general-april-3

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/general-april-3")

topic_model.get_topic_info()

Topic overview

• Number of topics: 11
• Number of training documents: 6795

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
0	financial - legal - summarization - models - llms	251	0_financial_legal_summarization_models
1	reasoning - models - language - llms - language models	887	1_reasoning_models_language_llms
2	models - llms - attacks - attack - language	467	2_models_llms_attacks_attack
3	models - model - training - language - inference	528	3_models_model_training_language
4	language - models - llms - human - model	638	4_language_models_llms_human
5	medical - clinical - models - language - llms	524	5_medical_clinical_models_language
6	visual - multimodal - models - image - language	630	6_visual_multimodal_models_image
7	code - generation - code generation - llms - models	417	7_code_generation_code generation_llms
8	models - language - language models - model - llms	1232	8_models_language_language models_model
9	chatgpt - ai - students - education - generative	542	9_chatgpt_ai_students_education
10	models - chatgpt - language - ai - llms	679	10_models_chatgpt_language_ai

Training hyperparameters

• calculate_probabilities: False
• language: english
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: 11
• 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: 0.5.6
• Pandas: 2.0.3
• 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