Add BERTopic model
Browse files- README.md +95 -0
- config.json +14 -0
- ctfidf.safetensors +3 -0
- ctfidf_config.json +237 -0
- images/-1.jpg +0 -0
- images/0.jpg +0 -0
- images/1.jpg +0 -0
- images/10.jpg +0 -0
- images/11.jpg +0 -0
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- images/16.jpg +0 -0
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- images/19.jpg +0 -0
- images/2.jpg +0 -0
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- images/21.jpg +0 -0
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- images/25.jpg +0 -0
- images/26.jpg +0 -0
- images/27.jpg +0 -0
- images/3.jpg +0 -0
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- images/5.jpg +0 -0
- images/6.jpg +0 -0
- images/7.jpg +0 -0
- images/8.jpg +0 -0
- images/9.jpg +0 -0
- topic_embeddings.safetensors +3 -0
- topics.json +0 -0
README.md
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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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---
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# BERTopic_Multimodal
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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("MaartenGr/BERTopic_Multimodal")
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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: 29
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* Number of training documents: 8091
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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 | while - air - the - in - jumping | 34 | -1_while_air_the_in |
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| 0 | bench - sitting - people - woman - street | 1132 | 0_bench_sitting_people_woman |
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| 1 | grass - running - dog - grassy - field | 1693 | 1_grass_running_dog_grassy |
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| 2 | boy - girl - little - young - holding | 1290 | 2_boy_girl_little_young |
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| 3 | dog - frisbee - running - water - mouth | 1224 | 3_dog_frisbee_running_water |
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| 4 | skateboard - ramp - doing - trick - cement | 415 | 4_skateboard_ramp_doing_trick |
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| 5 | snow - dog - covered - running - through | 309 | 5_snow_dog_covered_running |
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| 6 | mountain - range - slope - standing - person | 205 | 6_mountain_range_slope_standing |
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| 7 | pool - blue - boy - toy - water | 189 | 7_pool_blue_boy_toy |
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| 8 | trail - bike - down - riding - person | 166 | 8_trail_bike_down_riding |
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| 9 | snowboarder - mid - jump - air - after | 126 | 9_snowboarder_mid_jump_air |
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| 10 | rock - climbing - up - wall - tree | 124 | 10_rock_climbing_up_wall |
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| 11 | wave - surfboard - top - riding - of | 112 | 11_wave_surfboard_top_riding |
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| 12 | beach - surfboard - people - with - walking | 102 | 12_beach_surfboard_people_with |
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| 13 | jumping - track - horse - racquet - dog | 98 | 13_jumping_track_horse_racquet |
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| 14 | snowboard - snow - girl - hill - slope | 95 | 14_snowboard_snow_girl_hill |
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| 15 | game - being - football - played - professional | 91 | 15_game_being_football_played |
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| 16 | soccer - kicking - team - ball - player | 80 | 16_soccer_kicking_team_ball |
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| 17 | dirt - bike - person - rider - going | 75 | 17_dirt_bike_person_rider |
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| 18 | soccer - boys - field - ball - kicking | 69 | 18_soccer_boys_field_ball |
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| 19 | baseball - player - bat - swinging - into | 63 | 19_baseball_player_bat_swinging |
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| 20 | basketball - up - and - playing - jumping | 59 | 20_basketball_up_and_playing |
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| 21 | bird - body - flying - over - long | 55 | 21_bird_body_flying_over |
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| 22 | motorcycle - track - race - racer - racing | 55 | 22_motorcycle_track_race_racer |
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| 23 | boat - sitting - water - lake - hose | 53 | 23_boat_sitting_water_lake |
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| 24 | street - riding - down - bike - woman | 52 | 24_street_riding_down_bike |
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| 25 | paddle - suit - paddling - water - in | 49 | 25_paddle_suit_paddling_water |
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| 26 | pair - scissors - stage - white - shirt | 42 | 26_pair_scissors_stage_white |
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| 27 | tennis - court - racket - racquet - swinging | 34 | 27_tennis_court_racket_racquet |
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</details>
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## Training hyperparameters
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* calculate_probabilities: False
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* language: None
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* low_memory: False
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* min_topic_size: 30
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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: True
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## Framework versions
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* Numpy: 1.23.5
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* HDBSCAN: 0.8.29
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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: 4.29.2
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* Numba: 0.56.4
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* Plotly: 5.14.1
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* Python: 3.10.10
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config.json
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{
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"calculate_probabilities": false,
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"language": null,
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"low_memory": false,
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"min_topic_size": 30,
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"n_gram_range": [
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1,
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1
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],
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"nr_topics": null,
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": true
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}
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ctfidf.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:465603b0624b3ee3419120940aaf719522f68ab182a3e29e63a1d48381cfd2c1
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size 10240
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ctfidf_config.json
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{
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"ctfidf_model": {
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"bm25_weighting": false,
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"reduce_frequent_words": false
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},
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"vectorizer_model": {
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"params": {
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"analyzer": "word",
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"binary": false,
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"decode_error": "strict",
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"encoding": "utf-8",
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"input": "content",
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"lowercase": true,
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"max_df": 1.0,
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"max_features": null,
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"min_df": 1,
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"ngram_range": [
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1,
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1
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],
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"stop_words": null,
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"strip_accents": null,
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"token_pattern": "(?u)\\b\\w\\w+\\b",
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"vocabulary": null
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},
