teo-sanchez/en_ner_prompting

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