Universe

Healthsea

Healthsea: an end-to-end spaCy pipeline for exploring health supplement effects

This spaCy project trains an NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities to analyze supplement reviews and their potential effects on health.

Example

import spacy nlp = spacy.load("en_healthsea") doc = nlp("This is great for joint pain.") # Clause Segmentation & Blinding print(doc._.clauses) > { > "split_indices": [0, 7], > "has_ent": true, > "ent_indices": [4, 6], > "blinder": "_CONDITION_", > "ent_name": "joint pain", > "cats": { > "POSITIVE": 0.9824668169021606, > "NEUTRAL": 0.017364952713251114, > "NEGATIVE": 0.00002889777533710003, > "ANAMNESIS": 0.0001394189748680219 > "prediction_text": ["This", "is", "great", "for", "_CONDITION_", "!"] > } # Aggregated results > { > "joint_pain": { > "effects": ["POSITIVE"], > "effect": "POSITIVE", > "label": "CONDITION", > "text": "joint pain" > } > }
Author info

Edward Schmuhl

GitHubexplosion/healthsea

Categories pipeline research

Submit your project

If you have a project that you want the spaCy community to make use of, you can suggest it by submitting a pull request to the spaCy website repository. The Universe database is open-source and collected in a simple JSON file. For more details on the formats and available fields, see the documentation. Looking for inspiration your own spaCy plugin or extension? Check out the project idea label on the issue tracker.

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