Datasets:
license: apache-2.0
annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- structure-prediction
- question-answering
- text-scoring
- text-classification
task_ids:
- named-entity-recognition
- parsing
- closed-domain-qa
- semantic-similarity-scoring
- text-scoring-other-sentence-similrity
- topic-classification---
Dataset Card for BLURB
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://microsoft.github.io/BLURB/index.html
- Repository:
- Paper: https://arxiv.org/pdf/2007.15779.pdf
- Leaderboard: https://microsoft.github.io/BLURB/leaderboard.html
- Point of Contact:
Dataset Summary
BLURB is a collection of resources for biomedical natural language processing. In general domains, such as newswire and the Web, comprehensive benchmarks and leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. In biomedicine, however, such resources are ostensibly scarce. In the past, there have been a plethora of shared tasks in biomedical NLP, such as BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These efforts have played a significant role in fueling interest and progress by the research community, but they typically focus on individual tasks. The advent of neural language models, such as BERT provides a unifying foundation to leverage transfer learning from unlabeled text to support a wide range of NLP applications. To accelerate progress in biomedical pretraining strategies and task-specific methods, it is thus imperative to create a broad-coverage benchmark encompassing diverse biomedical tasks.
Inspired by prior efforts toward this direction (e.g., BLUE), we have created BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP applications, as well as a leaderboard for tracking progress by the community. BLURB includes thirteen publicly available datasets in six diverse tasks. To avoid placing undue emphasis on tasks with many available datasets, such as named entity recognition (NER), BLURB reports the macro average across all tasks as the main score. The BLURB leaderboard is model-agnostic. Any system capable of producing the test predictions using the same training and development data can participate. The main goal of BLURB is to lower the entry barrier in biomedical NLP and help accelerate progress in this vitally important field for positive societal and human impact.
Supported Tasks and Leaderboards
| Dataset | Task | Train | Dev | Test | Evaluation Metrics | Added |
|---|---|---|---|---|---|---|
| BC5-chem | NER | 5203 | 5347 | 5385 | F1 entity-level | Yes |
| BC5-disease | NER | 4182 | 4244 | 4424 | F1 entity-level | Yes |
| NCBI-disease | NER | 5134 | 787 | 960 | F1 entity-level | Yes |
| BC2GM | NER | 15197 | 3061 | 6325 | F1 entity-level | Yes |
| JNLPBA | NER | 46750 | 4551 | 8662 | F1 entity-level | Yes |
| EBM PICO | PICO | 339167 | 85321 | 16364 | Macro F1 word-level | No |
| ChemProt | Relation Extraction | 18035 | 11268 | 15745 | Micro F1 | No |
| DDI | Relation Extraction | 25296 | 2496 | 5716 | Micro F1 | No |
| GAD | Relation Extraction | 4261 | 535 | 534 | Micro F1 | No |
| BIOSSES | Sentence Similarity | 64 | 16 | 20 | Pearson | No |
| HoC | Document Classification | 1295 | 186 | 371 | Average Micro F1 | No |
| PubMedQA | Question Answering | 450 | 50 | 500 | Accuracy | No |
| BioASQ | Question Answering | 670 | 75 | 140 | Accuracy | No |
Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB. This is something to be checked.
Languages
English from biomedical texts
Dataset Structure
Data Instances
- NER
{ 'id': 0, 'tokens': [ "DPP6", "as", "a", "candidate", "gene", "for", "neuroleptic", "-", "induced", "tardive", "dyskinesia", "." ] 'ner_tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ] } - PICO
- Relation Extraction
- Sentence Similarity
- Document Classification
- Question Answering
Data Fields
- NER
- id, ner_tags, tokens
- PICO
- Relation Extraction
- Sentence Similarity
- Document Classification
- Question Answering
Data Splits
Shown in the table of supported tasks.
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
Thanks to @github-username for adding this dataset.