recipecomments-bertopic

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("daveripper0020/recipecomments-bertopic")

topic_model.get_topic_info()

Topic overview

• Number of topics: 6
• Number of training documents: 386

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	블로그 - 이형 - 많이 - 까지 - 항상	12	-1_블로그_이형_많이_까지
0	잼민 - 춘장 - 인가요 - 뚝딱 - 너무	54	0_잼민_춘장_인가요_뚝딱
1	뚝딱 - 잼민 - 춘장 - 요리 - 수익	151	1_뚝딱_잼민_춘장_요리
2	찜닭 - 마늘 - 레시피 - 넣고 - 간장	77	2_찜닭_마늘_레시피_넣고
3	맛있어요 - 진짜 - 먹었는데 - 간단하고 - 너무	76	3_맛있어요_진짜_먹었는데_간단하고
4	감사합니다 - 합니다 - 자는 - 저리 - 믿는	16	4_감사합니다_합니다_자는_저리

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

Framework versions

• Numpy: 1.23.5
• HDBSCAN: 0.8.33
• UMAP: 0.5.4
• Pandas: 1.5.3
• Scikit-Learn: 1.2.2
• Sentence-transformers: 2.2.2
• Transformers: 4.35.1
• Numba: 0.58.1
• Plotly: 5.15.0
• Python: 3.10.12