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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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# open-mar11 |
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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/open-mar11") |
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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: 20 |
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* Number of training documents: 2109 |
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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 - model - language models - tasks | 11 | -1_models_language_model_language models | |
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| 0 | models - language - model - language models - data | 498 | 0_models_language_model_language models | |
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| 1 | visual - multimodal - image - images - video | 573 | 1_visual_multimodal_image_images | |
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| 2 | reasoning - models - questions - question - language | 149 | 2_reasoning_models_questions_question | |
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| 3 | attacks - attack - adversarial - models - safety | 127 | 3_attacks_attack_adversarial_models | |
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| 4 | code - code generation - generation - models - llms | 116 | 4_code_code generation_generation_models | |
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| 5 | quantization - memory - inference - gpu - models | 99 | 5_quantization_memory_inference_gpu | |
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| 6 | medical - clinical - llms - models - language | 85 | 6_medical_clinical_llms_models | |
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| 7 | llms - language - planning - models - human | 78 | 7_llms_language_planning_models | |
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| 8 | detection - text - data - generated - models | 73 | 8_detection_text_data_generated | |
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| 9 | knowledge - graph - graphs - language - sql | 48 | 9_knowledge_graph_graphs_language | |
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| 10 | bias - biases - gender - models - language | 46 | 10_bias_biases_gender_models | |
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| 11 | recommendation - ranking - retrieval - reranking - item | 44 | 11_recommendation_ranking_retrieval_reranking | |
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| 12 | pruning - lora - finetuning - sparsity - parameters | 42 | 12_pruning_lora_finetuning_sparsity | |
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| 13 | music - poetry - generation - poems - audio | 28 | 13_music_poetry_generation_poems | |
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| 14 | brain - language - models - attention - processing | 24 | 14_brain_language_models_attention | |
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| 15 | hallucinations - hallucination - models - large - lvlms | 23 | 15_hallucinations_hallucination_models_large | |
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| 16 | circuit - heads - interpretability - mechanistic - mechanistic interpretability | 20 | 16_circuit_heads_interpretability_mechanistic | |
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| 17 | financial - analysis - chinese - financial domain - news | 13 | 17_financial_analysis_chinese_financial domain | |
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| 18 | sentiment - sentiment analysis - reviews - analysis - aspect | 12 | 18_sentiment_sentiment analysis_reviews_analysis | |
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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: 20 |
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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: 0.5.5 |
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* Pandas: 1.5.3 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 2.5.1 |
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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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