license: mit
task_categories:
- token-classification
language:
- en
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-SpecificDisease
'2': I-SpecificDisease
'3': B-Context_Error
'4': I-Context_Error
'5': B-Undefined_semantics
'6': I-Undefined_semantics
'7': B-SequenceVariant
'8': I-SequenceVariant
'9': B-c-Requires_causality
'10': I-c-Requires_causality
'11': B-CompositeMention
'12': I-CompositeMention
'13': B-OrganismTaxon
'14': I-OrganismTaxon
'15': B-Drug
'16': I-Drug
'17': B-Not_a_criteria
'18': I-Not_a_criteria
'19': B-Post-eligibility
'20': I-Post-eligibility
'21': B-Competing_trial
'22': I-Competing_trial
'23': B-Observation
'24': I-Observation
'25': B-Disease
'26': I-Disease
'27': B-Value
'28': I-Value
'29': B-Line
'30': I-Line
'31': B-Grammar_Error
'32': I-Grammar_Error
'33': B-Measurement
'34': I-Measurement
'35': B-CellLine
'36': I-CellLine
'37': B-Person
'38': I-Person
'39': B-Device
'40': I-Device
'41': B-Pregnancy_considerations
'42': I-Pregnancy_considerations
'43': B-DiseaseOrPhenotypicFeature
'44': I-DiseaseOrPhenotypicFeature
'45': B-Condition
'46': I-Condition
'47': B-Mood
'48': I-Mood
'49': B-Non-representable
'50': I-Non-representable
'51': B-Reference_point
'52': I-Reference_point
'53': B-Non-query-able
'54': I-Non-query-able
'55': B-Qualifier
'56': I-Qualifier
'57': B-DiseaseClass
'58': I-DiseaseClass
'59': B-Parsing_Error
'60': I-Parsing_Error
'61': B-Chemical
'62': I-Chemical
'63': B-Multiplier
'64': I-Multiplier
'65': B-ChemicalEntity
'66': I-ChemicalEntity
'67': B-Procedure
'68': I-Procedure
'69': B-Temporal
'70': I-Temporal
'71': B-Subjective_judgement
'72': I-Subjective_judgement
'73': B-GeneOrGeneProduct
'74': I-GeneOrGeneProduct
'75': B-Informed_consent
'76': I-Informed_consent
'77': B-Intoxication_considerations
'78': I-Intoxication_considerations
'79': B-Modifier
'80': I-Modifier
'81': B-Visit
'82': I-Visit
'83': B-Negation
'84': I-Negation
splits:
- name: test
num_bytes: 786438
num_examples: 346
- name: train
num_bytes: 10705550
num_examples: 3259
download_size: 2474358
dataset_size: 11491988
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
Benchmark Merged dataset - Train
This dataset was generated by the Data preprocessing step of the NERFAIR workflow (More information: https://github.com/YasCoMa/ner-fair-workflow )
It contains the train data of the following datasets:
ncbi: Doğan, Rezarta Islamaj, Robert Leaman, and Zhiyong Lu. 2014. “NCBI Disease Corpus: A Resource for Disease Name Recognition and Concept Normalization.” Journal of Biomedical Informatics 47 (February): 1–10.
bc5cdr: Li, Jiao, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016. “BioCreative V CDR Task Corpus: A Resource for Chemical Disease Relation Extraction.” Database: The Journal of Biological Databases and Curation 2016 (May): baw068.
biored: Luo, Ling, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N. Arighi, and Zhiyong Lu. 2022. “BioRED: A Rich Biomedical Relation Extraction Dataset.” Briefings in Bioinformatics 23 (5): bbac282.
chia: Kury, Fabrício, Alex Butler, Chi Yuan, Li-Heng Fu, Yingcheng Sun, Hao Liu, Ida Sim, Simona Carini, and Chunhua Weng. 2020. “Chia, a Large Annotated Corpus of Clinical Trial Eligibility Criteria.” Scientific Data 7 (1): 281.