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.

Read more in the blog post and visit the healthsea repository for all 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
Downloads last month
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Hosted inference API
Text Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.

Evaluation results