|
|
| --- |
| 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 |
| |
