Add BERTopic model

README.md

@@ -0,0 +1,81 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# topic_modelling_football
+
+This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. 
+BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. 
+
+## Usage 
+
+To use this model, please install BERTopic:
+
+```
+pip install -U bertopic
+```
+
+You can use the model as follows:
+
+```python
+from bertopic import BERTopic
+topic_model = BERTopic.load("riccardopresti99/topic_modelling_football")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 14
+* Number of training documents: 350
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | tournament - competition - leaving - final - compete | 16 | -1_tournament_competition_leaving_final | 
+| 0 | video - games - football - players - experience | 10 | 0_video_games_football_players | 
+| 1 | supporters - atmosphere - stadiums - football - create | 48 | 1_supporters_atmosphere_stadiums_football | 
+| 2 | physiotherapists - injury - injuries - players - prevention | 32 | 2_physiotherapists_injury_injuries_players | 
+| 3 | united - film - football - war - story | 29 | 3_united_film_football_war | 
+| 4 | ronaldo - ability - scoring - aspiring - one | 26 | 4_ronaldo_ability_scoring_aspiring | 
+| 5 | scandals - illegal - officials - within - concerns | 26 | 5_scandals_illegal_officials_within | 
+| 6 | healthy - footballers - energy - supports - performance | 25 | 6_healthy_footballers_energy_supports | 
+| 7 | strikers - striker - scoring - teammates - goals | 25 | 7_strikers_striker_scoring_teammates | 
+| 8 | investors - stock - stocks - market - club | 25 | 8_investors_stock_stocks_market | 
+| 9 | women - football - girls - equal - sport | 25 | 9_women_football_girls_equal | 
+| 10 | serie - milan - league - inter - italian | 23 | 10_serie_milan_league_inter | 
+| 11 | champions - league - european - club - uefa | 22 | 11_champions_league_european_club | 
+| 12 | cup - world - fifa - held - trophy | 18 | 12_cup_world_fifa_held |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: None
+* low_memory: False
+* min_topic_size: 10
+* n_gram_range: (1, 1)
+* nr_topics: None
+* seed_topic_list: None
+* top_n_words: 10
+* verbose: True
+
+## Framework versions
+
+* Numpy: 1.23.5
+* HDBSCAN: 0.8.29
+* UMAP: 0.5.3
+* Pandas: 1.5.3
+* Scikit-Learn: 1.2.2
+* Sentence-transformers: 2.2.2
+* Transformers: 4.26.1
+* Numba: 0.56.4
+* Plotly: 5.13.1
+* Python: 3.10.10

config.json

@@ -0,0 +1,14 @@
+{
+  "calculate_probabilities": false,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": null,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": true
+}

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20e3ab58a26cfb5068ef20892f80ba6e20d81e5d26ade8951ebb4b6bb76eecba
+size 21592

topics.json

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+{
+  "topic_representations": {
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+      [
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+        0.21812133676028866
+      ],
+      [
+        "competition",
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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