Add BERTopic model
Browse files- README.md +149 -0
- config.json +17 -0
- ctfidf.bin +3 -0
- ctfidf_config.json +0 -0
- topic_embeddings.bin +3 -0
- topics.json +0 -0
README.md
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---
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tags:
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- bertopic
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library_name: bertopic
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pipeline_tag: text-classification
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---
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# topic_model_general_normal_april8
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
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## Usage
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To use this model, please install BERTopic:
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```
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pip install -U bertopic
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```
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You can use the model as follows:
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```python
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from bertopic import BERTopic
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topic_model = BERTopic.load("Thang203/topic_model_general_normal_april8")
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topic_model.get_topic_info()
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```
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## Topic overview
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* Number of topics: 80
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* Number of training documents: 6795
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<details>
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<summary>Click here for an overview of all topics.</summary>
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| Topic ID | Topic Keywords | Topic Frequency | Label |
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|----------|----------------|-----------------|-------|
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| -1 | models - language - llms - language models - chatgpt | 11 | -1_models_language_llms_language models |
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| 0 | translation - language - models - data - generation | 2010 | 0_translation_language_models_data |
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| 1 | visual - multimodal - image - images - video | 510 | 1_visual_multimodal_image_images |
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| 2 | reasoning - math - cot - mathematical - problems | 432 | 2_reasoning_math_cot_mathematical |
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| 3 | attacks - attack - adversarial - safety - jailbreak | 340 | 3_attacks_attack_adversarial_safety |
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| 4 | medical - clinical - biomedical - health - healthcare | 318 | 4_medical_clinical_biomedical_health |
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| 5 | code - code generation - generation - programming - software | 303 | 5_code_code generation_generation_programming |
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| 6 | students - education - ai - chatgpt - student | 153 | 6_students_education_ai_chatgpt |
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| 7 | robot - planning - robots - navigation - robotic | 110 | 7_robot_planning_robots_navigation |
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| 8 | dialogue - taskoriented - dialog - dialogue systems - systems | 107 | 8_dialogue_taskoriented_dialog_dialogue systems |
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| 9 | knowledge - question - answering - question answering - kgs | 97 | 9_knowledge_question_answering_question answering |
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| 10 | financial - sentiment - stock - market - investment | 78 | 10_financial_sentiment_stock_market |
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| 11 | bias - gender - biases - gender bias - fairness | 78 | 11_bias_gender_biases_gender bias |
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| 12 | emotion - emotional - empathetic - mental health - affective | 77 | 12_emotion_emotional_empathetic_mental health |
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| 13 | privacy - private - federated - data - attack | 76 | 13_privacy_private_federated_data |
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| 14 | text - detection - texts - aigenerated - machinegenerated | 75 | 14_text_detection_texts_aigenerated |
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| 15 | radiology - medical - reports - image - radiology reports | 75 | 15_radiology_medical_reports_image |
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| 16 | training - parallelism - gpu - memory - hardware | 71 | 16_training_parallelism_gpu_memory |
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| 17 | summarization - summaries - abstractive - summary - text summarization | 70 | 17_summarization_summaries_abstractive_summary |
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| 18 | game - games - agents - social - llm agents | 69 | 18_game_games_agents_social |
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| 19 | quantization - quantized - weights - memory - compression | 66 | 19_quantization_quantized_weights_memory |
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| 20 | sql - texttosql - table - database - tabular | 62 | 20_sql_texttosql_table_database |
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| 21 | retrieval - ranking - rag - reranking - retrievalaugmented | 61 | 21_retrieval_ranking_rag_reranking |
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| 22 | lora - attention - lowrank - finetuning - memory | 59 | 22_lora_attention_lowrank_finetuning |
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| 23 | legal - patent - claim - court - law | 58 | 23_legal_patent_claim_court |
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| 24 | alignment - preference - reward - rlhf - preferences | 58 | 24_alignment_preference_reward_rlhf |
