academia-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/academia-mar11Top10")

topic_model.get_topic_info()

Topic overview

Number of topics: 10
Number of training documents: 2353

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	models - language - language models - large - llms	10	-1_models_language_language models_large
0	language - models - language models - model - large	678	0_language_models_language models_model
1	code - software - llms - models - generation	1117	1_code_software_llms_models
2	chatgpt - ai - education - students - learning	168	2_chatgpt_ai_education_students
3	adversarial - attacks - models - privacy - attack	127	3_adversarial_attacks_models_privacy
4	legal - financial - models - patent - language	73	4_legal_financial_models_patent
5	learning - reinforcement learning - reinforcement - rl - policy	55	5_learning_reinforcement learning_reinforcement_rl
6	training - quantization - memory - transformer - models	54	6_training_quantization_memory_transformer
7	materials - molecular - molecule - discovery - models	52	7_materials_molecular_molecule_discovery
8	surprisal - reading - models - language - language models	19	8_surprisal_reading_models_language

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