Add BERTopic model

README.md

@@ -0,0 +1,80 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# close-hdscan-april3
+
+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-hdscan-april3")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 11
+* Number of training documents: 5027
+
+<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 - llms - language models - large | 1484 | 0_models_language_llms_language models | 
+| 1 | chatgpt - models - ai - language - llms | 2023 | 1_chatgpt_models_ai_language | 
+| 2 | visual - models - multimodal - image - graph | 589 | 2_visual_models_multimodal_image | 
+| 3 | llms - attacks - attack - models - adversarial | 317 | 3_llms_attacks_attack_models | 
+| 4 | code - generation - code generation - software - models | 273 | 4_code_generation_code generation_software | 
+| 5 | design - creative - ai - ideas - music | 229 | 5_design_creative_ai_ideas | 
+| 6 | robot - dialogue - round - robots - preliminary | 60 | 6_robot_dialogue_round_robots | 
+| 7 | causal - causality - causal reasoning - llms - causal inference | 15 | 7_causal_causality_causal reasoning_llms | 
+| 8 | astronomy - scientific - data - knowledge - generative | 14 | 8_astronomy_scientific_data_knowledge | 
+| 9 | urban - traffic - foundation models - foundation - transportation | 12 | 9_urban_traffic_foundation models_foundation |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: english
+* low_memory: False
+* min_topic_size: 10
+* n_gram_range: (1, 1)
+* nr_topics: 11
+* 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: 0.5.6
+* Pandas: 2.0.3
+* 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": "english",
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": 11,
+  "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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+        [
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+      ],
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+        [
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+        [
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+      ]
+    }
+  }
+}