industry-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/industry-mar11Top10")

topic_model.get_topic_info()

Topic overview

Number of topics: 10
Number of training documents: 516

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	models - language - data - large - language models	15	-1_models_language_data_large
0	models - model - language - training - language models	169	0_models_model_language_training
1	code - language - models - llms - programming	118	1_code_language_models_llms
2	ai - models - language - dialogue - human	49	2_ai_models_language_dialogue
3	detection - models - text - language - model	47	3_detection_models_text_language
4	multimodal - visual - image - models - generation	32	4_multimodal_visual_image_models
5	agents - language - policy - learning - tasks	24	5_agents_language_policy_learning
6	speech - asr - text - speaker - recognition	22	6_speech_asr_text_speaker
7	reasoning - cot - models - problems - commonsense	21	7_reasoning_cot_models_problems
8	retrieval - information - query - llms - models	19	8_retrieval_information_query_llms

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