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"vocab": {
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"woman": 203,
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"is": 85,
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"playing": 131,
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"with": 202,
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"frisbee": 60,
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"on": 113,
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"the": 176,
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"beach": 17,
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"man": 101,
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"in": 83,
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"blue": 28,
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"shirt": 152,
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"water": 196,
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"young": 207,
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"boy": 33,
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"wet": 198,
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"suit": 169,
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"standing": 166,
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"person": 124,
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"jumping": 91,
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"air": 2,
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"while": 199,
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"holding": 78,
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"surfboard": 171,
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"rock": 148,
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"climbing": 44,
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"wall": 193,
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"two": 188,
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"boys": 34,
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"are": 7,
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"to": 179,
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"catch": 39,
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"walking": 192,
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"down": 52,
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"street": 167,
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"cell": 40,
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"phone": 125,
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"crowd": 47,
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"of": 112,
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"people": 122,
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"sitting": 153,
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"bench": 21,
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"outside": 116,
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"building": 36,
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"talking": 173,
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"next": 110,
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"group": 69,
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"dog": 49,
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"bird": 26,
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"her": 73,
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"head": 72,
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"running": 150,
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"grass": 66,
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"through": 177,
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"grassy": 67,
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"field": 56,
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"dogs": 50,
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"each": 54,
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"other": 114,
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"across": 0,
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"and": 5,
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"cat": 38,
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"its": 87,
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"mouth": 108,
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"black": 27,
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"brown": 35,
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"girl": 63,
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"hugging": 82,
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"baseball": 14,
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"bat": 16,
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"his": 75,
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"hand": 70,
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"little": 99,
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"red": 143,
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"umbrella": 189,
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"metal": 103,
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"pole": 132,
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"wooden": 205,
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"bed": 19,
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"skateboard": 154,
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"middle": 105,
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"park": 121,
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"throwing": 178,
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"hill": 74,
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"ball": 11,
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"along": 4,
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"body": 31,
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"skateboarder": 155,
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"doing": 51,
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"trick": 187,
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"ramp": 140,
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"riding": 145,
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"top": 182,
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"cement": 41,
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"bike": 24,
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"snow": 159,
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"covered": 46,
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"snowboard": 160,
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"snowy": 163,
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"surface": 170,
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"slope": 158,
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"tongue": 181,
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"out": 115,
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"laying": 97,
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"fighting": 57,
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"over": 117,
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"ground": 68,
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"jacket": 88,
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"mountain": 107,
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"range": 141,
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"up": 191,
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"backpack": 10,
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"skis": 156,
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"children": 43,
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"pool": 133,
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"toy": 183,
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"baby": 9,
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"board": 29,
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"white": 200,
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"boogie": 32,
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"dirt": 48,
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"trail": 185,
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"snowboarder": 161,
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"pile": 126,
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"mid": 104,
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"after": 1,
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"jump": 90,
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"airborne": 3,
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"going": 65,
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"tree": 186,
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"near": 109,
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"rope": 149,
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"wave": 197,
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"ocean": 111,
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"track": 184,
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"horse": 80,
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"racquet": 139,
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"pair": 120,
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"jockeys": 89,
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166 |
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