Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# close-mar11
+
+This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. 
+BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. 
+
+## Usage 
+
+To use this model, please install BERTopic:
+
+```
+pip install -U bertopic
+```
+
+You can use the model as follows:
+
+```python
+from bertopic import BERTopic
+topic_model = BERTopic.load("Thang203/close-mar11")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 20
+* Number of training documents: 4147
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | models - language - llms - language models - chatgpt | 11 | -1_models_language_llms_language models | 
+| 0 | code - models - language - llms - language models | 1366 | 0_code_models_language_llms | 
+| 1 | medical - clinical - models - llms - language | 840 | 1_medical_clinical_models_llms | 
+| 2 | language - models - human - model - llms | 310 | 2_language_models_human_model | 
+| 3 | bias - llms - language - models - biases | 196 | 3_bias_llms_language_models | 
+| 4 | attacks - adversarial - attack - llms - security | 188 | 4_attacks_adversarial_attack_llms | 
+| 5 | visual - image - multimodal - models - video | 184 | 5_visual_image_multimodal_models | 
+| 6 | text - detection - chatgpt - models - content | 175 | 6_text_detection_chatgpt_models | 
+| 7 | reasoning - language - models - mathematical - logical | 173 | 7_reasoning_language_models_mathematical | 
+| 8 | students - chatgpt - education - learning - programming | 119 | 8_students_chatgpt_education_learning | 
+| 9 | training - models - model - transformer - transformers | 109 | 9_training_models_model_transformer | 
+| 10 | ai - chatgpt - ethical - concerns - research | 106 | 10_ai_chatgpt_ethical_concerns | 
+| 11 | ai - design - creative - generative - ideas | 84 | 11_ai_design_creative_generative | 
+| 12 | financial - sentiment - stock - market - investment | 68 | 12_financial_sentiment_stock_market | 
+| 13 | spatial - urban - models - traffic - large | 52 | 13_spatial_urban_models_traffic | 
+| 14 | materials - chemistry - drug - discovery - molecule | 41 | 14_materials_chemistry_drug_discovery | 
+| 15 | legal - analysis - law - llms - lawyers | 35 | 15_legal_analysis_law_llms | 
+| 16 | recommendation - recommender - recommender systems - systems - recommendations | 35 | 16_recommendation_recommender_recommender systems_systems | 
+| 17 | game - agents - games - llms - playing | 30 | 17_game_agents_games_llms | 
+| 18 | astronomy - scientific - knowledge - galactica - data | 25 | 18_astronomy_scientific_knowledge_galactica |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: None
+* low_memory: False
+* min_topic_size: 10
+* n_gram_range: (1, 1)
+* nr_topics: 20
+* seed_topic_list: None
+* top_n_words: 10
+* verbose: True
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 1.25.2
+* HDBSCAN: 0.8.33
+* UMAP: 0.5.5
+* Pandas: 1.5.3
+* Scikit-Learn: 1.2.2
+* Sentence-transformers: 2.6.1
+* Transformers: 4.38.2
+* Numba: 0.58.1
+* Plotly: 5.15.0
+* Python: 3.10.12

config.json

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+{
+  "calculate_probabilities": false,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": 20,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": true,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null,
+  "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
+}

ctfidf.bin

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topic_embeddings.bin

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