metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
MARTINI_enrich_BERTopic_healingivermectin
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("AIDA-UPM/MARTINI_enrich_BERTopic_healingivermectin")
topic_model.get_topic_info()
Topic overview
- Number of topics: 5
- Number of training documents: 260
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | ivermectin - fenbendazole - antitumor - cures - cyanide | 22 | -1_ivermectin_fenbendazole_antitumor_cures |
| 0 | ivermectin - healing - bacterial - hcq - honey | 107 | 0_ivermectin_healing_bacterial_hcq |
| 1 | parasitic - pinworms - cancers - schizophrenic - nematode | 54 | 1_parasitic_pinworms_cancers_schizophrenic |
| 2 | vaccines - pfizer - conspiracy - deaths - mmr | 41 | 2_vaccines_pfizer_conspiracy_deaths |
| 3 | download - recordings - telegram - 700mb - hello | 36 | 3_download_recordings_telegram_700mb |
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