metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
string2-string
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("syntag/string2-string")
topic_model.get_topic_info()
Topic overview
- Number of topics: 4
- Number of training documents: 20
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| 0 | life - make - adulting - worm - gives | 7 | 0_life_make_adulting_worm |
| 1 | like - bar - walk - matter - coding | 7 | 1_like_bar_walk_matter |
| 2 | break - version - vacation - told - succeed | 3 | 2_break_version_vacation_told |
| 3 | don - skeletons - shame - scientists - parallel | 3 | 3_don_skeletons_shame_scientists |
Training hyperparameters
- calculate_probabilities: False
- 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
Framework versions
- Numpy: 1.24.4
- HDBSCAN: 0.8.33
- UMAP: 0.5.4
- Pandas: 2.0.3
- Scikit-Learn: 1.3.1
- Sentence-transformers: 2.2.2
- Transformers: 4.34.1
- Numba: 0.58.1
- Plotly: 5.17.0
- Python: 3.10.12