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KingKazma
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xsum_22457_3000_1500_validation

Text Classification
BERTopic
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  1. README.md +93 -0
  2. config.json +15 -0
  3. topic_embeddings.safetensors +3 -0
  4. topics.json +2789 -0
README.md ADDED
@@ -0,0 +1,93 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ---
3
+ tags:
4
+ - bertopic
5
+ library_name: bertopic
6
+ pipeline_tag: text-classification
7
+ ---
8
+
9
+ # xsum_22457_3000_1500_validation
10
+
11
+ This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
12
+ BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
13
+
14
+ ## Usage
15
+
16
+ To use this model, please install BERTopic:
17
+
18
+ ```
19
+ pip install -U bertopic
20
+ ```
21
+
22
+ You can use the model as follows:
23
+
24
+ ```python
25
+ from bertopic import BERTopic
26
+ topic_model = BERTopic.load("KingKazma/xsum_22457_3000_1500_validation")
27
+
28
+ topic_model.get_topic_info()
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+ ```
30
+
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+ ## Topic overview
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+
33
+ * Number of topics: 26
34
+ * Number of training documents: 1500
35
+
36
+ <details>
37
+ <summary>Click here for an overview of all topics.</summary>
38
+
39
+ | Topic ID | Topic Keywords | Topic Frequency | Label |
40
+ |----------|----------------|-----------------|-------|
41
+ | -1 | said - people - would - one - year | 5 | -1_said_people_would_one |
42
+ | 0 | said - police - court - mr - heard | 646 | 0_said_police_court_mr |
43
+ | 1 | labour - party - mr - scotland - vote | 242 | 1_labour_party_mr_scotland |
44
+ | 2 | race - olympic - gold - team - medal | 56 | 2_race_olympic_gold_team |
45
+ | 3 | president - un - mr - south - said | 51 | 3_president_un_mr_south |
46
+ | 4 | united - foul - half - kick - win | 48 | 4_united_foul_half_kick |
47
+ | 5 | price - bank - rose - share - said | 44 | 5_price_bank_rose_share |
48
+ | 6 | attack - taliban - militant - killed - said | 41 | 6_attack_taliban_militant_killed |
49
+ | 7 | care - health - nhs - hospital - patient | 32 | 7_care_health_nhs_hospital |
50
+ | 8 | england - cricket - wicket - test - ball | 27 | 8_england_cricket_wicket_test |
51
+ | 9 | specie - tiger - bird - said - breeding | 27 | 9_specie_tiger_bird_said |
52
+ | 10 | rugby - wales - player - coach - world | 27 | 10_rugby_wales_player_coach |
53
+ | 11 | celtic - league - season - game - rangers | 26 | 11_celtic_league_season_game |
54
+ | 12 | album - music - song - show - singer | 26 | 12_album_music_song_show |
55
+ | 13 | open - round - world - play - american | 25 | 13_open_round_world_play |
56
+ | 14 | school - education - schools - said - child | 24 | 14_school_education_schools_said |
57
+ | 15 | film - best - actor - star - actress | 21 | 15_film_best_actor_star |
58
+ | 16 | eu - uk - brexit - trade - would | 21 | 16_eu_uk_brexit_trade |
59
+ | 17 | data - us - internet - said - information | 21 | 17_data_us_internet_said |
60
+ | 18 | league - transfer - season - club - appearance | 20 | 18_league_transfer_season_club |
61
+ | 19 | parking - council - said - road - ringgo | 19 | 19_parking_council_said_road |
62
+ | 20 | trump - mr - clinton - republican - president | 15 | 20_trump_mr_clinton_republican |
63
+ | 21 | water - supply - affected - flooding - customer | 12 | 21_water_supply_affected_flooding |
64
+ | 22 | fifa - corruption - scala - also - president | 12 | 22_fifa_corruption_scala_also |
65
+ | 23 | testimonial - match - tevez - united - player | 6 | 23_testimonial_match_tevez_united |
66
+ | 24 | hiv - outbreak - disease - kong - hong | 6 | 24_hiv_outbreak_disease_kong |
67
+
68
+ </details>
69
+
70
+ ## Training hyperparameters
71
+
72
+ * calculate_probabilities: True
73
+ * language: english
74
+ * low_memory: False
75
+ * min_topic_size: 10
76
+ * n_gram_range: (1, 1)
77
+ * nr_topics: None
78
+ * seed_topic_list: None
79
+ * top_n_words: 10
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+ * verbose: False
81
+
82
+ ## Framework versions
83
+
84
+ * Numpy: 1.22.4
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+ * HDBSCAN: 0.8.33
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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: 4.31.0
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+ * Numba: 0.57.1
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+ * Plotly: 5.13.1
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+ * Python: 3.10.12
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+ ],
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+ "verbose": false,
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+ "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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+ }
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