_label_120_label_123 | 
| 121 | clean -  -  -  -  | 24 | 121_clean___ | 
| 122 | ru - tr - el - en - hi | 24 | 122_ru_tr_el_en | 
| 123 | disgust - sadness surprise - joy love - surprise - joy | 24 | 123_disgust_sadness surprise_joy love_surprise | 
| 124 | statement - info - check - news - non | 24 | 124_statement_info_check_news | 
| 125 | motor - start - help - housing - yes | 24 | 125_motor_start_help_housing | 
| 126 | greek - chinese - italian - japanese - dutch | 24 | 126_greek_chinese_italian_japanese | 
| 127 | anger disgust - fear - disgust - sadness - anger | 23 | 127_anger disgust_fear_disgust_sadness | 
| 128 | date event - percent person - quantity - money - percent | 23 | 128_date event_percent person_quantity_money | 
| 129 | label_95 label_96 label_97 - label_97 label_98 label_99 - label_97 label_98 - label_94 label_95 label_96 - label_94 label_95 | 23 | 129_label_95 label_96 label_97_label_97 label_98 label_99_label_97 label_98_label_94 label_95 label_96 | 
| 130 | period - question - noun - number -  | 23 | 130_period_question_noun_number | 
| 131 | neutral -  -  -  -  | 22 | 131_neutral___ | 
| 132 | local - la - pad - data - personal | 22 | 132_local_la_pad_data | 
| 133 | partial -  -  -  -  | 22 | 133_partial___ | 
| 134 | human - art - machine -  -  | 22 | 134_human_art_machine_ | 
| 135 | fear joy - sadness surprise - surprise - disgust fear - joy | 21 | 135_fear joy_sadness surprise_surprise_disgust fear | 
| 136 | location organization - organization person - organization - price - disease | 21 | 136_location organization_organization person_organization_price | 
| 137 | 14 15 16 - 12 13 14 - 13 14 15 - 11 12 13 - 10 11 12 | 21 | 137_14 15 16_12 13 14_13 14 15_11 12 13 | 
| 138 | sports - tech - business - sport -  | 21 | 138_sports_tech_business_sport | 
| 139 | disorder - body - patient - age - disease | 20 | 139_disorder_body_patient_age | 
| 140 | sad - dis - sur - joy -  | 20 | 140_sad_dis_sur_joy | 
| 141 | healthy -  -  -  -  | 20 | 141_healthy___ | 
| 142 | drink - tea - wine - coffee - soft | 20 | 142_drink_tea_wine_coffee | 
| 143 | protein - chemical - cell -  -  | 20 | 143_protein_chemical_cell_ | 
| 144 | rna -  -  -  -  | 20 | 144_rna___ | 
| 145 | normal - covid -  -  -  | 20 | 145_normal_covid__ | 
| 146 | ex - pt -  -  -  | 20 | 146_ex_pt__ | 
| 147 | ok - ft - year - int - rel | 20 | 147_ok_ft_year_int | 
| 148 | header - currency - item - zip - state | 20 | 148_header_currency_item_zip | 
| 149 | label_122 label_123 - label_123 - label_122 - label_121 - label_120 | 19 | 149_label_122 label_123_label_123_label_122_label_121 | 
| 150 | anger disgust - anger disgust fear - disgust fear - disgust - sadness surprise | 19 | 150_anger disgust_anger disgust fear_disgust fear_disgust | 
| 151 | na - nn - ft - dis - bio | 19 | 151_na_nn_ft_dis | 
| 152 | angry - happy - sad - happy neutral - neutral | 19 | 152_angry_happy_sad_happy neutral | 
| 153 | organization percent person - organization percent - miscellaneous - percent person - percent | 19 | 153_organization percent person_organization percent_miscellaneous_percent person | 
| 154 | paper - metal - glass - tray - ticket | 19 | 154_paper_metal_glass_tray | 
| 155 | mask - normal - sharp - head - green | 19 | 155_mask_normal_sharp_head | 
| 156 | noun num pron - num pron propn - pron propn punct - num pron - adj adp adv | 18 | 156_noun num pron_num pron propn_pron propn punct_num pron | 
| 157 | answer -  -  -  -  | 18 | 157_answer___ | 
| 158 | review - id - job - email - state | 18 | 158_review_id_job_email | 
| 159 | seven - queen - jack - king - war | 18 | 159_seven_queen_jack_king | 
| 160 | neg - nan - good -  -  | 18 | 160_neg_nan_good_ | 
| 161 | ii - blank - vi - et - lower | 18 | 161_ii_blank_vi_et | 
| 162 | golden - husky - samoyed - pug - german | 17 | 162_golden_husky_samoyed_pug | 
