china-only-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/china-only-mar11Top10")

topic_model.get_topic_info()

Topic overview

Number of topics: 10
Number of training documents: 847

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	language - models - llms - data - large	11	-1_language_models_llms_data
0	visual - multimodal - models - image - language	205	0_visual_multimodal_models_image
1	models - language - model - language models - large	158	1_models_language_model_language models
2	reasoning - knowledge - language - models - llms	151	2_reasoning_knowledge_language_models
3	code - llms - code generation - generation - software	149	3_code_llms_code generation_generation
4	chatgpt - detection - text - sentiment - news	87	4_chatgpt_detection_text_sentiment
5	rl - reinforcement learning - reinforcement - learning - policy	38	5_rl_reinforcement learning_reinforcement_learning
6	recommendation - user - recommendations - systems - users	17	6_recommendation_user_recommendations_systems
7	agents - social - bots - interactions - language	16	7_agents_social_bots_interactions
8	molecular - design - property - prediction - generative pretrained	15	8_molecular_design_property_prediction

Training hyperparameters

calculate_probabilities: False
language: english
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