| Feature | Description |
|---|---|
| Name | en_ner_prompting |
| Version | 0.0.3 |
| spaCy | >=3.4.3,<3.5.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | CC BY 3.0 |
| Author | Selas.ai |
Description
Name entity recognition model to analyzing text-to-image prompts (Stable Diffusion).
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.
├── medium/
│ ├── photography
│ ├── painting
│ ├── rendering
│ └── illustration
├── influence/
│ ├── artist
│ ├── genre
│ ├── artwork
│ └── repository
├── light
├── color
├── composition
├── detail
└── context/
├── era
├── weather
└── emotion
Prompt data are from the diffusionDB database and were annotated by hand using Prodigy.
Label Scheme
View label scheme (16 labels for 1 components)
| 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 |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
73.42 |
ENTS_P |
74.38 |
ENTS_R |
72.49 |
TOK2VEC_LOSS |
19323.84 |
NER_LOSS |
144524.82 |
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Evaluation results
- NER Precisionself-reported0.744
- NER Recallself-reported0.725
- NER F Scoreself-reported0.734