Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# general-mar11Top10
+
+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-mar11Top10")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 10
+* 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 - large - language models | 11 | -1_models_language_llms_large | 
+| 0 | models - language - llms - large - model | 3795 | 0_models_language_llms_large | 
+| 1 | models - language - llms - detection - language models | 6668 | 1_models_language_llms_detection | 
+| 2 | ai - chatgpt - education - students - language | 889 | 2_ai_chatgpt_education_students | 
+| 3 | protein - molecular - materials - chemical - drug | 438 | 3_protein_molecular_materials_chemical | 
+| 4 | learning - reinforcement learning - reinforcement - policy - rl | 103 | 4_learning_reinforcement learning_reinforcement_policy | 
+| 5 | math - mathematical - reasoning - problems - models | 100 | 5_math_mathematical_reasoning_problems | 
+| 6 | style - poetry - style transfer - transfer - poems | 91 | 6_style_poetry_style transfer_transfer | 
+| 7 | regression - mathbb - bf - softmax - matrix | 37 | 7_regression_mathbb_bf_softmax | 
+| 8 | recipes - recipe - food - cooking - recipe generation | 14 | 8_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: 10
+* 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": 10,
+  "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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