metadata

tags:
  - spacy
  - token-classification
  - text-classification
language:
  - en
model-index:
  - name: en_healthsea
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 80.77
          - name: NER Recall
            type: recall
            value: 79.92
          - name: NER F Score
            type: f_score
            value: 80.34

Welcome to Healthsea ✨

Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities.

In our blog post you can read more about the architecture of healthsea and you can also visit the healthsea repository for all the training workflows, custom components and training data.

Feature	Description
Name	`en_healthsea`
Version	`0.0.0`
spaCy	`>=3.2.0,<3.3.0`
Default Pipeline	`sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation`
Components	`sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation`
Vectors	684830 keys, 684830 unique vectors (300 dimensions)
Sources	n/a
License	MIT
Author	Explosion

Label Scheme

View label scheme (6 labels for 2 components)

Component	Labels
`ner`	`BENEFIT`, `CONDITION`
`clausecat`	`POSITIVE`, `NEUTRAL`, `NEGATIVE`, `ANAMNESIS`

Accuracy

Type	Score
`ENTS_F`	80.34
`ENTS_P`	80.77
`ENTS_R`	79.92
`CATS_SCORE`	74.87
`CATS_MICRO_P`	82.17
`CATS_MICRO_R`	80.85
`CATS_MICRO_F`	81.51
`CATS_MACRO_P`	78.01
`CATS_MACRO_R`	72.41
`CATS_MACRO_F`	74.87
`CATS_MACRO_AUC`	92.76
`CATS_LOSS`	297.22