close-mar11Top10

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("Thang203/close-mar11Top10")

topic_model.get_topic_info()

Topic overview

• Number of topics: 10
• Number of training documents: 4147

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	models - language - llms - gpt - chatgpt	11	-1_models_language_llms_gpt
0	models - language - llms - large - language models	1366	0_models_language_llms_large
1	ai - chatgpt - students - education - learning	2110	1_ai_chatgpt_students_education
2	llms - attacks - adversarial - attack - security	193	2_llms_attacks_adversarial_attack
3	training - models - transformer - model - large	184	3_training_models_transformer_model
4	financial - legal - models - llms - analysis	106	4_financial_legal_models_llms
5	materials - chemistry - drug - molecule - discovery	87	5_materials_chemistry_drug_molecule
6	recommendation - recommender - recommender systems - user - systems	35	6_recommendation_recommender_recommender systems_user
7	game - agents - games - llms - language	30	7_game_agents_games_llms
8	astronomy - scientific - wave - knowledge - data	25	8_astronomy_scientific_wave_knowledge

Training hyperparameters

• calculate_probabilities: False
• language: None
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: 10
• seed_topic_list: None
• top_n_words: 10
• verbose: True
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 1.25.2
• HDBSCAN: 0.8.33
• UMAP: 0.5.5
• Pandas: 1.5.3
• Scikit-Learn: 1.2.2
• Sentence-transformers: 2.6.1
• Transformers: 4.38.2
• Numba: 0.58.1
• Plotly: 5.15.0
• Python: 3.10.12