| 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 |
- Downloads last month
- 3
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Space using teo-sanchez/en_ner_prompting 1
Evaluation results
- NER Precisionself-reported0.744
- NER Recallself-reported0.725
- NER F Scoreself-reported0.734