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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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# close-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/close-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: 4147 |
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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 | models - language - llms - language models - chatgpt | 11 | -1_models_language_llms_language models | |
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| 0 | code - models - language - llms - language models | 1366 | 0_code_models_language_llms | |
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| 1 | medical - clinical - models - llms - language | 840 | 1_medical_clinical_models_llms | |
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| 2 | language - models - human - model - llms | 310 | 2_language_models_human_model | |
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| 3 | bias - llms - language - models - biases | 196 | 3_bias_llms_language_models | |
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| 4 | attacks - adversarial - attack - llms - security | 188 | 4_attacks_adversarial_attack_llms | |
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| 5 | visual - image - multimodal - models - video | 184 | 5_visual_image_multimodal_models | |
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| 6 | text - detection - chatgpt - models - content | 175 | 6_text_detection_chatgpt_models | |
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| 7 | reasoning - language - models - mathematical - logical | 173 | 7_reasoning_language_models_mathematical | |
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| 8 | students - chatgpt - education - learning - programming | 119 | 8_students_chatgpt_education_learning | |
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| 9 | training - models - model - transformer - transformers | 109 | 9_training_models_model_transformer | |
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| 10 | ai - chatgpt - ethical - concerns - research | 106 | 10_ai_chatgpt_ethical_concerns | |
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| 11 | ai - design - creative - generative - ideas | 84 | 11_ai_design_creative_generative | |
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| 12 | financial - sentiment - stock - market - investment | 68 | 12_financial_sentiment_stock_market | |
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| 13 | spatial - urban - models - traffic - large | 52 | 13_spatial_urban_models_traffic | |
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| 14 | materials - chemistry - drug - discovery - molecule | 41 | 14_materials_chemistry_drug_discovery | |
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| 15 | legal - analysis - law - llms - lawyers | 35 | 15_legal_analysis_law_llms | |
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| 16 | recommendation - recommender - recommender systems - systems - recommendations | 35 | 16_recommendation_recommender_recommender systems_systems | |
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| 17 | game - agents - games - llms - playing | 30 | 17_game_agents_games_llms | |
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| 18 | astronomy - scientific - knowledge - galactica - data | 25 | 18_astronomy_scientific_knowledge_galactica | |
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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: 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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