bal_arxiv_scientific_paps_berttopic_model

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("Rchamba/bal_arxiv_scientific_paps_berttopic_model")

topic_model.get_topic_info()

Topic overview

• Number of topics: 14
• Number of training documents: 360

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	data - steganography - secret - probability - method	13	-1_data_steganography_secret_probability
0	sp - intelligence - processing - human - image	35	0_sp_intelligence_processing_human
1	quantum - automata - finite - classical - measurement	64	1_quantum_automata_finite_classical
2	problems - complexity - constraints - symmetry - csps	37	2_problems_complexity_constraints_symmetry
3	logic - computability - cl - edu - www	25	3_logic_computability_cl_edu
4	science - citation - journals - social - communication	24	4_science_citation_journals_social
5	tetraquark - vector - bar - rm - qcd	23	5_tetraquark_vector_bar_rm
6	combinatorial - problems - design - problem - clustering	22	6_combinatorial_problems_design_problem
7	prediction - entropy - model - universal - cc	22	7_prediction_entropy_model_universal
8	notes - informal - spaces - analysis - metric	21	8_notes_informal_spaces_analysis
9	orbital - earth - postnewtonian - effects - artificial	21	9_orbital_earth_postnewtonian_effects
10	keyphrases - word - algorithm - semantic - similarity	20	10_keyphrases_word_algorithm_semantic
11	kernel - gmm - kernels - datasets - classification	17	11_kernel_gmm_kernels_datasets
12	data - ultrametric - ultrametricity - analysis - application	16	12_data_ultrametric_ultrametricity_analysis

Training hyperparameters

• calculate_probabilities: True
• language: english
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: None
• seed_topic_list: None
• top_n_words: 10
• verbose: False
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 2.0.2
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.2
• Scikit-Learn: 1.6.1
• Sentence-transformers: 4.1.0
• Transformers: 4.52.4
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.11.13