---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# china-only-mar11

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/china-only-mar11")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 20
* Number of training documents: 847

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | 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 |
  
</details>

## 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