us-only-mar11

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/us-only-mar11")

topic_model.get_topic_info()

Topic overview

• Number of topics: 20
• Number of training documents: 1908

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	models - language - model - language models - llms	10	-1_models_language_model_language models
0	models - language - reasoning - language models - large	616	0_models_language_reasoning_language models
1	code - llms - language - models - programming	467	1_code_llms_language_models
2	learning - reinforcement - reinforcement learning - planning - rl	139	2_learning_reinforcement_reinforcement learning_planning
3	clinical - medical - models - language - data	92	3_clinical_medical_models_language
4	language - models - language models - llms - scaling	86	4_language_models_language models_llms
5	summarization - event - generation - events - text	75	5_summarization_event_generation_events
6	dialogue - dialog - systems - conversational - conversations	59	6_dialogue_dialog_systems_conversational
7	text - adversarial - attacks - detection - models	58	7_text_adversarial_attacks_detection
8	bias - biases - social - gender - models	52	8_bias_biases_social_gender
9	ai - chatgpt - ethical - artificial intelligence - intelligence	49	9_ai_chatgpt_ethical_artificial intelligence
10	education - students - programming - educational - questions	49	10_education_students_programming_educational
11	privacy - private - federated - attacks - models	37	11_privacy_private_federated_attacks
12	speech - audio - asr - speech recognition - recognition	21	12_speech_audio_asr_speech recognition
13	materials - chemistry - chemical - molecular - model	20	13_materials_chemistry_chemical_molecular
14	recommendation - user - item - reviews - news	20	14_recommendation_user_item_reviews
15	financial - sentiment - stock - data - market	17	15_financial_sentiment_stock_data
16	game - games - state - generate - state information	15	16_game_games_state_generate
17	legal - law - argumentative - court - standards	14	17_legal_law_argumentative_court
18	metadata - language - keyphrase - large - user intents	12	18_metadata_language_keyphrase_large

Training hyperparameters

• calculate_probabilities: False
• language: english
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: 20
• 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: 2.5.1
• Transformers: 4.38.2
• Numba: 0.58.1
• Plotly: 5.15.0
• Python: 3.10.12