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| 25 | recommendation - recommender - recommendations - recommender systems - user | 56 | 25_recommendation_recommender_recommendations_recommender systems |
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| 26 | transformer - transformers - attention - layers - layer | 55 | 26_transformer_transformers_attention_layers |
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| 27 | tom - cognitive - analogical - analogies - human | 52 | 27_tom_cognitive_analogical_analogies |
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| 28 | vulnerability - vulnerabilities - code - security - smart | 48 | 28_vulnerability_vulnerabilities_code_security |
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| 29 | materials - chemistry - materials science - chemical - molecular | 48 | 29_materials_chemistry_materials science_chemical |
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| 30 | agent - agents - rl - environments - language agents | 47 | 30_agent_agents_rl_environments |
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| 31 | repair - bugs - bug - program repair - apr | 43 | 31_repair_bugs_bug_program repair |
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| 32 | graph - graphs - graph reasoning - graph neural - graph data | 43 | 32_graph_graphs_graph reasoning_graph neural |
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| 33 | speech - asr - audio - speech recognition - recognition | 42 | 33_speech_asr_audio_speech recognition |
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| 34 | ai - ethical - regulation - risks - risk | 41 | 34_ai_ethical_regulation_risks |
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| 35 | personality - traits - personality traits - personas - personalities | 41 | 35_personality_traits_personality traits_personas |
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| 36 | context - context window - window - length - long | 36 | 36_context_context window_window_length |
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| 37 | chatgpt - research - writing - ai - academic | 34 | 37_chatgpt_research_writing_ai |
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| 38 | incontext - demonstrations - icl - incontext learning - learning | 33 | 38_incontext_demonstrations_icl_incontext learning |
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| 39 | sentiment - sentiment analysis - analysis - aspectbased - polarity | 32 | 39_sentiment_sentiment analysis_analysis_aspectbased |
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| 40 | cultural - opinions - political - survey - values | 30 | 40_cultural_opinions_political_survey |
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| 41 | tool - tools - apis - api - llms | 29 | 41_tool_tools_apis_api |
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| 42 | hallucinations - hallucination - hallucination detection - detection - llms | 29 | 42_hallucinations_hallucination_hallucination detection_detection |
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| 43 | creative - ideas - ai - creativity - storytelling | 28 | 43_creative_ideas_ai_creativity |
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| 44 | music - musical - audio - lyrics - song | 28 | 44_music_musical_audio_lyrics |
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| 45 | scaling - scaling laws - laws - training - model | 27 | 45_scaling_scaling laws_laws_training |
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| 46 | physics - students - chatgpt - education - responses | 26 | 46_physics_students_chatgpt_education |
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| 47 | correction - grammatical - gec - error - error correction | 26 | 47_correction_grammatical_gec_error |
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| 48 | test - unit - tests - test generation - test cases | 23 | 48_test_unit_tests_test generation |
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| 49 | pruning - sparsity - structured pruning - structured - weights | 23 | 49_pruning_sparsity_structured pruning_structured |
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| 50 | commonsense - commonsense knowledge - knowledge - commonsense question answering - commonsense question | 21 | 50_commonsense_commonsense knowledge_knowledge_commonsense question answering |
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| 51 | distillation - teacher - student - kd - knowledge distillation | 20 | 51_distillation_teacher_student_kd |
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| 52 | visualization - visualizations - data visualization - natural - natural language | 20 | 52_visualization_visualizations_data visualization_natural |
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| 53 | hallucination - hallucinations - lvlms - mllms - visual | 20 | 53_hallucination_hallucinations_lvlms_mllms |
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| 54 | adversarial - vlms - attacks - attack - adversarial examples | 20 | 54_adversarial_vlms_attacks_attack |
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| 55 | verilog - design - hardware - hardware design - rtl | 18 | 55_verilog_design_hardware_hardware design |
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| 56 | spatial - geospatial - geographic - location - populations | 18 | 56_spatial_geospatial_geographic_location |
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| 57 | intent - intent detection - slot - detection - slot filling | 18 | 57_intent_intent detection_slot_detection |
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| 58 | prompts - prompt - performance - negated - pseudocode | 18 | 58_prompts_prompt_performance_negated |
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| 59 | brain - fmri - neural - activity - eeg | 17 | 59_brain_fmri_neural_activity |
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| 60 | watermarking - copyright - protection - text - model | 16 | 60_watermarking_copyright_protection_text |
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| 61 | public - social - media - early - ai | 16 | 61_public_social_media_early |
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| 62 | ai - productivity - chatbots - chatgpt - economy | 15 | 62_ai_productivity_chatbots_chatgpt |
