Tutorials
We provide step-by-step guides to get you started. We cover the following use-cases:
Base tutorials
Spacy representations
Learn the basics of how documents are represented with spaCy.
Matching a terminology
Extract phrases that belong to a given terminology.
Qualifying entities
Ensure extracted concepts are not invalidated by linguistic modulation.
Detecting dates
Detect and parse dates in a text.
Processing multiple texts
Improve the inference speed of your pipeline
Detecting hospitalisation reason
Identify spans mentioning the reason for hospitalisation or tag entities as the reason.
↵ Detecting false endlines
Classify each line end and add the excluded attribute to these tokens.
Aggregating results
Aggregate the results of your pipeline at the document level.
FastAPI
Deploy your pipeline as an API.
Visualization
Quickly visualize the results of your pipeline as annotations or tables.
Deep learning tutorials
We also provide tutorials on how to train deep-learning models with EDS-NLP. These tutorials cover the training API, hyperparameter tuning, and more.
Writing a training script
Learn how EDS-NLP handles training deep-neural networks, and how to write a training script on your own.
Training a NER model
Learn how to quickly train a NER model with edsnlp.train.
Training a Span Classifier model
Learn how to quickly train a biopsy date classifier model model with edsnlp.train.
Hyperparameter Tuning
Learn how to tune hyperparameters of a model with edsnlp.tune.