File size: 3,363 Bytes
847bd10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
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
|