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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# saxa3-capstone |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. |
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This text-classification model was modeled from The Department of Veterans Affairs Advisory Committee on Women Veterans biennial reports, from |
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a period of 1996 - 2020. It was specifically generated from recommendations used within each of the reports. |
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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("magica1/saxa3-capstone") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 24 |
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* Number of training documents: 1602 |
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## Training hyperparameters |
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* calculate_probabilities: False |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: False |
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## Framework versions |
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* Numpy: 1.23.5 |
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* HDBSCAN: 0.8.33 |
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* UMAP: 0.5.4 |
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* Pandas: 2.1.2 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 2.2.2 |
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* Transformers: 4.35.0 |
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* Numba: 0.56.4 |
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* Plotly: 5.15.0 |
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* Python: 3.10.12 |
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