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

# close-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/close-mar11")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 20
* Number of training documents: 4147

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | models - language - llms - language models - chatgpt | 11 | -1_models_language_llms_language models | 
| 0 | code - models - language - llms - language models | 1366 | 0_code_models_language_llms | 
| 1 | medical - clinical - models - llms - language | 840 | 1_medical_clinical_models_llms | 
| 2 | language - models - human - model - llms | 310 | 2_language_models_human_model | 
| 3 | bias - llms - language - models - biases | 196 | 3_bias_llms_language_models | 
| 4 | attacks - adversarial - attack - llms - security | 188 | 4_attacks_adversarial_attack_llms | 
| 5 | visual - image - multimodal - models - video | 184 | 5_visual_image_multimodal_models | 
| 6 | text - detection - chatgpt - models - content | 175 | 6_text_detection_chatgpt_models | 
| 7 | reasoning - language - models - mathematical - logical | 173 | 7_reasoning_language_models_mathematical | 
| 8 | students - chatgpt - education - learning - programming | 119 | 8_students_chatgpt_education_learning | 
| 9 | training - models - model - transformer - transformers | 109 | 9_training_models_model_transformer | 
| 10 | ai - chatgpt - ethical - concerns - research | 106 | 10_ai_chatgpt_ethical_concerns | 
| 11 | ai - design - creative - generative - ideas | 84 | 11_ai_design_creative_generative | 
| 12 | financial - sentiment - stock - market - investment | 68 | 12_financial_sentiment_stock_market | 
| 13 | spatial - urban - models - traffic - large | 52 | 13_spatial_urban_models_traffic | 
| 14 | materials - chemistry - drug - discovery - molecule | 41 | 14_materials_chemistry_drug_discovery | 
| 15 | legal - analysis - law - llms - lawyers | 35 | 15_legal_analysis_law_llms | 
| 16 | recommendation - recommender - recommender systems - systems - recommendations | 35 | 16_recommendation_recommender_recommender systems_systems | 
| 17 | game - agents - games - llms - playing | 30 | 17_game_agents_games_llms | 
| 18 | astronomy - scientific - knowledge - galactica - data | 25 | 18_astronomy_scientific_knowledge_galactica |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* 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