
![](https://www.selas.ai/assets/logo-selas.86b7b0b6.svg)

### Description

Extended dataset infered by the name entity recognition model [en_ner_prompting](https://huggingface.co/teo-sanchez/en_ner_prompting). This model has been trained on hand-annotated prompts from [poloclub/diffusiondb](https://huggingface.co/datasets/poloclub/diffusiondb).
This dataset is hence infered by this model and can comprise mistakes, especially on certain categories (cf. model card).

  The entities comprise 7 main categories and 11 subcategories for a total of 16 categories, extracted from a topic analysis made with [BERTopic](https://maartengr.github.io/BERTopic/index.html).
  The topic analysis can be explored [the following visualization](https://teo-sanchez.github.io/projects/prompting_map.html).

```
  ├── medium/
  │   ├── photography
  │   ├── painting
  │   ├── rendering
  │   └── illustration
  ├── influence/
  │   ├── artist
  │   ├── genre
  │   ├── artwork
  │   └── repository
  ├── light
  ├── color
  ├── composition
  ├── detail
  └── context/
      ├── era
      ├── weather
      └── emotion
```

### Label Scheme

<details>

<summary>View label scheme (16 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `color`, `composition`, `context/emotion`, `context/era`, `context/weather`, `detail`, `influence/artist`, `influence/artwork`, `influence/genre`, `influence/repository`, `light`, `medium/illustration`, `medium/painting`, `medium/photography`, `medium/rendering`, `subject` |

</details>
language_creators:found multilinguality:monolingual size_categories:100M<n<1G source_datasets:poloclub/diffusiondb language:en license:cc-by-3.0 stable diffusion prompt engineering prompts research paper region:us
{"language_creators": ["found"], "language": ["en"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["100M<n<1G"], "source_datasets": ["poloclub/diffusiondb"], "pretty_name": "NER-DiffusionDB", "layout": "default", "title": "Name Entity Recognition of DiffusionDB", "nav_order": 1, "has_children": false, "tags": ["stable diffusion", "prompt engineering", "prompts", "research paper"]}
TAGS
#language_creators-found #multilinguality-monolingual #size_categories-100M<n<1G #source_datasets-poloclub/diffusiondb #language-English #license-cc-by-3.0 #stable diffusion #prompt engineering #prompts #research paper #region-us 

![](URL


### Description


Extended dataset infered by the name entity recognition model en\_ner\_prompting. This model has been trained on hand-annotated prompts from poloclub/diffusiondb.
This dataset is hence infered by this model and can comprise mistakes, especially on certain categories (cf. model card).


The entities comprise 7 main categories and 11 subcategories for a total of 16 categories, extracted from a topic analysis made with BERTopic.
The topic analysis can be explored the following visualization.


### Label Scheme



View label scheme (16 labels for 1 components)


### Description


Extended dataset infered by the name entity recognition model en\_ner\_prompting. This model has been trained on hand-annotated prompts from poloclub/diffusiondb.
This dataset is hence infered by this model and can comprise mistakes, especially on certain categories (cf. model card).


The entities comprise 7 main categories and 11 subcategories for a total of 16 categories, extracted from a topic analysis made with BERTopic.
The topic analysis can be explored the following visualization. ### Label Scheme



View label scheme (16 labels for 1 components)
TAGS
#language_creators-found #multilinguality-monolingual #size_categories-100M<n<1G #source_datasets-poloclub/diffusiondb #language-English #license-cc-by-3.0 #stable diffusion #prompt engineering #prompts #research paper #region-us 
 ### Description


Extended dataset infered by the name entity recognition model en\_ner\_prompting. This model has been trained on hand-annotated prompts from poloclub/diffusiondb.
This dataset is hence infered by this model and can comprise mistakes, especially on certain categories (cf. model card).


The entities comprise 7 main categories and 11 subcategories for a total of 16 categories, extracted from a topic analysis made with BERTopic.
The topic analysis can be explored the following visualization. ### Label Scheme



View label scheme (16 labels for 1 components)
passage: TAGS
#language_creators-found #multilinguality-monolingual #size_categories-100M<n<1G #source_datasets-poloclub/diffusiondb #language-English #license-cc-by-3.0 #stable diffusion #prompt engineering #prompts #research paper #region-us 
### Description


Extended dataset infered by the name entity recognition model en\_ner\_prompting. This model has been trained on hand-annotated prompts from poloclub/diffusiondb.
This dataset is hence infered by this model and can comprise mistakes, especially on certain categories (cf. model card).


The entities comprise 7 main categories and 11 subcategories for a total of 16 categories, extracted from a topic analysis made with BERTopic.
The topic analysis can be explored the following visualization.### Label Scheme



View label scheme (16 labels for 1 components)