metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
topic_docs5000
This is a 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:
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
Click here for an overview of all topics.
| 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 |
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