Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# general-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/general-mar11")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 20
+* Number of training documents: 12146
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | models - language - llms - language models - large | 11 | -1_models_language_llms_language models | 
+| 0 | models - language - language models - model - llms | 3795 | 0_models_language_language models_model | 
+| 1 | code - language - llms - models - large | 3252 | 1_code_language_llms_models | 
+| 2 | visual - image - models - multimodal - video | 1236 | 2_visual_image_models_multimodal | 
+| 3 | detection - text - models - attacks - adversarial | 724 | 3_detection_text_models_attacks | 
+| 4 | bias - models - biases - llms - gender | 610 | 4_bias_models_biases_llms | 
+| 5 | medical - clinical - models - language - llms | 451 | 5_medical_clinical_models_language | 
+| 6 | legal - financial - sentiment - models - language | 437 | 6_legal_financial_sentiment_models | 
+| 7 | ai - chatgpt - generative - design - generative ai | 377 | 7_ai_chatgpt_generative_design | 
+| 8 | privacy - data - private - models - federated | 274 | 8_privacy_data_private_models | 
+| 9 | students - education - chatgpt - ai - student | 201 | 9_students_education_chatgpt_ai | 
+| 10 | driving - autonomous - autonomous driving - traffic - spatial | 164 | 10_driving_autonomous_autonomous driving_traffic | 
+| 11 | protein - molecular - materials - chemical - drug | 113 | 11_protein_molecular_materials_chemical | 
+| 12 | reinforcement learning - reinforcement - learning - rl - policy | 103 | 12_reinforcement learning_reinforcement_learning_rl | 
+| 13 | math - mathematical - problems - reasoning - theorem | 100 | 13_math_mathematical_problems_reasoning | 
+| 14 | vulnerability - code - vulnerabilities - security - log | 91 | 14_vulnerability_code_vulnerabilities_security | 
+| 15 | forecasting - climate - time series - data - carbon | 78 | 15_forecasting_climate_time series_data | 
+| 16 | style - poetry - style transfer - transfer - poems | 78 | 16_style_poetry_style transfer_transfer | 
+| 17 | regression - matrix - softmax - mathbbrn - bf | 37 | 17_regression_matrix_softmax_mathbbrn | 
+| 18 | recipes - recipe - food - cooking - dietary | 14 | 18_recipes_recipe_food_cooking |
+  
+</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: 2.5.1
+* 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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