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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# MARTINI_enrich_BERTopic_awakenedworlduk |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. |
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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_awakenedworlduk") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 17 |
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* Number of training documents: 1610 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | trauma - eyes - dr - helps - everyone | 20 | -1_trauma_eyes_dr_helps | |
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| 0 | vaccinated - pfizer - jabs - monkeypox - mandates | 882 | 0_vaccinated_pfizer_jabs_monkeypox | |
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| 1 | canning - pantry - cooker - survival - lentils | 151 | 1_canning_pantry_cooker_survival | |
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| 2 | awakening - consciousness - souls - reality - darkness | 76 | 2_awakening_consciousness_souls_reality | |
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| 3 | safeguarding - abused - offences - chancellor - libraries | 54 | 3_safeguarding_abused_offences_chancellor | |
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| 4 | brainwashed - bonkers - watched - sovereign - cnn | 49 | 4_brainwashed_bonkers_watched_sovereign | |
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| 5 | channel - banned - subscribers - awakened - lol | 49 | 5_channel_banned_subscribers_awakened | |
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| 6 | mugwort - echinacea - tinctures - antioxidants - medicinal | 47 | 6_mugwort_echinacea_tinctures_antioxidants | |
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| 7 | vegetables - cabbages - radishes - lettuces - sowing | 39 | 7_vegetables_cabbages_radishes_lettuces | |
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| 8 | ukraine - shortages - petrol - skyrocketing - electricity | 38 | 8_ukraine_shortages_petrol_skyrocketing | |
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| 9 | methylfolate - statins - niacin - magnesium - mitochondria | 37 | 9_methylfolate_statins_niacin_magnesium | |
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| 10 | toothpaste - shampoo - rinse - peppermint - ingredients | 34 | 10_toothpaste_shampoo_rinse_peppermint | |
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| 11 | nhs - carers - euthanasia - consent - mca | 30 | 11_nhs_carers_euthanasia_consent | |
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| 12 | transglutaminase - additives - carcinogenic - meat - benzalkonium | 29 | 12_transglutaminase_additives_carcinogenic_meat | |
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| 13 | savetheseeds - gmo - allotment - farmers - shropshire | 29 | 13_savetheseeds_gmo_allotment_farmers | |
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| 14 | kwh - gas - charges - october - suppliers | 25 | 14_kwh_gas_charges_october | |
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| 15 | propolis - antimicrobial - ointment - wound - candida | 21 | 15_propolis_antimicrobial_ointment_wound | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: True |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: False |
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* zeroshot_min_similarity: 0.7 |
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* zeroshot_topic_list: None |
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## Framework versions |
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* Numpy: 1.26.4 |
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* HDBSCAN: 0.8.40 |
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* UMAP: 0.5.7 |
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* Pandas: 2.2.3 |
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* Scikit-Learn: 1.5.2 |
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* Sentence-transformers: 3.3.1 |
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* Transformers: 4.46.3 |
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* Numba: 0.60.0 |
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* Plotly: 5.24.1 |
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* Python: 3.10.12 |
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