Thang203/general_nlp_research_paper

tags:

• —bertopic libraryname: bertopic pipelinetag: text-classification ---

generalnlpresearch_paper

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_nlp_research_paper")

topic_model.get_topic_info()

Topic overview

• —Number of topics: 165
• —Number of training documents: 11000

<details> <summary>Click here for an overview of all topics.</summary>

</details>

Training hyperparameters

• —calculate_probabilities: False
• —language: english
• —low_memory: False
• —mintopicsize: 10
• —ngramrange: (1, 1)
• —nr_topics: None
• —seedtopiclist: None
• —topnwords: 10
• —verbose: True
• —zeroshotminsimilarity: 0.7
• —zeroshottopiclist: None

Framework versions

• —Numpy: 1.25.2
• —HDBSCAN: 0.8.33
• —UMAP: 0.5.6
• —Pandas: 2.0.3
• —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