general-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/general-mar11Top10")

topic_model.get_topic_info()

Topic overview

• Number of topics: 10
• Number of training documents: 12146

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	models - language - llms - large - language models	11	-1_models_language_llms_large
0	models - language - llms - large - model	3795	0_models_language_llms_large
1	models - language - llms - detection - language models	6668	1_models_language_llms_detection
2	ai - chatgpt - education - students - language	889	2_ai_chatgpt_education_students
3	protein - molecular - materials - chemical - drug	438	3_protein_molecular_materials_chemical
4	learning - reinforcement learning - reinforcement - policy - rl	103	4_learning_reinforcement learning_reinforcement_policy
5	math - mathematical - reasoning - problems - models	100	5_math_mathematical_reasoning_problems
6	style - poetry - style transfer - transfer - poems	91	6_style_poetry_style transfer_transfer
7	regression - mathbb - bf - softmax - matrix	37	7_regression_mathbb_bf_softmax
8	recipes - recipe - food - cooking - recipe generation	14	8_recipes_recipe_food_cooking

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