Instructions to use karl2990/en_med12_trf_weakly_supervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use karl2990/en_med12_trf_weakly_supervised with spaCy:
!pip install https://huggingface.co/karl2990/en_med12_trf_weakly_supervised/resolve/main/en_med12_trf_weakly_supervised-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_med12_trf_weakly_supervised") # Importing as module. import en_med12_trf_weakly_supervised nlp = en_med12_trf_weakly_supervised.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
- text-classification
language:
- en
model-index:
- name: en_med12_trf_weakly_supervised
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9995091177
- name: NER Recall
type: recall
value: 0.9983655976
- name: NER F Score
type: f_score
value: 0.9989370304
| Feature | Description |
|---|---|
| Name | en_med12_trf_weakly_supervised |
| Version | 0.0.0 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | transformer, ner, textcat |
| Components | transformer, ner, textcat |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (14 labels for 2 components)
| Component | Labels |
|---|---|
ner |
Denominator_Unit, Denominator_Value, Dose_Form, Medication_Name, NDC, Numerator_Unit, Numerator_Value, Product_Package_Type, Product_Package_Type_Value, Quantity_Factor_Unit, Quantity_Factor_Unit_Value, Quantity_Factor_Value |
textcat |
OTHER, MEDICATION |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.89 |
ENTS_P |
99.95 |
ENTS_R |
99.84 |
CATS_SCORE |
100.00 |
CATS_MICRO_P |
100.00 |
CATS_MICRO_R |
100.00 |
CATS_MICRO_F |
100.00 |
CATS_MACRO_P |
100.00 |
CATS_MACRO_R |
100.00 |
CATS_MACRO_F |
100.00 |
CATS_MACRO_AUC |
100.00 |
CATS_MACRO_AUC_PER_TYPE |
0.00 |
TRANSFORMER_LOSS |
18547.50 |
NER_LOSS |
153097.22 |
TEXTCAT_LOSS |
0.00 |