open-mar11
This is a 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:
from bertopic import BERTopic
topic_model = BERTopic.load("Thang203/open-mar11")
topic_model.get_topic_info()
Topic overview
- Number of topics: 20
- Number of training documents: 2109
Click here for an overview of all topics.
Topic ID | Topic Keywords | Topic Frequency | Label |
---|---|---|---|
-1 | models - language - model - language models - tasks | 11 | -1_models_language_model_language models |
0 | models - language - model - language models - data | 498 | 0_models_language_model_language models |
1 | visual - multimodal - image - images - video | 573 | 1_visual_multimodal_image_images |
2 | reasoning - models - questions - question - language | 149 | 2_reasoning_models_questions_question |
3 | attacks - attack - adversarial - models - safety | 127 | 3_attacks_attack_adversarial_models |
4 | code - code generation - generation - models - llms | 116 | 4_code_code generation_generation_models |
5 | quantization - memory - inference - gpu - models | 99 | 5_quantization_memory_inference_gpu |
6 | medical - clinical - llms - models - language | 85 | 6_medical_clinical_llms_models |
7 | llms - language - planning - models - human | 78 | 7_llms_language_planning_models |
8 | detection - text - data - generated - models | 73 | 8_detection_text_data_generated |
9 | knowledge - graph - graphs - language - sql | 48 | 9_knowledge_graph_graphs_language |
10 | bias - biases - gender - models - language | 46 | 10_bias_biases_gender_models |
11 | recommendation - ranking - retrieval - reranking - item | 44 | 11_recommendation_ranking_retrieval_reranking |
12 | pruning - lora - finetuning - sparsity - parameters | 42 | 12_pruning_lora_finetuning_sparsity |
13 | music - poetry - generation - poems - audio | 28 | 13_music_poetry_generation_poems |
14 | brain - language - models - attention - processing | 24 | 14_brain_language_models_attention |
15 | hallucinations - hallucination - models - large - lvlms | 23 | 15_hallucinations_hallucination_models_large |
16 | circuit - heads - interpretability - mechanistic - mechanistic interpretability | 20 | 16_circuit_heads_interpretability_mechanistic |
17 | financial - analysis - chinese - financial domain - news | 13 | 17_financial_analysis_chinese_financial domain |
18 | sentiment - sentiment analysis - reviews - analysis - aspect | 12 | 18_sentiment_sentiment analysis_reviews_analysis |
Training hyperparameters
- calculate_probabilities: False
- language: english
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: 20
- 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: 2.5.1
- Transformers: 4.38.2
- Numba: 0.58.1
- Plotly: 5.15.0
- Python: 3.10.12
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