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Add BERTopic model

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Files changed (4) hide show
  1. README.md +82 -0
  2. config.json +15 -0
  3. topic_embeddings.safetensors +3 -0
  4. topics.json +2250 -0
README.md ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
2
+ ---
3
+ tags:
4
+ - bertopic
5
+ library_name: bertopic
6
+ pipeline_tag: text-classification
7
+ ---
8
+
9
+ # cnn_dailymail_6789_3000_1500_test
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
+
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+ ## Usage
15
+
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+ To use this model, please install BERTopic:
17
+
18
+ ```
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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/cnn_dailymail_6789_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: 15
34
+ * Number of training documents: 1500
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+
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+ <details>
37
+ <summary>Click here for an overview of all topics.</summary>
38
+
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+ | Topic ID | Topic Keywords | Topic Frequency | Label |
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+ |----------|----------------|-----------------|-------|
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+ | -1 | season - league - liverpool - player - club | 12 | -1_season_league_liverpool_player |
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+ | 0 | said - one - police - year - people | 151 | 0_said_one_police_year |
43
+ | 1 | madrid - league - champions - real - barcelona | 1070 | 1_madrid_league_champions_real |
44
+ | 2 | chelsea - united - manchester - van - league | 55 | 2_chelsea_united_manchester_van |
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+ | 3 | fight - pacquiao - ticket - mayweather - boxing | 43 | 3_fight_pacquiao_ticket_mayweather |
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+ | 4 | race - hamilton - rosberg - marathon - vettel | 28 | 4_race_hamilton_rosberg_marathon |
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+ | 5 | england - cook - pietersen - cricket - test | 25 | 5_england_cook_pietersen_cricket |
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+ | 6 | villa - sherwood - benteke - aston - game | 19 | 6_villa_sherwood_benteke_aston |
49
+ | 7 | try - minute - huddersfield - bristol - league | 17 | 7_try_minute_huddersfield_bristol |
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+ | 8 | celtic - scottish - rangers - game - inverness | 15 | 8_celtic_scottish_rangers_game |
51
+ | 9 | mcilroy - masters - woods - augusta - golf | 14 | 9_mcilroy_masters_woods_augusta |
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+ | 10 | arsenal - wenger - arsenals - reading - coquelin | 14 | 10_arsenal_wenger_arsenals_reading |
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+ | 11 | newcastle - sunderland - advocaat - game - rangers | 13 | 11_newcastle_sunderland_advocaat_game |
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+ | 12 | cup - toulon - saracens - clermont - bath | 12 | 12_cup_toulon_saracens_clermont |
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+ | 13 | stadium - stand - fan - fa - final | 12 | 13_stadium_stand_fan_fa |
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+
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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
62
+ * language: english
63
+ * low_memory: False
64
+ * min_topic_size: 10
65
+ * n_gram_range: (1, 1)
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+ * nr_topics: None
67
+ * seed_topic_list: None
68
+ * top_n_words: 10
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+ * verbose: False
70
+
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+ ## Framework versions
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+
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+ * Numpy: 1.22.4
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+ * HDBSCAN: 0.8.33
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+ * UMAP: 0.5.3
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+ * Pandas: 1.5.3
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+ * Scikit-Learn: 1.2.2
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+ * Sentence-transformers: 2.2.2
79
+ * Transformers: 4.31.0
80
+ * Numba: 0.56.4
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+ * Plotly: 5.13.1
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+ * Python: 3.10.6
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+ {
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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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+ }
topic_embeddings.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c9cb6bec9bdee7db7c9d6acd83e48673b9793f1e87b8c4768e6f0d3be43eef00
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