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Jerado
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BERTopic

Text Classification
BERTopic
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Files changed (6) hide show
  1. README.md +86 -0
  2. config.json +36 -0
  3. ctfidf.safetensors +3 -0
  4. ctfidf_config.json +0 -0
  5. topic_embeddings.safetensors +3 -0
  6. topics.json +1848 -0
README.md ADDED
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1
+
2
+ ---
3
+ tags:
4
+ - bertopic
5
+ library_name: bertopic
6
+ pipeline_tag: text-classification
7
+ ---
8
+
9
+ # BERTopic
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("Jerado/BERTopic")
27
+
28
+ topic_model.get_topic_info()
29
+ ```
30
+
31
+ ## Topic overview
32
+
33
+ * Number of topics: 17
34
+ * Number of training documents: 1000
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 | theism - much - way - think - just | 15 | -1_theism_much_way_think |
42
+ | 0 | nhl - playoffs - rangers - hockey - league | 304 | 0_nhl_playoffs_rangers_hockey |
43
+ | 1 | performance - ram - drivers - monitor - speed | 92 | 1_performance_ram_drivers_monitor |
44
+ | 2 | x11r5 - hyperhelp - windows - pc - application | 82 | 2_x11r5_hyperhelp_windows_pc |
45
+ | 3 | dos - windows - harddisk - disk - software | 82 | 3_dos_windows_harddisk_disk |
46
+ | 4 | amp - amps - amplifier - ampere - current | 75 | 4_amp_amps_amplifier_ampere |
47
+ | 5 | scripture - christians - sin - bible - commandment | 44 | 5_scripture_christians_sin_bible |
48
+ | 6 | patients - biological - medicine - studies - doctors | 41 | 6_patients_biological_medicine_studies |
49
+ | 7 | nasa - solar - space - shuttle - orbiting | 39 | 7_nasa_solar_space_shuttle |
50
+ | 8 | armenians - armenian - armenia - turks - genocide | 38 | 8_armenians_armenian_armenia_turks |
51
+ | 9 | guns - gun - amendment - constitution - laws | 36 | 9_guns_gun_amendment_constitution |
52
+ | 10 | - - - - | 33 | 10____ |
53
+ | 11 | motorcycle - bikes - cobralinks - bike - riding | 32 | 11_motorcycle_bikes_cobralinks_bike |
54
+ | 12 | encryption - security - encrypted - privacy - secure | 24 | 12_encryption_security_encrypted_privacy |
55
+ | 13 | contacted - address - mail - contact - email | 23 | 13_contacted_address_mail_contact |
56
+ | 14 | paganism - faith - christianity - christians - atheists | 21 | 14_paganism_faith_christianity_christians |
57
+ | 15 | action - fbi - batf - war - president | 19 | 15_action_fbi_batf_war |
58
+
59
+ </details>
60
+
61
+ ## Training hyperparameters
62
+
63
+ * calculate_probabilities: False
64
+ * language: english
65
+ * low_memory: False
66
+ * min_topic_size: 10
67
+ * n_gram_range: (1, 1)
68
+ * nr_topics: None
69
+ * seed_topic_list: [['drug', 'cancer', 'drugs', 'doctor'], ['windows', 'drive', 'dos', 'file'], ['space', 'launch', 'orbit', 'lunar']]
70
+ * top_n_words: 10
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+ * verbose: False
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+ * zeroshot_min_similarity: 0.7
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+ * zeroshot_topic_list: None
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+
75
+ ## Framework versions
76
+
77
+ * Numpy: 1.23.5
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+ * HDBSCAN: 0.8.33
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+ * UMAP: 0.5.6
80
+ * Pandas: 2.0.3
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+ * Scikit-Learn: 1.2.2
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+ * Sentence-transformers: 2.7.0
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+ * Transformers: 4.40.1
84
+ * Numba: 0.58.1
85
+ * Plotly: 5.15.0
86
+ * Python: 3.10.12
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+ [
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+ "windows",
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+ ],
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+ [
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+ "space",
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+ "launch",
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+ "orbit",
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+ "lunar"
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+ ]
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+ "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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+ }
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