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.


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


Categories pipeline research

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