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MARTINI_enrich_BERTopic_awakenedworlduk

Text Classification
BERTopic
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MARTINI_enrich_BERTopic_awakenedworlduk
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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# MARTINI_enrich_BERTopic_awakenedworlduk

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_awakenedworlduk")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 17
* Number of training documents: 1610

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | trauma - eyes - dr - helps - everyone | 20 | -1_trauma_eyes_dr_helps | 
| 0 | vaccinated - pfizer - jabs - monkeypox - mandates | 882 | 0_vaccinated_pfizer_jabs_monkeypox | 
| 1 | canning - pantry - cooker - survival - lentils | 151 | 1_canning_pantry_cooker_survival | 
| 2 | awakening - consciousness - souls - reality - darkness | 76 | 2_awakening_consciousness_souls_reality | 
| 3 | safeguarding - abused - offences - chancellor - libraries | 54 | 3_safeguarding_abused_offences_chancellor | 
| 4 | brainwashed - bonkers - watched - sovereign - cnn | 49 | 4_brainwashed_bonkers_watched_sovereign | 
| 5 | channel - banned - subscribers - awakened - lol | 49 | 5_channel_banned_subscribers_awakened | 
| 6 | mugwort - echinacea - tinctures - antioxidants - medicinal | 47 | 6_mugwort_echinacea_tinctures_antioxidants | 
| 7 | vegetables - cabbages - radishes - lettuces - sowing | 39 | 7_vegetables_cabbages_radishes_lettuces | 
| 8 | ukraine - shortages - petrol - skyrocketing - electricity | 38 | 8_ukraine_shortages_petrol_skyrocketing | 
| 9 | methylfolate - statins - niacin - magnesium - mitochondria | 37 | 9_methylfolate_statins_niacin_magnesium | 
| 10 | toothpaste - shampoo - rinse - peppermint - ingredients | 34 | 10_toothpaste_shampoo_rinse_peppermint | 
| 11 | nhs - carers - euthanasia - consent - mca | 30 | 11_nhs_carers_euthanasia_consent | 
| 12 | transglutaminase - additives - carcinogenic - meat - benzalkonium | 29 | 12_transglutaminase_additives_carcinogenic_meat | 
| 13 | savetheseeds - gmo - allotment - farmers - shropshire | 29 | 13_savetheseeds_gmo_allotment_farmers | 
| 14 | kwh - gas - charges - october - suppliers | 25 | 14_kwh_gas_charges_october | 
| 15 | propolis - antimicrobial - ointment - wound - candida | 21 | 15_propolis_antimicrobial_ointment_wound |
  
</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