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
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