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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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# recipecomments-bertopic |
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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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## 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("daveripper0020/recipecomments-bertopic") |
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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: 6 |
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* Number of training documents: 386 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | ๋ธ๋ก๊ทธ - ์ดํ - ๋ง์ด - ๊น์ง - ํญ์ | 12 | -1_๋ธ๋ก๊ทธ_์ดํ_๋ง์ด_๊น์ง | |
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| 0 | ์ผ๋ฏผ - ์ถ์ฅ - ์ธ๊ฐ์ - ๋๋ฑ - ๋๋ฌด | 54 | 0_์ผ๋ฏผ_์ถ์ฅ_์ธ๊ฐ์_๋๋ฑ | |
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| 1 | ๋๋ฑ - ์ผ๋ฏผ - ์ถ์ฅ - ์๋ฆฌ - ์์ต | 151 | 1_๋๋ฑ_์ผ๋ฏผ_์ถ์ฅ_์๋ฆฌ | |
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| 2 | ์ฐ๋ญ - ๋ง๋ - ๋ ์ํผ - ๋ฃ๊ณ - ๊ฐ์ฅ | 77 | 2_์ฐ๋ญ_๋ง๋_๋ ์ํผ_๋ฃ๊ณ | |
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| 3 | ๋ง์์ด์ - ์ง์ง - ๋จน์๋๋ฐ - ๊ฐ๋จํ๊ณ - ๋๋ฌด | 76 | 3_๋ง์์ด์_์ง์ง_๋จน์๋๋ฐ_๊ฐ๋จํ๊ณ | |
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| 4 | ๊ฐ์ฌํฉ๋๋ค - ํฉ๋๋ค - ์๋ - ์ ๋ฆฌ - ๋ฏฟ๋ | 16 | 4_๊ฐ์ฌํฉ๋๋ค_ํฉ๋๋ค_์๋_์ ๋ฆฌ | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: True |
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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: 1.5.3 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 2.2.2 |
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* Transformers: 4.35.1 |
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* Numba: 0.58.1 |
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* Plotly: 5.15.0 |
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* Python: 3.10.12 |
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