Instructions to use ken-dwyer/en_medextract_md with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use ken-dwyer/en_medextract_md with spaCy:
!pip install https://huggingface.co/ken-dwyer/en_medextract_md/resolve/main/en_medextract_md-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_medextract_md") # Importing as module. import en_medextract_md nlp = en_medextract_md.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_medextract_md |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 20000 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (9 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ADE, Dosage, Drug, Duration, Form, Frequency, Reason, Route, Strength |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
85.17 |
ENTS_P |
87.00 |
ENTS_R |
83.43 |
TOK2VEC_LOSS |
16984196.13 |
NER_LOSS |
2004372.73 |
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Evaluation results
- NER Precisionself-reported0.870
- NER Recallself-reported0.834
- NER F Scoreself-reported0.852