metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
cnn_dailymail_6789_2000_1000_v1_train
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("KingKazma/cnn_dailymail_6789_2000_1000_v1_train")
topic_model.get_topic_info()
Topic overview
- Number of topics: 3
- Number of training documents: 2000
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | second - rider - minute - roma - teammate | 268 | -1_second_rider_minute_roma |
| 0 | said - one - year - would - people | 1 | 0_said_one_year_would |
| 1 | player - game - world - first - club | 1731 | 1_player_game_world_first |
Training hyperparameters
- calculate_probabilities: False
- language: english
- 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: 1.23.5
- HDBSCAN: 0.8.33
- UMAP: 0.5.3
- Pandas: 1.5.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 2.2.2
- Transformers: 4.31.0
- Numba: 0.57.1
- Plotly: 5.15.0
- Python: 3.10.12