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wongzien2000
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model_sbs

Text Classification
BERTopic
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Add BERTopic model

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Files changed (6) hide show
  1. README.md +76 -0
  2. config.json +17 -0
  3. ctfidf.bin +3 -0
  4. ctfidf_config.json +0 -0
  5. topic_embeddings.bin +3 -0
  6. topics.json +2687 -0
README.md ADDED
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+
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+ ---
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+ tags:
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+ - bertopic
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+ library_name: bertopic
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+ pipeline_tag: text-classification
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+ ---
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+
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+ # model_sbs
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+
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+ This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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+ BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
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+
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+ ## Usage
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+
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+ To use this model, please install BERTopic:
17
+
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+ ```
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+ pip install -U bertopic
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+ ```
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+
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+ You can use the model as follows:
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+
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+ ```python
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+ from bertopic import BERTopic
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+ topic_model = BERTopic.load("wongzien2000/model_sbs")
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+
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+ topic_model.get_topic_info()
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+ ```
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+
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+ ## Topic overview
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+
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+ * Number of topics: 7
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+ * Number of training documents: 1396
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+
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+ <details>
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+ <summary>Click here for an overview of all topics.</summary>
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+
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+ | Topic ID | Topic Keywords | Topic Frequency | Label |
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+ |----------|----------------|-----------------|-------|
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+ | -1 | training - just - greg - videos - people | 57 | -1_training_just_greg_videos |
42
+ | 0 | sets - muscle - volume - week - training | 151 | 0_sets_muscle_volume_week |
43
+ | 1 | bench - press - shoulder - ohp - bar | 583 | 1_bench_press_shoulder_ohp |
44
+ | 2 | greg - great - answer - thanks - thank | 222 | 2_greg_great_answer_thanks |
45
+ | 3 | mike - dr - dr mike - eric - dr pak | 187 | 3_mike_dr_dr mike_eric |
46
+ | 4 | science - based - science based - studies - brad | 135 | 4_science_based_science based_studies |
47
+ | 5 | music - background - background music - audio - merrily | 61 | 5_music_background_background music_audio |
48
+
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+ </details>
50
+
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+ ## Training hyperparameters
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+
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+ * calculate_probabilities: True
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+ * language: None
55
+ * low_memory: False
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+ * min_topic_size: 10
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+ * n_gram_range: (1, 1)
58
+ * nr_topics: None
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+ * seed_topic_list: None
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+ * top_n_words: 10
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+ * verbose: True
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+ * zeroshot_min_similarity: 0.7
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+ * zeroshot_topic_list: None
64
+
65
+ ## Framework versions
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+
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+ * Numpy: 2.0.2
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+ * HDBSCAN: 0.8.40
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+ * UMAP: 0.5.7
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+ * Pandas: 2.2.2
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+ * Scikit-Learn: 1.6.1
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+ * Sentence-transformers: 3.4.1
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+ * Transformers: 4.50.2
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+ * Numba: 0.60.0
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+ * Plotly: 5.24.1
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+ * Python: 3.11.11
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+ "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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+ }
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