Remove unnecessary files and add Benchmark QA dataset

.gitattributes

@@ -61,3 +61,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.csv filter=lfs diff=lfs merge=lfs -text
 *.pdf filter=lfs diff=lfs merge=lfs -text
 *.txt filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -16,10 +16,13 @@ pretty_name: MedNLPCombined
 
 **MedNLPCombined** is a collected repository of medical Natural Language Processing (NLP) datasets, primarily focused on **Chemical-Disease Relations (CDR)**, **Toxicogenomics**, and **Gene Interactions**. This repository is designed to facilitate research in Named Entity Recognition (NER) and Relation Extraction (RE) within the biomedical domain.
 
-The repository currently includes three major components:
+The repository currently includes six major components:
 1. **BioCreative V CDR (BC5CDR) Task Corpus**
 2. **Comparative Toxicogenomics Database (CTD) Derived Data**
 3. **ChemDisGene Dataset**
+4. **MedQA Dataset**
+5. **PubMedQA Dataset**
+6. **BioASQ Dataset**
 
 ## Repository Structure
 
@@ -46,6 +49,18 @@ Contains the **ChemDisGene** dataset for distant supervision of biomedical relat
 - **`related_documents/`**: Documentation and guidelines.
   - `AnnotationGuidelines.pdf`
 
+### 4. `medqa/`
+Contains the **MedQA** dataset.
+- A large-scale open-domain multiple-choice dataset for medical problems, collected from professional exams.
+
+### 5. `pubmedqa/`
+Contains the **PubMedQA** dataset.
+- A biomedical research question answering dataset requiring reasoning over PubMed abstracts.
+
+### 6. `bioasq/`
+Contains the **BioASQ** dataset.
+- A benchmark dataset for large-scale biomedical semantic indexing and question answering.
+
 ## Dataset Details
 
 ### BioCreative V CDR (BC5CDR)
@@ -57,7 +72,32 @@ The BC5CDR corpus consists of **1,500 PubMed articles** with annotated chemical
 The CTD data provides manually curated information about chemical-gene/protein interactions, chemical-disease and gene-disease relationships. This component of the repository is likely a snapshot or a derived subset focusing on specific interaction types (e.g., chemical-disease-gene networks).
 
 ### ChemDisGene
-ChemDisGene is a large-scale, distant-supervision dataset for extracting biomedical relationships between chemicals, diseases, and genes. It provides a valuable resource for training models on a broader range of biomedical interactions.
+ChemDisGene is a large-scale, distant-supervision dataset for extracting biomedical relationships between chemicals, diseases, and genes. It provides a valuable resource for training models on a broader range of biomedical interactions. The dataset contains approximately 80,000 biomedical research abstracts annotated with mentions of chemical, disease, and gene/gene-product entities, along with their pairwise relationships.
+
+- **Paper**: [A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes](https://aclanthology.org/2022.lrec-1.666/)
+- **GitHub**: [chanzuckerberg/ChemDisGene](https://github.com/chanzuckerberg/ChemDisGene)
+- **Hugging Face**: [bigbio/chem_dis_gene](https://huggingface.co/datasets/bigbio/chem_dis_gene)
+
+### MedQA
+MedQA is a large-scale open-domain question answering dataset derived from professional medical board exams in the US, Mainland China, and Taiwan. It evaluates models on professional medical knowledge and clinical decision-making through multiple-choice questions.
+
+- **Paper**: [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://arxiv.org/abs/2009.13081)
+- **GitHub**: [jind11/MedQA](https://github.com/jind11/MedQA)
+- **Hugging Face**: [bigbio/med_qa](https://huggingface.co/datasets/bigbio/med_qa)
+
+### PubMedQA
+PubMedQA is a biomedical question answering dataset designed to answer research questions with yes/no/maybe using the corresponding abstracts. It requires reasoning over quantitative content and scientific texts.
+
+- **Paper**: [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146)
+- **GitHub**: [pubmedqa/pubmedqa](https://github.com/pubmedqa/pubmedqa)
+- **Hugging Face**: [bigbio/pubmed_qa](https://huggingface.co/datasets/bigbio/pubmed_qa)
+
+### BioASQ
+BioASQ is a large-scale biomedical semantic indexing and question answering benchmark dataset. It provides various QA tasks challenging systems with realistic information needs from biomedical experts.
+
+- **Paper**: [An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0564-6)
+- **GitHub**: [BioASQ Organization](https://github.com/BioASQ)
+- **Hugging Face**: [bigbio/bioasq_task_b](https://huggingface.co/datasets/bigbio/bioasq_task_b)
 
