general-hdscan-april3

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-hdscan-april3")

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
-1	models - language - llms - language models - model	12	-1_models_language_llms_language models
0	models - language - llms - model - language models	2197	0_models_language_llms_model
1	models - training - model - language - quantization	2990	1_models_training_model_language
2	code - generation - code generation - models - llms	430	2_code_generation_code generation_models
3	attacks - models - llms - adversarial - attack	330	3_attacks_models_llms_adversarial
4	language - models - agents - llms - human	328	4_language_models_agents_llms
5	ai - chatgpt - generative - generative ai - chatbots	234	5_ai_chatgpt_generative_generative ai
6	students - education - chatgpt - ai - physics	118	6_students_education_chatgpt_ai
7	music - poetry - generation - lyrics - audio	97	7_music_poetry_generation_lyrics
8	mobile - wireless - devices - generative - network	44	8_mobile_wireless_devices_generative
9	robot - dialogue - round - robots - preliminary round	15	9_robot_dialogue_round_robots

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: 1.24.4
HDBSCAN: 0.8.33
UMAP: 0.5.6
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