xsum_108_3000_1500_validation

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/xsum_108_3000_1500_validation")

topic_model.get_topic_info()

Topic overview

Number of topics: 3
Number of training documents: 1500

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	yn - ar - yr - mae - wedi	408	-1_yn_ar_yr_mae
0	said - mr - would - people - also	9	0_said_mr_would_people
1	win - game - said - player - team	1083	1_win_game_said_player

Training hyperparameters

calculate_probabilities: True
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: 1.22.4
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: 5.13.1
Python: 3.10.12