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KingKazma
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xsum_123_3000_1500_test

Text Classification
BERTopic
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  1. README.md +76 -0
  2. config.json +15 -0
  3. topic_embeddings.safetensors +3 -0
  4. topics.json +1956 -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
6
+ pipeline_tag: text-classification
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+ ---
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+
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+ # xsum_123_3000_1500_test
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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.
13
+
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+ ## Usage
15
+
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+ To use this model, please install BERTopic:
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+
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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("KingKazma/xsum_123_3000_1500_test")
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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: 9
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+ * Number of training documents: 1500
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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 | yn - game - win - player - league | 12 | -1_yn_game_win_player |
42
+ | 0 | said - mr - would - people - also | 142 | 0_said_mr_would_people |
43
+ | 1 | right - box - win - foul - half | 1033 | 1_right_box_win_foul |
44
+ | 2 | race - world - sport - champion - team | 118 | 2_race_world_sport_champion |
45
+ | 3 | film - prize - album - book - said | 60 | 3_film_prize_album_book |
46
+ | 4 | league - season - appearance - club - transfer | 49 | 4_league_season_appearance_club |
47
+ | 5 | cricket - england - test - wicket - captain | 41 | 5_cricket_england_test_wicket |
48
+ | 6 | wales - rugby - side - ospreys - team | 27 | 6_wales_rugby_side_ospreys |
49
+ | 7 | egypt - morocco - cup - uganda - football | 18 | 7_egypt_morocco_cup_uganda |
50
+
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+ </details>
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+
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+ ## Training hyperparameters
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+
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+ * calculate_probabilities: True
56
+ * language: english
57
+ * low_memory: False
58
+ * min_topic_size: 10
59
+ * n_gram_range: (1, 1)
60
+ * nr_topics: None
61
+ * seed_topic_list: None
62
+ * top_n_words: 10
63
+ * verbose: False
64
+
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+ ## Framework versions
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+
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+ * Numpy: 1.22.4
68
+ * HDBSCAN: 0.8.33
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+ * UMAP: 0.5.3
70
+ * Pandas: 1.5.3
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+ * Scikit-Learn: 1.2.2
72
+ * Sentence-transformers: 2.2.2
73
+ * Transformers: 4.31.0
74
+ * Numba: 0.57.1
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+ * Plotly: 5.13.1
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+ * Python: 3.10.12
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+ {
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+ "calculate_probabilities": true,
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+ "low_memory": false,
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+ "min_topic_size": 10,
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+ "n_gram_range": [
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+ 1,
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+ ],
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+ "nr_topics": null,
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+ "seed_topic_list": null,
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+ "top_n_words": 10,
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+ "verbose": false,
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+ "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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+ }
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+ oid sha256:5f3df68c4d8129e6701d902e9ac9c8616c01ccbcfbbe0a6053a420553f59703d
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+ size 13912
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