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AIDA-UPM
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MARTINI_enrich_BERTopic_UKcitizen2021

Text Classification
BERTopic
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Add BERTopic model

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Files changed (6) hide show
  1. README.md +80 -0
  2. config.json +16 -0
  3. ctfidf.safetensors +3 -0
  4. ctfidf_config.json +0 -0
  5. topic_embeddings.safetensors +3 -0
  6. topics.json +1055 -0
README.md ADDED
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1
+
2
+ ---
3
+ tags:
4
+ - bertopic
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+ library_name: bertopic
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+ pipeline_tag: text-classification
7
+ ---
8
+
9
+ # MARTINI_enrich_BERTopic_UKcitizen2021
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+
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("AIDA-UPM/MARTINI_enrich_BERTopic_UKcitizen2021")
27
+
28
+ topic_model.get_topic_info()
29
+ ```
30
+
31
+ ## Topic overview
32
+
33
+ * Number of topics: 11
34
+ * Number of training documents: 721
35
+
36
+ <details>
37
+ <summary>Click here for an overview of all topics.</summary>
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+
39
+ | Topic ID | Topic Keywords | Topic Frequency | Label |
40
+ |----------|----------------|-----------------|-------|
41
+ | -1 | lockdown - wef - monkeypox - agenda - nuremberg | 22 | -1_lockdown_wef_monkeypox_agenda |
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+ | 0 | vaccinated - pfizer - vaxx - worldcouncilforhealth - injections | 313 | 0_vaccinated_pfizer_vaxx_worldcouncilforhealth |
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+ | 1 | ukcitizen2021 - amendments - supranational - pandemic - wgihr | 89 | 1_ukcitizen2021_amendments_supranational_pandemic |
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+ | 2 | constable - arrested - victims - allegations - thamesvalley | 73 | 2_constable_arrested_victims_allegations |
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+ | 3 | bbc - matt - tomorrow - southampton - everywhere | 51 | 3_bbc_matt_tomorrow_southampton |
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+ | 4 | vaccines - mhra - parliamentary - claims - chope | 42 | 4_vaccines_mhra_parliamentary_claims |
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+ | 5 | ukcitizen2021 - councils - mobilise - responses - nottinghamshire | 35 | 5_ukcitizen2021_councils_mobilise_responses |
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+ | 6 | ivermectin - hydroxychloroquine - quercetin - iodine - denied | 26 | 6_ivermectin_hydroxychloroquine_quercetin_iodine |
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+ | 7 | solicitors - mhra - regulatory - bayliss - allegations | 25 | 7_solicitors_mhra_regulatory_bayliss |
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+ | 8 | vaccination - nhs - consent - compulsory - jobsnotjabs | 23 | 8_vaccination_nhs_consent_compulsory |
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+ | 9 | digital - controligarchs - england - passport - currency | 22 | 9_digital_controligarchs_england_passport |
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+
53
+ </details>
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+
55
+ ## Training hyperparameters
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+
57
+ * calculate_probabilities: True
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+ * language: None
59
+ * low_memory: False
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+ * min_topic_size: 10
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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: False
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+ * zeroshot_min_similarity: 0.7
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+ * zeroshot_topic_list: None
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+
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+ ## Framework versions
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+
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+ * Numpy: 1.26.4
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+ * HDBSCAN: 0.8.40
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+ * UMAP: 0.5.7
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+ * Pandas: 2.2.3
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+ * Scikit-Learn: 1.5.2
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+ * Sentence-transformers: 3.3.1
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+ * Transformers: 4.46.3
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+ * Numba: 0.60.0
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+ * Plotly: 5.24.1
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+ * Python: 3.10.12
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+ "zeroshot_min_similarity": 0.7,
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+ }
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