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KingKazma
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xsum_55555_3000_1500_test

Text Classification
BERTopic
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Files changed (4) hide show
  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_55555_3000_1500_test
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_55555_3000_1500_test")
27
+
28
+ topic_model.get_topic_info()
29
+ ```
30
+
31
+ ## Topic overview
32
+
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 - mr - also - would - people | 5 | -1_said_mr_also_would |
42
+ | 0 | police - said - mr - court - heard | 716 | 0_police_said_mr_court |
43
+ | 1 | syria - turkey - syrian - military - said | 112 | 1_syria_turkey_syrian_military |
44
+ | 2 | foul - win - kick - half - shot | 72 | 2_foul_win_kick_half |
45
+ | 3 | growth - year - bank - business - economy | 68 | 3_growth_year_bank_business |
46
+ | 4 | council - said - building - development - new | 63 | 4_council_said_building_development |
47
+ | 5 | england - cricket - captain - test - wicket | 48 | 5_england_cricket_captain_test |
48
+ | 6 | league - club - season - loan - transfer | 42 | 6_league_club_season_loan |
49
+ | 7 | sport - gold - world - athlete - olympic | 38 | 7_sport_gold_world_athlete |
50
+ | 8 | film - music - best - star - song | 36 | 8_film_music_best_star |
51
+ | 9 | party - labour - mr - leader - said | 33 | 9_party_labour_mr_leader |
52
+ | 10 | ireland - wales - leinster - rugby - player | 32 | 10_ireland_wales_leinster_rugby |
53
+ | 11 | care - nhs - hospital - patient - said | 27 | 11_care_nhs_hospital_patient |
54
+ | 12 | road - crash - police - collision - car | 26 | 12_road_crash_police_collision |
55
+ | 13 | dog - animal - greyhound - racing - owner | 23 | 13_dog_animal_greyhound_racing |
56
+ | 14 | ship - beach - said - lifeguard - rnli | 22 | 14_ship_beach_said_lifeguard |
57
+ | 15 | school - education - child - council - said | 20 | 15_school_education_child_council |
58
+ | 16 | wales - bill - welsh - labour - assembly | 19 | 16_wales_bill_welsh_labour |
59
+ | 17 | eu - uk - european - europe - referendum | 18 | 17_eu_uk_european_europe |
60
+ | 18 | fire - blaze - bus - flame - said | 18 | 18_fire_blaze_bus_flame |
61
+ | 19 | mr - president - besigye - maduro - election | 16 | 19_mr_president_besigye_maduro |
62
+ | 20 | race - froome - stage - second - lap | 13 | 20_race_froome_stage_second |
63
+ | 21 | rail - train - rmt - scotrail - transport | 10 | 21_rail_train_rmt_scotrail |
64
+ | 22 | planet - earth - electron - theory - mars | 10 | 22_planet_earth_electron_theory |
65
+ | 23 | ryder - cup - tour - pga - mcilroy | 7 | 23_ryder_cup_tour_pga |
66
+ | 24 | email - lazar - fbi - guccifer - ferizi | 6 | 24_email_lazar_fbi_guccifer |
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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+ "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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+ }
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