|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # BERTopic |
|
|
| 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("Jerado/BERTopic") |
| |
| topic_model.get_topic_info() |
| ``` |
|
|
| ## Topic overview |
|
|
| * Number of topics: 17 |
| * Number of training documents: 1000 |
|
|
| <details> |
| <summary>Click here for an overview of all topics.</summary> |
| |
| | Topic ID | Topic Keywords | Topic Frequency | Label | |
| |----------|----------------|-----------------|-------| |
| | -1 | theism - much - way - think - just | 15 | -1_theism_much_way_think | |
| | 0 | nhl - playoffs - rangers - hockey - league | 304 | 0_nhl_playoffs_rangers_hockey | |
| | 1 | performance - ram - drivers - monitor - speed | 92 | 1_performance_ram_drivers_monitor | |
| | 2 | x11r5 - hyperhelp - windows - pc - application | 82 | 2_x11r5_hyperhelp_windows_pc | |
| | 3 | dos - windows - harddisk - disk - software | 82 | 3_dos_windows_harddisk_disk | |
| | 4 | amp - amps - amplifier - ampere - current | 75 | 4_amp_amps_amplifier_ampere | |
| | 5 | scripture - christians - sin - bible - commandment | 44 | 5_scripture_christians_sin_bible | |
| | 6 | patients - biological - medicine - studies - doctors | 41 | 6_patients_biological_medicine_studies | |
| | 7 | nasa - solar - space - shuttle - orbiting | 39 | 7_nasa_solar_space_shuttle | |
| | 8 | armenians - armenian - armenia - turks - genocide | 38 | 8_armenians_armenian_armenia_turks | |
| | 9 | guns - gun - amendment - constitution - laws | 36 | 9_guns_gun_amendment_constitution | |
| | 10 | - - - - | 33 | 10____ | |
| | 11 | motorcycle - bikes - cobralinks - bike - riding | 32 | 11_motorcycle_bikes_cobralinks_bike | |
| | 12 | encryption - security - encrypted - privacy - secure | 24 | 12_encryption_security_encrypted_privacy | |
| | 13 | contacted - address - mail - contact - email | 23 | 13_contacted_address_mail_contact | |
| | 14 | paganism - faith - christianity - christians - atheists | 21 | 14_paganism_faith_christianity_christians | |
| | 15 | action - fbi - batf - war - president | 19 | 15_action_fbi_batf_war | |
| |
| </details> |
|
|
| ## Training hyperparameters |
|
|
| * calculate_probabilities: False |
| * language: english |
| * low_memory: False |
| * min_topic_size: 10 |
| * n_gram_range: (1, 1) |
| * nr_topics: None |
| * seed_topic_list: [['drug', 'cancer', 'drugs', 'doctor'], ['windows', 'drive', 'dos', 'file'], ['space', 'launch', 'orbit', 'lunar']] |
| * top_n_words: 10 |
| * verbose: False |
| * zeroshot_min_similarity: 0.7 |
| * zeroshot_topic_list: None |
| |
| ## Framework versions |
| |
| * Numpy: 1.23.5 |
| * HDBSCAN: 0.8.33 |
| * UMAP: 0.5.6 |
| * Pandas: 2.0.3 |
| * Scikit-Learn: 1.2.2 |
| * Sentence-transformers: 2.7.0 |
| * Transformers: 4.40.1 |
| * Numba: 0.58.1 |
| * Plotly: 5.15.0 |
| * Python: 3.10.12 |
| |
