---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# MARTINI_enrich_BERTopic_Anti_Feminist_Gang

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("AIDA-UPM/MARTINI_enrich_BERTopic_Anti_Feminist_Gang")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 42
* Number of training documents: 6617

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | rape - feminst - पर - raha - india | 21 | -1_rape_feminst_पर_raha | 
| 0 | feminsts - misogynist - ladkiyon - isliye - chanakya | 3753 | 0_feminsts_misogynist_ladkiyon_isliye | 
| 1 | ladkiyan - bhai - dekho - durga - priyadarshani | 270 | 1_ladkiyan_bhai_dekho_durga | 
| 2 | hindus - jainism - sanatana - atharveda - atheistic | 234 | 2_hindus_jainism_sanatana_atharveda | 
| 3 | promiscuity - virginity - marry - oxytocin - brahmachraya | 212 | 3_promiscuity_virginity_marry_oxytocin | 
| 4 | antihindu - bjp - jihad - hitler - modi | 204 | 4_antihindu_bjp_jihad_hitler | 
| 5 | shaadi - bhai - vivah - rahul - brahmachraya | 141 | 5_shaadi_bhai_vivah_rahul | 
| 6 | hypergamy - attractive - strong - bodybuilder - males | 126 | 6_hypergamy_attractive_strong_bodybuilder | 
| 7 | brainwashed - anyway - arguments - rekt - dumb | 120 | 7_brainwashed_anyway_arguments_rekt | 
| 8 | feminazi - misogynistic - bitches - shit - brainwash | 112 | 8_feminazi_misogynistic_bitches_shit | 
| 9 | bhaasha - khudaai - pehle - tujhe - samjega | 96 | 9_bhaasha_khudaai_pehle_tujhe | 
| 10 | tumhare - dikhayega - chaar - dad - mohalla | 90 | 10_tumhare_dikhayega_chaar_dad | 
| 11 | gendered - dysphoria - stereotypes - socity - natural | 90 | 11_gendered_dysphoria_stereotypes_socity | 
| 12 | feminsts - rape - men - punishment - biased | 81 | 12_feminsts_rape_men_punishment | 
| 13 | rape - allegations - rajveer - falsely - bail | 79 | 13_rape_allegations_rajveer_falsely | 
| 14 | grp - pleasebahar - bnayega - kharab - chodu | 63 | 14_grp_pleasebahar_bnayega_kharab | 
| 15 | equality - patriarchy - samaj - haramipanti - ladies | 63 | 15_equality_patriarchy_samaj_haramipanti | 
| 16 | policewala - safai - shekhar - ladkiys - khilooo | 60 | 16_policewala_safai_shekhar_ladkiys | 
| 17 | housewife - equal - hardwork - dhundti - income | 59 | 17_housewife_equal_hardwork_dhundti | 
| 18 | lgbtq - homophobic - marginalises - hilaunga - btayege | 54 | 18_lgbtq_homophobic_marginalises_hilaunga | 
| 19 | fraud - cases - patekar - badhte - victims | 50 | 19_fraud_cases_patekar_badhte | 
| 20 | porn - belissa - empowered - sister - aapka | 45 | 20_porn_belissa_empowered_sister | 
| 21 | bhai - mujse - rishtedaro - rudrani - madhvi | 42 | 21_bhai_mujse_rishtedaro_rudrani | 
| 22 | rape - uttarakhand - bachayega - victims - mujhee | 41 | 22_rape_uttarakhand_bachayega_victims | 
| 23 | alimony - dowry - shadi - wali - sudhregi | 41 | 23_alimony_dowry_shadi_wali | 
| 24 | crimee - women - kabhi - victim - isliye | 40 | 24_crimee_women_kabhi_victim | 
| 25 | ladkiya - jagha - manipur - reserved - castism | 37 | 25_ladkiya_jagha_manipur_reserved | 
| 26 | ramji - joked - muslims - madhuri - smjhaarha | 35 | 26_ramji_joked_muslims_madhuri | 
| 27 | vedio - youtube - jagrukta - bharmachari - anyaye | 33 | 27_vedio_youtube_jagrukta_bharmachari | 
| 28 | muslims - quranic - muhammad - superstitions - haleem | 32 | 28_muslims_quranic_muhammad_superstitions | 
| 29 | rape - consensual - ladki - sakta - लय | 31 | 29_rape_consensual_ladki_sakta | 
| 30 | pornography - masturbating - objectification - watching - empowering | 30 | 30_pornography_masturbating_objectification_watching | 
| 31 | genders - unequally - compulsory - leadership - segregates | 26 | 31_genders_unequally_compulsory_leadership | 
| 32 | misogynist - metoo - barkha - deepika - false | 24 | 32_misogynist_metoo_barkha_deepika | 
| 33 | ladkiyi - teachers - dekho - badha - homework | 24 | 33_ladkiyi_teachers_dekho_badha | 
| 34 | ladkiyi - rakhegi - parinaam - duniya - nature | 24 | 34_ladkiyi_rakhegi_parinaam_duniya | 
| 35 | argue - tumhari - sahaanubhooti - baar - kalank | 24 | 35_argue_tumhari_sahaanubhooti_baar | 
| 36 | violences - casemai - surpanakha - daughters - vajh | 23 | 36_violences_casemai_surpanakha_daughters | 
| 37 | naukri - dhoondti - chaprasi - jobless - jaataa | 22 | 37_naukri_dhoondti_chaprasi_jobless | 
| 38 | slaves - islamic - muhammed - racism - african | 22 | 38_slaves_islamic_muhammed_racism | 
| 39 | twitter - influencer - plz - kangana - bjp | 22 | 39_twitter_influencer_plz_kangana | 
| 40 | bollywood - amitabh - salman - varun - villian | 21 | 40_bollywood_amitabh_salman_varun |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* 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: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.26.4
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.3
* Scikit-Learn: 1.5.2
* Sentence-transformers: 3.3.1
* Transformers: 4.46.3
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.10.12