Add BERTopic model

README.md

@@ -0,0 +1,97 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# topic_docs5000
+
+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("Kamaljp/topic_docs5000")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 30
+* Number of training documents: 5000
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | the - to - of - and - is | 12 | -1_the_to_of_and | 
+| 0 | the - in - to - he - game | 1606 | 0_the_in_to_he | 
+| 1 | the - drive - to - with - for | 450 | 1_the_drive_to_with | 
+| 2 | the - to - that - of - and | 344 | 2_the_to_that_of | 
+| 3 | the - of - and - in - to | 246 | 3_the_of_and_in | 
+| 4 | of - to - the - is - and | 220 | 4_of_to_the_is | 
+| 5 | the - car - and - it - for | 203 | 5_the_car_and_it | 
+| 6 | the - of - that - to - is | 186 | 6_the_of_that_to | 
+| 7 | call - three - bittrolff - uhhhh - test | 172 | 7_call_three_bittrolff_uhhhh | 
+| 8 | the - to - be - of - key | 172 | 8_the_to_be_of | 
+| 9 | the - space - of - and - to | 169 | 9_the_space_of_and | 
+| 10 | the - openwindows - to - window - and | 169 | 10_the_openwindows_to_window | 
+| 11 | for - and - 100 - to - the | 146 | 11_for_and_100_to | 
+| 12 | windows - dos - the - and - to | 132 | 12_windows_dos_the_and | 
+| 13 | the - bike - to - my - was | 105 | 13_the_bike_to_my | 
+| 14 | you - that - to - of - your | 100 | 14_you_that_to_of | 
+| 15 | for - and - to - mail - send | 100 | 15_for_and_to_mail | 
+| 16 | to - that - homosexual - of - is | 94 | 16_to_that_homosexual_of | 
+| 17 | is - that - objective - of - science | 66 | 17_is_that_objective_of | 
+| 18 | printer - fonts - deskjet - hp - the | 56 | 18_printer_fonts_deskjet_hp | 
+| 19 | jpeg - image - gif - file - format | 45 | 19_jpeg_image_gif_file | 
+| 20 | points - graeme - polygon - the - lines | 44 | 20_points_graeme_polygon_the | 
+| 21 | radar - detector - detectors - is - the | 28 | 21_radar_detector_detectors_is | 
+| 22 | hotel - dj - for - ticket - price | 27 | 22_hotel_dj_for_ticket | 
+| 23 | insurance - health - private - the - and | 26 | 23_insurance_health_private_the | 
+| 24 | water - battery - temperature - the - discharge | 21 | 24_water_battery_temperature_the | 
+| 25 | oil - paint - it - wax - and | 17 | 25_oil_paint_it_wax | 
+| 26 | drugs - cocaine - lsd - drug - license | 16 | 26_drugs_cocaine_lsd_drug | 
+| 27 | motif - toolkit - cosecomplient - api - mean | 15 | 27_motif_toolkit_cosecomplient_api | 
+| 28 | maxaxaxaxaxaxaxaxaxaxaxaxaxaxax - entry - entries - rules - we | 13 | 28_maxaxaxaxaxaxaxaxaxaxaxaxaxaxax_entry_entries_rules |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: True
+* language: english
+* low_memory: False
+* min_topic_size: 10
+* n_gram_range: (1, 1)
+* nr_topics: 30
+* seed_topic_list: None
+* top_n_words: 10
+* verbose: True
+
+## Framework versions
+
+* Numpy: 1.22.4
+* HDBSCAN: 0.8.29
+* UMAP: 0.5.3
+* Pandas: 1.5.3
+* Scikit-Learn: 1.2.2
+* Sentence-transformers: 2.2.2
+* Transformers: 4.30.2
+* Numba: 0.56.4
+* Plotly: 5.13.1
+* Python: 3.10.12

config.json

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+{
+  "calculate_probabilities": true,
+  "language": "english",
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": 30,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": true
+}

ctfidf.safetensors

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+size 2154196

topic_embeddings.safetensors

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topics.json

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