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china-only-mar11
Thang203/china-only-mar11
china-only-mar11 is a text classification model from Thang203. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Mar 26, 2024
- Updated
- Mar 26, 2024
- SHA
- d17e5779
Try a prompt
Model card
From the Hugging Face model README
china-only-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/china-only-mar11")
topic_model.get_topic_info()
Topic overview
- Number of topics: 20
- Number of training documents: 847
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | language - llms - models - data - large | 21 | -1_language_llms_models_data |
| 0 | visual - image - multimodal - models - language | 205 | 0_visual_image_multimodal_models |
| 1 | embodied - driving - navigation - robot - robotic | 142 | 1_embodied_driving_navigation_robot |
| 2 | recommendation - user - recommendations - systems - behavior | 16 | 2_recommendation_user_recommendations_systems |
| 3 | agents - social - bots - interactions - ai agents | 16 | 3_agents_social_bots_interactions |
| 4 | rl - reinforcement learning - reinforcement - learning - policy | 15 | 4_rl_reinforcement learning_reinforcement_learning |
| 5 | molecular - design - property - prediction - gnns | 17 | 5_molecular_design_property_prediction |
| 6 | code - code generation - generation - software - programming | 11 | 6_code_code generation_generation_software |
| 7 | medical - knowledge - medical knowledge - llms - language | 73 | 7_medical_knowledge_medical knowledge_llms |
| 8 | extraction - information extraction - event - information - relation | 16 | 8_extraction_information extraction_event_information |
| 9 | safety - llms - robustness - instructions - assurance | 15 | 9_safety_llms_robustness_instructions |
| 10 | reasoning - prompting - cot - llms - chainofthought | 14 | 10_reasoning_prompting_cot_llms |
| 11 | knowledge - language - knowledge graph - web - kg | 52 | 11_knowledge_language_knowledge graph_web |
| 12 | question - answering - commonsense - question answering - knowledge | 17 | 12_question_answering_commonsense_question answering |
| 13 | models - language - model - training - language models | 18 | 13_models_language_model_training |
| 14 | dialogue - dialog - models - responses - model | 104 | 14_dialogue_dialog_models_responses |
| 15 | detection - fake - news - detectors - texts | 31 | 15_detection_fake_news_detectors |
| 16 | chatgpt - sentiment - evaluation - sentiment analysis - human | 16 | 16_chatgpt_sentiment_evaluation_sentiment analysis |
| 17 | chinese - evaluation - models - language - language models | 22 | 17_chinese_evaluation_models_language |
| 18 | translation - arabic - languages - language - models | 26 | 18_translation_arabic_languages_language |
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: 2.6.1
- Transformers: 4.38.2
- Numba: 0.58.1
- Plotly: 5.15.0
- Python: 3.10.12
- Task
- Text Classification
- Library
- bertopic
- Created
- Mar 26, 2024
- Updated
- Mar 26, 2024