| 163 | arg - delete - act - neg - lead | 17 | 163_arg_delete_act_neg | 
| 164 | exp - pp - intj - punc - prep | 17 | 164_exp_pp_intj_punc | 
| 165 | email - form - letter - report - news | 17 | 165_email_form_letter_report | 
| 166 | protein - rna - cell - line - type | 17 | 166_protein_rna_cell_line | 
| 167 | en - hi - fur -  -  | 17 | 167_en_hi_fur_ | 
| 168 |  -  -  -  -  | 17 | 168____ | 
| 169 |  -  -  -  -  | 17 | 169____ | 
| 170 | loc loc - loc - pers - evt -  | 16 | 170_loc loc_loc_pers_evt | 
| 171 | menu -  -  -  -  | 16 | 171_menu___ | 
| 172 | normal -  -  -  -  | 16 | 172_normal___ | 
| 173 | label_122 label_123 - label_97 label_98 label_99 - label_97 label_98 - label_96 label_97 label_98 - label_98 label_99 | 16 | 173_label_122 label_123_label_97 label_98 label_99_label_97 label_98_label_96 label_97 label_98 | 
| 174 | cell - organ - organism - tissue - disease | 16 | 174_cell_organ_organism_tissue | 
| 175 | target - instrument - opinion - price - product | 16 | 175_target_instrument_opinion_price | 
| 176 | org org - org org org - loc loc - org - prs | 16 | 176_org org_org org org_loc loc_org | 
| 177 | 10 11 - 10 11 12 - 11 12 - 12 - 11 | 16 | 177_10 11_10 11 12_11 12_12 | 
| 178 | korean - russian - dutch - persian - french | 16 | 178_korean_russian_dutch_persian | 
| 179 | label_4 label_40 label_41 - label_39 label_4 label_40 - label_38 label_39 label_4 - label_37 label_38 label_39 - label_40 label_41 | 16 | 179_label_4 label_40 label_41_label_39 label_4 label_40_label_38 label_39 label_4_label_37 label_38 label_39 | 
| 180 | experience - location - loc misc org - loc misc - misc org | 15 | 180_experience_location_loc misc org_loc misc | 
| 181 | normal - pressure - high - water -  | 15 | 181_normal_pressure_high_water | 
| 182 | company - institution - loc org - degree - org | 15 | 182_company_institution_loc org_degree | 
| 183 | short - sl - long -  -  | 15 | 183_short_sl_long_ | 
| 184 | good - bad - non -  -  | 15 | 184_good_bad_non_ | 
| 185 | 149 - 151 - 191 - 199 - 231 | 15 | 185_149_151_191_199 | 
| 186 | unknown - vi - ii -  -  | 15 | 186_unknown_vi_ii_ | 
| 187 | end - head - cross -  -  | 15 | 187_end_head_cross_ | 
| 188 | forest - street - road - tree - mountain | 15 | 188_forest_street_road_tree | 
| 189 | label_7 label_8 label_9 - label_8 label_9 - label_0 label_1 label_10 - label_1 label_10 - label_10 | 14 | 189_label_7 label_8 label_9_label_8 label_9_label_0 label_1 label_10_label_1 label_10 | 
| 190 | prod - loc - evt - org org - loc loc | 14 | 190_prod_loc_evt_org org | 
| 191 | tech - business - sports - science - female | 14 | 191_tech_business_sports_science | 
| 192 | adult - child - young -  -  | 14 | 192_adult_child_young_ | 
| 193 | human - organism - plants -  -  | 14 | 193_human_organism_plants_ | 
| 194 | hot dog - chicken - hot - food - dog | 14 | 194_hot dog_chicken_hot_food | 
| 195 | rain - snow -  -  -  | 14 | 195_rain_snow__ | 
| 196 | objective - neutral -  -  -  | 14 | 196_objective_neutral__ | 
| 197 | pro - neutral - russian - attack -  | 14 | 197_pro_neutral_russian_attack | 
| 198 | normal - disorder - good -  -  | 14 | 198_normal_disorder_good_ | 
| 199 | road - good - bike -  -  | 14 | 199_road_good_bike_ | 
| 200 |  -  -  -  -  | 14 | 200____ | 
| 201 | science - energy - arts - nuclear - systems | 13 | 201_science_energy_arts_nuclear | 
| 202 |  -  -  -  -  | 13 | 202____ | 
| 203 | event - ticket - ok - loose - non | 13 | 203_event_ticket_ok_loose | 
| 204 | neutral - left - right - unknown -  | 13 | 204_neutral_left_right_unknown | 
| 205 |  -  -  -  -  | 13 | 205____ | 
| 206 | crime - pers - time - book - org | 13 | 206_crime_pers_time_book | 
| 207 | seven - start - record - zero - open | 13 | 207_seven_start_record_zero | 
| 208 | label_5 label_50 label_51 - label_50 label_51 label_52 - label_51 label_52 label_53 - label_51 label_52 - label_50 label_51 | 13 | 208_label_5 label_50 label_51_label_50 label_51 label_52_label_51 label_52 label_53_label_51 label_52 | 
