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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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# BERTopic_hurricane_tweet |
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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("cindyangelira/BERTopic_hurricane_tweet") |
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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: 11 |
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* Number of training documents: 811 |
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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 | medicare - theft - medical - harvey - identity | 1 | -1_medicare_theft_medical_harvey | |
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| 0 | gofundme - donate - houstonstrong - texas - houston | 111 | Weather Updates | |
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| 1 | plaza - txmedcenter - medical - instagram - here | 516 | Relief Efforts | |
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| 2 | hurricaneharvey - houston - harvey - hurricane - houstonflood | 74 | Rescue Operations | |
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| 3 | harvey - hurricane - flooded - houston - tx | 33 | Flooding Reports | |
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| 4 | houston - astros - harvey - snow - houstonstrong | 28 | Storm Damage Reports | |
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| 5 | harvey - reliefforharvey - hurricaneharvey - relief - houstonians | 23 | 5_harvey_reliefforharvey_hurricaneharvey_relief | |
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| 6 | rescued - hurricaneharvey - - - | 14 | 6_rescued_hurricaneharvey__ | |
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| 7 | hurricaneharvey - hurricane - - - | 4 | 7_hurricaneharvey_hurricane__ | |
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| 8 | harvey - flooding - houston - floodwaters - flood | 4 | 8_harvey_flooding_houston_floodwaters | |
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| 9 | houston - hurricaneseason - hurricaneharvey - harvey - weather | 3 | 9_houston_hurricaneseason_hurricaneharvey_harvey | |
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</details> |
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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: 15 |
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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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* zeroshot_min_similarity: 0.85 |
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* zeroshot_topic_list: ['Weather Updates', 'Evacuation Information', 'Emergency Services', 'Relief Efforts', 'Rescue Operations', 'Flooding Reports', 'Traffic and Road Closures', 'Government and Local Authority Announcements', 'Personal Stories and Experiences', 'Storm Damage Reports', 'Others'] |
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## Framework versions |
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* Numpy: 1.26.4 |
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* HDBSCAN: 0.8.38.post1 |
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* UMAP: 0.5.6 |
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* Pandas: 2.2.2 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 3.1.0 |
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* Transformers: 4.44.0 |
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* Numba: 0.60.0 |
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* Plotly: 5.22.0 |
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* Python: 3.10.14 |
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