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from dataclasses import dataclass
from span_dataclass_converters import get_ner_spans_from_annotations
@dataclass
class PredefinedExample:
text: str
gt_labels: dict
# gt_spans: list
# predictions: list
@property
def gt_spans(self):
return sorted(
get_ner_spans_from_annotations(self.gt_labels),
key=lambda span: span["start"],
)
@property
def predictions(self):
return [self.gt_spans]
small_example = PredefinedExample(
text="The patient was diagnosed with bronchitis and was prescribed a mucolytic",
gt_labels={
"Disease": [
{"start": 31, "end": 41, "label": "bronchitis"},
],
"Drug": [
{"start": 63, "end": 72, "label": "mucolytic"},
],
},
)
big_example = PredefinedExample(
text=(
"The patient was experiencing stomach pain and flu like symptoms for 3 days. "
"Upon investigation, the chest xray revealed acute bronchitis disease. "
"The patient was asked to take rest for a week and was prescribed a mucolytic along with paracetamol for body pains."
),
gt_labels={
"Disease": [
{"start": 120, "end": 144, "label": "acute bronchitis disease"},
],
"Drug": [
{"start": 213, "end": 222, "label": "mucolytic"},
{"start": 234, "end": 245, "label": "paracetamol"},
],
"Symptoms": [
{"start": 29, "end": 41, "label": "stomach pain"},
{"start": 46, "end": 63, "label": "flu like symptoms"},
],
},
)
EXAMPLES = [small_example, big_example]
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