Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# topic_model_general_normal_april8
+
+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/topic_model_general_normal_april8")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 80
+* Number of training documents: 6795
+
+<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 | translation - language - models - data - generation | 2010 | 0_translation_language_models_data | 
+| 1 | visual - multimodal - image - images - video | 510 | 1_visual_multimodal_image_images | 
+| 2 | reasoning - math - cot - mathematical - problems | 432 | 2_reasoning_math_cot_mathematical | 
+| 3 | attacks - attack - adversarial - safety - jailbreak | 340 | 3_attacks_attack_adversarial_safety | 
+| 4 | medical - clinical - biomedical - health - healthcare | 318 | 4_medical_clinical_biomedical_health | 
+| 5 | code - code generation - generation - programming - software | 303 | 5_code_code generation_generation_programming | 
+| 6 | students - education - ai - chatgpt - student | 153 | 6_students_education_ai_chatgpt | 
+| 7 | robot - planning - robots - navigation - robotic | 110 | 7_robot_planning_robots_navigation | 
+| 8 | dialogue - taskoriented - dialog - dialogue systems - systems | 107 | 8_dialogue_taskoriented_dialog_dialogue systems | 
+| 9 | knowledge - question - answering - question answering - kgs | 97 | 9_knowledge_question_answering_question answering | 
+| 10 | financial - sentiment - stock - market - investment | 78 | 10_financial_sentiment_stock_market | 
+| 11 | bias - gender - biases - gender bias - fairness | 78 | 11_bias_gender_biases_gender bias | 
+| 12 | emotion - emotional - empathetic - mental health - affective | 77 | 12_emotion_emotional_empathetic_mental health | 
+| 13 | privacy - private - federated - data - attack | 76 | 13_privacy_private_federated_data | 
+| 14 | text - detection - texts - aigenerated - machinegenerated | 75 | 14_text_detection_texts_aigenerated | 
+| 15 | radiology - medical - reports - image - radiology reports | 75 | 15_radiology_medical_reports_image | 
+| 16 | training - parallelism - gpu - memory - hardware | 71 | 16_training_parallelism_gpu_memory | 
+| 17 | summarization - summaries - abstractive - summary - text summarization | 70 | 17_summarization_summaries_abstractive_summary | 
+| 18 | game - games - agents - social - llm agents | 69 | 18_game_games_agents_social | 
+| 19 | quantization - quantized - weights - memory - compression | 66 | 19_quantization_quantized_weights_memory | 
+| 20 | sql - texttosql - table - database - tabular | 62 | 20_sql_texttosql_table_database | 
+| 21 | retrieval - ranking - rag - reranking - retrievalaugmented | 61 | 21_retrieval_ranking_rag_reranking | 
+| 22 | lora - attention - lowrank - finetuning - memory | 59 | 22_lora_attention_lowrank_finetuning | 
+| 23 | legal - patent - claim - court - law | 58 | 23_legal_patent_claim_court | 
+| 24 | alignment - preference - reward - rlhf - preferences | 58 | 24_alignment_preference_reward_rlhf | 
+| 25 | recommendation - recommender - recommendations - recommender systems - user | 56 | 25_recommendation_recommender_recommendations_recommender systems | 
+| 26 | transformer - transformers - attention - layers - layer | 55 | 26_transformer_transformers_attention_layers | 
+| 27 | tom - cognitive - analogical - analogies - human | 52 | 27_tom_cognitive_analogical_analogies | 
+| 28 | vulnerability - vulnerabilities - code - security - smart | 48 | 28_vulnerability_vulnerabilities_code_security | 
+| 29 | materials - chemistry - materials science - chemical - molecular | 48 | 29_materials_chemistry_materials science_chemical | 
+| 30 | agent - agents - rl - environments - language agents | 47 | 30_agent_agents_rl_environments | 
+| 31 | repair - bugs - bug - program repair - apr | 43 | 31_repair_bugs_bug_program repair | 
+| 32 | graph - graphs - graph reasoning - graph neural - graph data | 43 | 32_graph_graphs_graph reasoning_graph neural | 
+| 33 | speech - asr - audio - speech recognition - recognition | 42 | 33_speech_asr_audio_speech recognition | 
+| 34 | ai - ethical - regulation - risks - risk | 41 | 34_ai_ethical_regulation_risks | 
+| 35 | personality - traits - personality traits - personas - personalities | 41 | 35_personality_traits_personality traits_personas | 
+| 36 | context - context window - window - length - long | 36 | 36_context_context window_window_length | 
+| 37 | chatgpt - research - writing - ai - academic | 34 | 37_chatgpt_research_writing_ai | 
+| 38 | incontext - demonstrations - icl - incontext learning - learning | 33 | 38_incontext_demonstrations_icl_incontext learning | 
+| 39 | sentiment - sentiment analysis - analysis - aspectbased - polarity | 32 | 39_sentiment_sentiment analysis_analysis_aspectbased | 
