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---
license: mit
task_categories:
- token-classification
language:
- en
tags:
- biology
- medical
size_categories:
- 1M<n<10M
field:
- data
---
# MACCROBAT-biomedical-ner
## Uses
```Python
import datasets
from datasets import load_dataset
features_data = datasets.Features(
{
"full_text": Value(dtype="string"),
"ner_info": [
{
"text": Value(dtype="string"),
"label": Value(dtype="string"),
"start": Value(dtype="int64"),
"end": Value(dtype="int64"),
}
],
"tokens": Sequence(Value(dtype="string")),
"ner_labels": Sequence(
ClassLabel(
names=[
"O",
"B-ACTIVITY",
"I-ACTIVITY",
"I-ADMINISTRATION",
"B-ADMINISTRATION",
"B-AGE",
"I-AGE",
"I-AREA",
"B-AREA",
"B-BIOLOGICAL_ATTRIBUTE",
"I-BIOLOGICAL_ATTRIBUTE",
"I-BIOLOGICAL_STRUCTURE",
"B-BIOLOGICAL_STRUCTURE",
"B-CLINICAL_EVENT",
"I-CLINICAL_EVENT",
"B-COLOR",
"I-COLOR",
"I-COREFERENCE",
"B-COREFERENCE",
"B-DATE",
"I-DATE",
"I-DETAILED_DESCRIPTION",
"B-DETAILED_DESCRIPTION",
"I-DIAGNOSTIC_PROCEDURE",
"B-DIAGNOSTIC_PROCEDURE",
"I-DISEASE_DISORDER",
"B-DISEASE_DISORDER",
"B-DISTANCE",
"I-DISTANCE",
"B-DOSAGE",
"I-DOSAGE",
"I-DURATION",
"B-DURATION",
"I-FAMILY_HISTORY",
"B-FAMILY_HISTORY",
"B-FREQUENCY",
"I-FREQUENCY",
"I-HEIGHT",
"B-HEIGHT",
"B-HISTORY",
"I-HISTORY",
"I-LAB_VALUE",
"B-LAB_VALUE",
"I-MASS",
"B-MASS",
"I-MEDICATION",
"B-MEDICATION",
"I-NONBIOLOGICAL_LOCATION",
"B-NONBIOLOGICAL_LOCATION",
"I-OCCUPATION",
"B-OCCUPATION",
"B-OTHER_ENTITY",
"I-OTHER_ENTITY",
"B-OTHER_EVENT",
"I-OTHER_EVENT",
"I-OUTCOME",
"B-OUTCOME",
"I-PERSONAL_BACKGROUND",
"B-PERSONAL_BACKGROUND",
"B-QUALITATIVE_CONCEPT",
"I-QUALITATIVE_CONCEPT",
"I-QUANTITATIVE_CONCEPT",
"B-QUANTITATIVE_CONCEPT",
"B-SEVERITY",
"I-SEVERITY",
"B-SEX",
"I-SEX",
"B-SHAPE",
"I-SHAPE",
"B-SIGN_SYMPTOM",
"I-SIGN_SYMPTOM",
"B-SUBJECT",
"I-SUBJECT",
"B-TEXTURE",
"I-TEXTURE",
"B-THERAPEUTIC_PROCEDURE",
"I-THERAPEUTIC_PROCEDURE",
"I-TIME",
"B-TIME",
"B-VOLUME",
"I-VOLUME",
"I-WEIGHT",
"B-WEIGHT",
]
)
),
}
)
# load the data
medical_ner_data = load_dataset("singh-aditya/MACCROBAT-biomedical-ner", field="data", features=features_data)
print(medical_ner_data)
```
```
DatasetDict({
train: Dataset({
features: ['ner_labels', 'tokens', 'full_text', 'ner_info'],
num_rows: 200
})
})
```
<!-- Address questions around how the dataset is intended to be used. -->
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
**BibTeX:**
```JSON
{
article= Caufield2020,
author = "J. Harry Caufield",
title = "{MACCROBAT}",
year = "2020",
month = "1",
url = "https://figshare.com/articles/dataset/MACCROBAT2018/9764942",
doi = "10.6084/m9.figshare.9764942.v2"
}
``` |