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  Use below snippet to load the data properly and it can be used to finetune medical based NER model with some additional processing.
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  ```Python
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- import datasets
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  from datasets import load_dataset
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- features_data = datasets.Features(
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- {
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- "full_text": Value(dtype="string"),
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- "ner_info": [
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- {
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- "text": Value(dtype="string"),
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- "label": Value(dtype="string"),
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- "start": Value(dtype="int64"),
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- "end": Value(dtype="int64"),
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- }
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- ],
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- "tokens": Sequence(Value(dtype="string")),
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- "ner_labels": Sequence(
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- ClassLabel(
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- names=[
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- "O",
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- "B-ACTIVITY",
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- "I-ACTIVITY",
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- "I-ADMINISTRATION",
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- "B-ADMINISTRATION",
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- "B-AGE",
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- "I-AGE",
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- "I-AREA",
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- "B-AREA",
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- "B-BIOLOGICAL_ATTRIBUTE",
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- "I-BIOLOGICAL_ATTRIBUTE",
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- "I-BIOLOGICAL_STRUCTURE",
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- "B-BIOLOGICAL_STRUCTURE",
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- "B-CLINICAL_EVENT",
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- "I-CLINICAL_EVENT",
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- "B-COLOR",
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- "I-COLOR",
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- "I-COREFERENCE",
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- "B-COREFERENCE",
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- "B-DATE",
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- "I-DATE",
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- "I-DETAILED_DESCRIPTION",
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- "B-DETAILED_DESCRIPTION",
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- "I-DIAGNOSTIC_PROCEDURE",
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- "B-DIAGNOSTIC_PROCEDURE",
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- "I-DISEASE_DISORDER",
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- "B-DISEASE_DISORDER",
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- "B-DISTANCE",
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- "I-DISTANCE",
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- "B-DOSAGE",
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- "I-DOSAGE",
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- "I-DURATION",
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- "B-DURATION",
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- "I-FAMILY_HISTORY",
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- "B-FAMILY_HISTORY",
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- "B-FREQUENCY",
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- "I-FREQUENCY",
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- "I-HEIGHT",
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- "B-HEIGHT",
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- "B-HISTORY",
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- "I-HISTORY",
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- "I-LAB_VALUE",
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- "B-LAB_VALUE",
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- "I-MASS",
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- "B-MASS",
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- "I-MEDICATION",
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- "B-MEDICATION",
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- "I-NONBIOLOGICAL_LOCATION",
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- "B-NONBIOLOGICAL_LOCATION",
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- "I-OCCUPATION",
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- "B-OCCUPATION",
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- "B-OTHER_ENTITY",
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- "I-OTHER_ENTITY",
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- "B-OTHER_EVENT",
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- "I-OTHER_EVENT",
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- "I-OUTCOME",
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- "B-OUTCOME",
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- "I-PERSONAL_BACKGROUND",
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- "B-PERSONAL_BACKGROUND",
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- "B-QUALITATIVE_CONCEPT",
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- "I-QUALITATIVE_CONCEPT",
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- "I-QUANTITATIVE_CONCEPT",
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- "B-QUANTITATIVE_CONCEPT",
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- "B-SEVERITY",
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- "I-SEVERITY",
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- "B-SEX",
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- "I-SEX",
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- "B-SHAPE",
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- "I-SHAPE",
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- "B-SIGN_SYMPTOM",
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- "I-SIGN_SYMPTOM",
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- "B-SUBJECT",
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- "I-SUBJECT",
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- "B-TEXTURE",
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- "I-TEXTURE",
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- "B-THERAPEUTIC_PROCEDURE",
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- "I-THERAPEUTIC_PROCEDURE",
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- "I-TIME",
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- "B-TIME",
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- "B-VOLUME",
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- "I-VOLUME",
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- "I-WEIGHT",
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- "B-WEIGHT",
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- ]
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- )
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- ),
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- }
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- )
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  # load the data
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- medical_ner_data = load_dataset("singh-aditya/MACCROBAT-biomedical-ner", field="data", features=features_data)
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  print(medical_ner_data)
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  ```
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  Use below snippet to load the data properly and it can be used to finetune medical based NER model with some additional processing.
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  ```Python
 
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  from datasets import load_dataset
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  # load the data
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+ medical_ner_data = load_dataset("singh-aditya/MACCROBAT_biomedical_ner")
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  print(medical_ner_data)
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  ```
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