| 209 | label_29 label_3 label_30 - label_26 label_27 label_28 - label_27 label_28 label_29 - label_27 label_28 - label_28 label_29 label_3 | 13 | 209_label_29 label_3 label_30_label_26 label_27 label_28_label_27 label_28 label_29_label_27 label_28 | 
| 210 | human - machine -  -  -  | 13 | 210_human_machine__ | 
| 211 | control - la - sin - social - ambient | 13 | 211_control_la_sin_social | 
| 212 | anger fear - sadness - anger - fear - fear joy | 13 | 212_anger fear_sadness_anger_fear | 
| 213 | panda - ticket - air - bamboo - el | 13 | 213_panda_ticket_air_bamboo | 
| 214 | target -  -  -  -  | 13 | 214_target___ | 
| 215 | id - container - type - person - number | 12 | 215_id_container_type_person | 
| 216 | neutral - positive - negative - neutral positive - positive negative | 12 | 216_neutral_positive_negative_neutral positive | 
| 217 | change - bad - movement - work - science | 12 | 217_change_bad_movement_work | 
| 218 | rust -  -  -  -  | 12 | 218_rust___ | 
| 219 | quantity - container - package - id - weight | 12 | 219_quantity_container_package_id | 
| 220 | text -  -  -  -  | 12 | 220_text___ | 
| 221 | background - objective -  -  -  | 12 | 221_background_objective__ | 
| 222 | middle - subject - yes - request - answer | 12 | 222_middle_subject_yes_request | 
| 223 |  -  -  -  -  | 12 | 223____ | 
| 224 | public - ambiguous - non - person -  | 12 | 224_public_ambiguous_non_person | 
| 225 | healthy - plant - pepper - spot - leaf | 12 | 225_healthy_plant_pepper_spot | 
| 226 | punc - prep - digit - latin - conj | 12 | 226_punc_prep_digit_latin | 
| 227 | location money - language - percent person - actor - money | 12 | 227_location money_language_percent person_actor | 
| 228 |  -  -  -  -  | 11 | 228____ | 
| 229 | punc - zero - pers - neg - reflex | 11 | 229_punc_zero_pers_neg | 
| 230 | album - major - copper - coon - common | 11 | 230_album_major_copper_coon | 
| 231 | metal - pop - country - dance - hip | 11 | 231_metal_pop_country_dance | 
| 232 | energy - common - grass - persian - removal | 11 | 232_energy_common_grass_persian | 
| 233 | man - double - bird - long - single | 11 | 233_man_double_bird_long | 
| 234 | 17 - 16 - 18 - 13 - 15 | 11 | 234_17_16_18_13 | 
| 235 | email - actor - threat - tools - attack | 11 | 235_email_actor_threat_tools | 
| 236 | space -  -  -  -  | 11 | 236_space___ | 
| 237 | type - country - jeep - van - lincoln | 11 | 237_type_country_jeep_van | 
| 238 | general -  -  -  -  | 10 | 238_general___ | 
| 239 | ru - mat -  -  -  | 10 | 239_ru_mat__ | 
| 240 | contradiction - non - entailment - neutral -  | 10 | 240_contradiction_non_entailment_neutral | 
| 241 | city - new - country - location - label_1 | 10 | 241_city_new_country_location | 
| 242 | non - legal - sub -  -  | 9 | 242_non_legal_sub_ | 
| 243 | tulip - cattle - motorcycle - road - color | 8 | 243_tulip_cattle_motorcycle_road | 
| 244 | item - color - cc - model -  | 8 | 244_item_color_cc_model | 
| 245 | delivery - product - service - different - environment | 7 | 245_delivery_product_service_different | 
| 246 | degree - tim - neg - pos - propn | 6 | 246_degree_tim_neg_pos | 
| 247 | threat - hate - non - unknown - neutral | 6 | 247_threat_hate_non_unknown | 
| 248 | label_33 label_34 - label_32 label_33 label_34 - label_32 label_33 - label_31 label_32 label_33 - label_31 label_32 | 6 | 248_label_33 label_34_label_32 label_33 label_34_label_32 label_33_label_31 label_32 label_33 | 
| 249 | experience - location -  -  -  | 6 | 249_experience_location__ | 
| 250 | nat - gpe - geo - pro - tim | 5 | 250_nat_gpe_geo_pro |
  
</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
============


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: 252
* 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 
