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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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# china-only-mar11 |
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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("Thang203/china-only-mar11") |
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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: 20 |
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* Number of training documents: 847 |
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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 | language - llms - models - data - large | 21 | -1_language_llms_models_data | |
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| 0 | visual - image - multimodal - models - language | 205 | 0_visual_image_multimodal_models | |
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| 1 | embodied - driving - navigation - robot - robotic | 142 | 1_embodied_driving_navigation_robot | |
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| 2 | recommendation - user - recommendations - systems - behavior | 16 | 2_recommendation_user_recommendations_systems | |
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| 3 | agents - social - bots - interactions - ai agents | 16 | 3_agents_social_bots_interactions | |
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| 4 | rl - reinforcement learning - reinforcement - learning - policy | 15 | 4_rl_reinforcement learning_reinforcement_learning | |
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| 5 | molecular - design - property - prediction - gnns | 17 | 5_molecular_design_property_prediction | |
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| 6 | code - code generation - generation - software - programming | 11 | 6_code_code generation_generation_software | |
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| 7 | medical - knowledge - medical knowledge - llms - language | 73 | 7_medical_knowledge_medical knowledge_llms | |
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| 8 | extraction - information extraction - event - information - relation | 16 | 8_extraction_information extraction_event_information | |
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| 9 | safety - llms - robustness - instructions - assurance | 15 | 9_safety_llms_robustness_instructions | |
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| 10 | reasoning - prompting - cot - llms - chainofthought | 14 | 10_reasoning_prompting_cot_llms | |
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| 11 | knowledge - language - knowledge graph - web - kg | 52 | 11_knowledge_language_knowledge graph_web | |
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| 12 | question - answering - commonsense - question answering - knowledge | 17 | 12_question_answering_commonsense_question answering | |
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| 13 | models - language - model - training - language models | 18 | 13_models_language_model_training | |
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| 14 | dialogue - dialog - models - responses - model | 104 | 14_dialogue_dialog_models_responses | |
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| 15 | detection - fake - news - detectors - texts | 31 | 15_detection_fake_news_detectors | |
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| 16 | chatgpt - sentiment - evaluation - sentiment analysis - human | 16 | 16_chatgpt_sentiment_evaluation_sentiment analysis | |
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| 17 | chinese - evaluation - models - language - language models | 22 | 17_chinese_evaluation_models_language | |
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| 18 | translation - arabic - languages - language - models | 26 | 18_translation_arabic_languages_language | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: False |
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* language: english |
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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: 20 |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: True |
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* zeroshot_min_similarity: 0.7 |
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* zeroshot_topic_list: None |
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## Framework versions |
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* Numpy: 1.25.2 |
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* HDBSCAN: 0.8.33 |
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* UMAP: 0.5.5 |
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* Pandas: 1.5.3 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 2.6.1 |
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* Transformers: 4.38.2 |
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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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