fix blurb readme

README.md

@@ -2,51 +2,67 @@
 language: en
 license: other
 multilinguality: monolingual
-pretty_name: BioASQ Task B
+pretty_name: BLURB
 ---
 
 
-# Dataset Card for BioASQ Task B
+# Dataset Card for BLURB
 
 ## Dataset Description
 
-- **Homepage:** http://participants-area.bioasq.org/datasets/
+- **Homepage:** https://microsoft.github.io/BLURB/tasks.html
 - **Pubmed:** True
-- **Public:** False
-- **Tasks:** Question Answering
+- **Public:** True
+- **Tasks:** Named Entity Recognition
 
+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. 
 
-The BioASQ corpus contains multiple question
-answering tasks annotated by biomedical experts, including yes/no, factoid, list,
-and summary questions. Pertaining to our objective of comparing neural language
-models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
-of other tasks to future work. Each question is paired with a reference text
-containing multiple sentences from a PubMed abstract and a yes/no answer. We use
-the official train/dev/test split of 670/75/140 questions.
+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.
 
-See 'Domain-Specific Language Model Pretraining for Biomedical
-Natural Language Processing'
+This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits.
 
 
 ## Citation Information
 
 ```
-@article{tsatsaronis2015overview,
-	title        = {
-		An overview of the BIOASQ large-scale biomedical semantic indexing and
-		question answering competition
-	},
-	author       = {
-		Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos
-        and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and
-		Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and
-		Polychronopoulos, Dimitris and others
-	},
-	year         = 2015,
-	journal      = {BMC bioinformatics},
-	publisher    = {BioMed Central Ltd},
-	volume       = 16,
-	number       = 1,
-	pages        = 138
+@article{gu2021domain,
+    title = {
+        Domain-specific language model pretraining for biomedical natural
+        language processing
+    },
+    author = {
+        Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and
+        Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao,
+        Jianfeng and Poon, Hoifung
+    },
+    year = 2021,
+    journal = {ACM Transactions on Computing for Healthcare (HEALTH)},
+    publisher = {ACM New York, NY},
+    volume = 3,
+    number = 1,
+    pages = {1--23}
 }
 ```