metadata
tags:
- bertopic
library_name: bertopic
BERTopic_Multimodal
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:
pip install -U bertopic
You can use the model as follows:
from bertopic import BERTopic
topic_model = BERTopic.load("MaartenGr/BERTopic_Multimodal")
topic_model.get_topic_info()
Topic overview
- Number of topics: 29
- Number of training documents: 8091
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | while - air - the - in - jumping | 34 | -1_while_air_the_in |
| 0 | bench - sitting - people - woman - street | 1132 | 0_bench_sitting_people_woman |
| 1 | grass - running - dog - grassy - field | 1693 | 1_grass_running_dog_grassy |
| 2 | boy - girl - little - young - holding | 1290 | 2_boy_girl_little_young |
| 3 | dog - frisbee - running - water - mouth | 1224 | 3_dog_frisbee_running_water |
| 4 | skateboard - ramp - doing - trick - cement | 415 | 4_skateboard_ramp_doing_trick |
| 5 | snow - dog - covered - running - through | 309 | 5_snow_dog_covered_running |
| 6 | mountain - range - slope - standing - person | 205 | 6_mountain_range_slope_standing |
| 7 | pool - blue - boy - toy - water | 189 | 7_pool_blue_boy_toy |
| 8 | trail - bike - down - riding - person | 166 | 8_trail_bike_down_riding |
| 9 | snowboarder - mid - jump - air - after | 126 | 9_snowboarder_mid_jump_air |
| 10 | rock - climbing - up - wall - tree | 124 | 10_rock_climbing_up_wall |
| 11 | wave - surfboard - top - riding - of | 112 | 11_wave_surfboard_top_riding |
| 12 | beach - surfboard - people - with - walking | 102 | 12_beach_surfboard_people_with |
| 13 | jumping - track - horse - racquet - dog | 98 | 13_jumping_track_horse_racquet |
| 14 | snowboard - snow - girl - hill - slope | 95 | 14_snowboard_snow_girl_hill |
| 15 | game - being - football - played - professional | 91 | 15_game_being_football_played |
| 16 | soccer - kicking - team - ball - player | 80 | 16_soccer_kicking_team_ball |
| 17 | dirt - bike - person - rider - going | 75 | 17_dirt_bike_person_rider |
| 18 | soccer - boys - field - ball - kicking | 69 | 18_soccer_boys_field_ball |
| 19 | baseball - player - bat - swinging - into | 63 | 19_baseball_player_bat_swinging |
| 20 | basketball - up - and - playing - jumping | 59 | 20_basketball_up_and_playing |
| 21 | bird - body - flying - over - long | 55 | 21_bird_body_flying_over |
| 22 | motorcycle - track - race - racer - racing | 55 | 22_motorcycle_track_race_racer |
| 23 | boat - sitting - water - lake - hose | 53 | 23_boat_sitting_water_lake |
| 24 | street - riding - down - bike - woman | 52 | 24_street_riding_down_bike |
| 25 | paddle - suit - paddling - water - in | 49 | 25_paddle_suit_paddling_water |
| 26 | pair - scissors - stage - white - shirt | 42 | 26_pair_scissors_stage_white |
| 27 | tennis - court - racket - racquet - swinging | 34 | 27_tennis_court_racket_racquet |
Training hyperparameters
- calculate_probabilities: False
- language: None
- low_memory: False
- min_topic_size: 30
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 10
- verbose: True
Framework versions
- Numpy: 1.23.5
- 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: 5.14.1
- Python: 3.10.10