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| 63 | poetry - poems - poetry generation - lyrics - poem | 15 | 63_poetry_poems_poetry generation_lyrics |
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| 64 | geoscience - astronomy - scientific - astronomical - galactica | 15 | 64_geoscience_astronomy_scientific_astronomical |
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| 65 | editing - knowledge editing - knowledge - model editing - editing methods | 14 | 65_editing_knowledge editing_knowledge_model editing |
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| 66 | argument - arguments - argumentation - fallacy - fallacies | 14 | 66_argument_arguments_argumentation_fallacy |
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| 67 | mobile - wireless - devices - aigc - network | 14 | 67_mobile_wireless_devices_aigc |
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| 68 | design - bid - 3d - designs - generative | 14 | 68_design_bid_3d_designs |
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| 69 | simplification - text simplification - text - sentence - readability | 14 | 69_simplification_text simplification_text_sentence |
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| 70 | urban - traffic - transportation - foundation models - foundation | 13 | 70_urban_traffic_transportation_foundation models |
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| 71 | log - anomaly - root - anomaly detection - cloud | 13 | 71_log_anomaly_root_anomaly detection |
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| 72 | forgetting - catastrophic forgetting - catastrophic - continual - finetuning | 13 | 72_forgetting_catastrophic forgetting_catastrophic_continual |
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| 73 | scientific - papers - review - gpt4 - feedback | 13 | 73_scientific_papers_review_gpt4 |
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| 74 | causal - causality - causal discovery - causal inference - causal reasoning | 13 | 74_causal_causality_causal discovery_causal inference |
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| 75 | product - ecommerce - attribute - extraction - product descriptions | 13 | 75_product_ecommerce_attribute_extraction |
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| 76 | optimizers - adam - deep - training - networks | 12 | 76_optimizers_adam_deep_training |
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| 77 | chinese - questions - subjects - school - ceval | 12 | 77_chinese_questions_subjects_school |
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| 78 | speculative - decoding - draft - speculative decoding - draft model | 12 | 78_speculative_decoding_draft_speculative decoding |
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</details>
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## Training hyperparameters
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* calculate_probabilities: False
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* language: english
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* low_memory: False
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* min_topic_size: 10
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* n_gram_range: (1, 1)
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* nr_topics: auto
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* seed_topic_list: None
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* top_n_words: 10
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* verbose: True
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* zeroshot_min_similarity: 0.7
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* zeroshot_topic_list: None
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## Framework versions
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* Numpy: 1.25.2
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* HDBSCAN: 0.8.33
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* UMAP: 0.5.6
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* Pandas: 2.0.3
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* Scikit-Learn: 1.2.2
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* Sentence-transformers: 2.6.1
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* Transformers: 4.38.2
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* Numba: 0.58.1
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* Plotly: 5.15.0
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* Python: 3.10.12
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config.json
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{
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"calculate_probabilities": false,
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"language": "english",
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"low_memory": false,
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"min_topic_size": 10,
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"n_gram_range": [
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1,
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],
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"nr_topics": "auto",
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": true,
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"zeroshot_min_similarity": 0.7,
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"zeroshot_topic_list": null,
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"embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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}
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ctfidf.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:298d14667eac5ff99e83f009649b0d16ed2353717e136654ca1e3c3b50dc89aa
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size 5976003
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ctfidf_config.json
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topic_embeddings.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0ca00cdaa0a8edb8d261ba9995eab7c4476d6283a7fefd8ed130c62c83cee03
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size 124169
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topics.json
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