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karinegabsschon
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BERTopic_Social

Text Classification
BERTopic
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Add BERTopic model

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Files changed (6) hide show
  1. README.md +82 -0
  2. config.json +17 -0
  3. ctfidf.safetensors +3 -0
  4. ctfidf_config.json +0 -0
  5. topic_embeddings.safetensors +3 -0
  6. topics.json +1954 -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_Social
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("karinegabsschon/BERTopic_Social")
27
+
28
+ topic_model.get_topic_info()
29
+ ```
30
+
31
+ ## Topic overview
32
+
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+ * Number of topics: 13
34
+ * Number of training documents: 205
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 | new - electric - seat - car - manual | 5 | -1_new_electric_seat_car |
42
+ | 0 | electric - car - ev - charging - cent | 25 | 0_electric_car_ev_charging |
43
+ | 1 | tesla - musk - elon - elon musk - vehicle | 54 | 1_tesla_musk_elon_elon musk |
44
+ | 2 | new - nissan - citroen - car - retro | 30 | 2_new_nissan_citroen_car |
45
+ | 3 | percent - cars - car - private - electric | 15 | 3_percent_cars_car_private |
46
+ | 4 | chinese - china - electric - xiaomi - cars | 15 | 4_chinese_china_electric_xiaomi |
47
+ | 5 | electric - vehicles - french - electric car - price | 12 | 5_electric_vehicles_french_electric car |
48
+ | 6 | renault - car - electric - mg - new | 12 | 6_renault_car_electric_mg |
49
+ | 7 | german - trust - brands - quality - german brands | 9 | 7_german_trust_brands_quality |
50
+ | 8 | units - electric - april - russia - electric vehicles | 8 | 8_units_electric_april_russia |
51
+ | 9 | sharing - car sharing - car - audi - club | 8 | 9_sharing_car sharing_car_audi |
52
+ | 10 | used - carmax - car - used car - cars | 6 | 10_used_carmax_car_used car |
53
+ | 11 | best - ev9 - puma - edmunds - electric | 6 | 11_best_ev9_puma_edmunds |
54
+
55
+ </details>
56
+
57
+ ## Training hyperparameters
58
+
59
+ * calculate_probabilities: False
60
+ * language: None
61
+ * low_memory: False
62
+ * min_topic_size: 10
63
+ * n_gram_range: (1, 1)
64
+ * nr_topics: None
65
+ * seed_topic_list: None
66
+ * top_n_words: 10
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+ * verbose: True
68
+ * zeroshot_min_similarity: 0.7
69
+ * zeroshot_topic_list: None
70
+
71
+ ## Framework versions
72
+
73
+ * Numpy: 2.0.2
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+ * HDBSCAN: 0.8.40
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+ * UMAP: 0.5.8
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+ * Pandas: 2.2.2
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+ * Scikit-Learn: 1.6.1
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+ * Sentence-transformers: 4.1.0
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+ * Transformers: 4.53.0
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+ * Numba: 0.60.0
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+ * Plotly: 5.24.1
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+ * Python: 3.11.13
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+ "embedding_model": "ibm-granite/granite-embedding-125m-english"
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+ }
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