+| 40 | cultural - opinions - political - survey - values | 30 | 40_cultural_opinions_political_survey | 
+| 41 | tool - tools - apis - api - llms | 29 | 41_tool_tools_apis_api | 
+| 42 | hallucinations - hallucination - hallucination detection - detection - llms | 29 | 42_hallucinations_hallucination_hallucination detection_detection | 
+| 43 | creative - ideas - ai - creativity - storytelling | 28 | 43_creative_ideas_ai_creativity | 
+| 44 | music - musical - audio - lyrics - song | 28 | 44_music_musical_audio_lyrics | 
+| 45 | scaling - scaling laws - laws - training - model | 27 | 45_scaling_scaling laws_laws_training | 
+| 46 | physics - students - chatgpt - education - responses | 26 | 46_physics_students_chatgpt_education | 
+| 47 | correction - grammatical - gec - error - error correction | 26 | 47_correction_grammatical_gec_error | 
+| 48 | test - unit - tests - test generation - test cases | 23 | 48_test_unit_tests_test generation | 
+| 49 | pruning - sparsity - structured pruning - structured - weights | 23 | 49_pruning_sparsity_structured pruning_structured | 
+| 50 | commonsense - commonsense knowledge - knowledge - commonsense question answering - commonsense question | 21 | 50_commonsense_commonsense knowledge_knowledge_commonsense question answering | 
+| 51 | distillation - teacher - student - kd - knowledge distillation | 20 | 51_distillation_teacher_student_kd | 
+| 52 | visualization - visualizations - data visualization - natural - natural language | 20 | 52_visualization_visualizations_data visualization_natural | 
+| 53 | hallucination - hallucinations - lvlms - mllms - visual | 20 | 53_hallucination_hallucinations_lvlms_mllms | 
+| 54 | adversarial - vlms - attacks - attack - adversarial examples | 20 | 54_adversarial_vlms_attacks_attack | 
+| 55 | verilog - design - hardware - hardware design - rtl | 18 | 55_verilog_design_hardware_hardware design | 
+| 56 | spatial - geospatial - geographic - location - populations | 18 | 56_spatial_geospatial_geographic_location | 
+| 57 | intent - intent detection - slot - detection - slot filling | 18 | 57_intent_intent detection_slot_detection | 
+| 58 | prompts - prompt - performance - negated - pseudocode | 18 | 58_prompts_prompt_performance_negated | 
+| 59 | brain - fmri - neural - activity - eeg | 17 | 59_brain_fmri_neural_activity | 
+| 60 | watermarking - copyright - protection - text - model | 16 | 60_watermarking_copyright_protection_text | 
+| 61 | public - social - media - early - ai | 16 | 61_public_social_media_early | 
+| 62 | ai - productivity - chatbots - chatgpt - economy | 15 | 62_ai_productivity_chatbots_chatgpt | 
+| 63 | poetry - poems - poetry generation - lyrics - poem | 15 | 63_poetry_poems_poetry generation_lyrics | 
+| 64 | geoscience - astronomy - scientific - astronomical - galactica | 15 | 64_geoscience_astronomy_scientific_astronomical | 
+| 65 | editing - knowledge editing - knowledge - model editing - editing methods | 14 | 65_editing_knowledge editing_knowledge_model editing | 
+| 66 | argument - arguments - argumentation - fallacy - fallacies | 14 | 66_argument_arguments_argumentation_fallacy | 
+| 67 | mobile - wireless - devices - aigc - network | 14 | 67_mobile_wireless_devices_aigc | 
+| 68 | design - bid - 3d - designs - generative | 14 | 68_design_bid_3d_designs | 
+| 69 | simplification - text simplification - text - sentence - readability | 14 | 69_simplification_text simplification_text_sentence | 
+| 70 | urban - traffic - transportation - foundation models - foundation | 13 | 70_urban_traffic_transportation_foundation models | 
+| 71 | log - anomaly - root - anomaly detection - cloud | 13 | 71_log_anomaly_root_anomaly detection | 
+| 72 | forgetting - catastrophic forgetting - catastrophic - continual - finetuning | 13 | 72_forgetting_catastrophic forgetting_catastrophic_continual | 
+| 73 | scientific - papers - review - gpt4 - feedback | 13 | 73_scientific_papers_review_gpt4 | 
+| 74 | causal - causality - causal discovery - causal inference - causal reasoning | 13 | 74_causal_causality_causal discovery_causal inference | 
+| 75 | product - ecommerce - attribute - extraction - product descriptions | 13 | 75_product_ecommerce_attribute_extraction | 
+| 76 | optimizers - adam - deep - training - networks | 12 | 76_optimizers_adam_deep_training | 
+| 77 | chinese - questions - subjects - school - ceval | 12 | 77_chinese_questions_subjects_school | 
+| 78 | speculative - decoding - draft - speculative decoding - draft model | 12 | 78_speculative_decoding_draft_speculative decoding |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: english
+* low_memory: False
+* min_topic_size: 10
+* n_gram_range: (1, 1)
+* nr_topics: auto
+* 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": "auto",
+  "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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