 ## Usage
 
@@ -113,6 +153,41 @@ Please refer to the [CTD citation policy](http://ctdbase.org/about/publications/
 }
 ```
 
+**MedQA:**
+```bibtex
+@article{jin2020disease,
+  title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams},
+  author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
+  journal={arXiv preprint arXiv:2009.13081},
+  year={2020}
+}
+```
+
+**PubMedQA:**
+```bibtex
+@inproceedings{jin2019pubmedqa,
+  title={PubMedQA: A Dataset for Biomedical Research Question Answering},
+  author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
+  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
+  pages={2567--2577},
+  year={2019}
+}
+```
+
+**BioASQ:**
+```bibtex
+@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 Petasis, Pethes and Polychronopoulos, Dimitris and others},
+  journal={BMC bioinformatics},
+  volume={16},
+  number={1},
+  pages={1--28},
+  year={2015},
+  publisher={BioMed Central}
+}
+```
+
 ## License
 
-This repository is licensed under **Apache 2.0**. Please also adhere to the specific license agreements of the original datasets (BC5CDR, CTD, and ChemDisGene) if applicable.
+This repository is licensed under **Apache 2.0**. Please also adhere to the specific license agreements of the original datasets (BC5CDR, CTD, ChemDisGene, MedQA, PubMedQA, and BioASQ) if applicable.

bc5cdr/data/training/CDR_DevelopmentSet.PubTator.txt

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bc5cdr/data/training/CDR_TestSet.PubTator.txt

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bc5cdr/data/training/CDR_TrainingSet.PubTator.txt

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bioasq/task_b/bioasq_task_b.py

@@ -0,0 +1,836 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+BioASQ Task B On Biomedical Semantic QA (Involves IR, QA, Summarization qnd
+More). This task uses benchmark datasets containing development and test
+questions, in English, along with gold standard (reference) answers constructed
+by a team of biomedical experts. The participants have to respond with relevant
+concepts, articles, snippets and RDF triples, from designated resources, as well
+as exact and 'ideal' answers.
+
+Fore more information about the challenge, the organisers and the relevant
+publications please visit: http://bioasq.org/
+"""
+import glob
+import json
+import os
+import re
+
+import datasets
+
+from .bigbiohub import qa_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ["English"]
+_PUBMED = True
+_LOCAL = True
+_CITATION = """\
+@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
+}
+"""
+
+_DATASETNAME = "bioasq_task_b"
+_DISPLAYNAME = "BioASQ Task B"
+
+_BIOASQ_11B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ
+11, which will take place during 2023. There is one file containing the data:
+ - training11b.json
+
+The file contains the data of the first ten editions of the challenge: 4719
+questions [1] with their relevant documents, snippets, concepts and RDF
+triples, exact and ideal answers.
+
+Differences with BioASQ-training10b.json 
+	- 485 new questions added from BioASQ10
+		- The question with id 621ecf1a3a8413c653000061 had identical body with
+		5ac0a36f19833b0d7b000002. All relevant elements from both questions
+		are available in the merged question with id 5ac0a36f19833b0d7b000002.
+		
+[1] The distribution of 4719 questions : 1417 factoid, 1271 yesno, 1130 summary, 901 list
+"""
+
+_BIOASQ_10B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ
+10, which will take place during 2022. There is one file containing the data:
+ - training10b.json
+
+The file contains the data of the first nine editions of the challenge: 4234
+questions [1] with their relevant documents, snippets, concepts and RDF
+triples, exact and ideal answers.
+
+Differences with BioASQ-training9b.json
+- 492 new questions added from BioASQ9
+    - The question with id 56c1f01eef6e394741000046 had identical body with
+    602498cb1cb411341a00009e. All relevant elements from both questions
+    are available in the merged question with id 602498cb1cb411341a00009e.
+    - The question with id 5c7039207c78d69471000065 had identical body with
+    601c317a1cb411341a000014. All relevant elements from both questions
+    are available in the merged question with id 601c317a1cb411341a000014.
+	- The question with id 5e4b540b6d0a27794100001c had identical body with
+    602828b11cb411341a0000fc. All relevant elements from both questions
+    are available in the merged question with id 602828b11cb411341a0000fc.
+    - The question with id 5fdb42fba43ad31278000027 had identical body with
+    5d35eb01b3a638076300000f. All relevant elements from both questions
+    are available in the merged question with id 5d35eb01b3a638076300000f.
+    - The question with id 601d76311cb411341a000045 had identical body with
+    6060732b94d57fd87900003d. All relevant elements from both questions
+    are available in the merged question with id 6060732b94d57fd87900003d.
+
+[1] 4234 questions : 1252 factoid, 1148 yesno, 1018 summary, 816 list
+"""
+
+_BIOASQ_9B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ 9,
+which will take place during 2021. There is one file containing the data:
+ - training9b.json
+
+The file contains the data of the first seven editions of the challenge: 3742
+questions [1] with their relevant documents, snippets, concepts and RDF triples,
+exact and ideal answers.
+
+Differences with BioASQ-training8b.json
+- 499 new questions added from BioASQ8
+    - The question with id 5e30e689fbd6abf43b00003a had identical body with
+    5880e417713cbdfd3d000001. All relevant elements from both questions
+    are available in the merged question with id 5880e417713cbdfd3d000001.
+
+[1] 3742 questions : 1091 factoid, 1033 yesno, 899 summary, 719 list
+"""
+
+_BIOASQ_8B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ 8,
+which will take place during 2020. There is one file containing the data:
+ - training8b.json
+
+The file contains the data of the first seven editions of the challenge: 3243
+questions [1] with their relevant documents, snippets, concepts and RDF triples,
+exact and ideal answers.
+
+Differences with BioASQ-training7b.json
+- 500 new questions added from BioASQ7
+- 4 questions were removed
+    - The question with id 5717fb557de986d80d000009 had identical body with
+    571e06447de986d80d000016. All relevant elements from both questions
+    are available in the merged question with id 571e06447de986d80d000016.
+    - The question with id 5c589ddb86df2b917400000b had identical body with
+    5c6b7a9e7c78d69471000029. All relevant elements from both questions
+    are available in the merged question with id 5c6b7a9e7c78d69471000029.
+    - The question with id 52ffb5d12059c6d71c00007c had identical body with
+    52e7870a98d023950500001a. All relevant elements from both questions
+    are available in the merged question with id 52e7870a98d023950500001a.
+    - The question with id 53359338d6d3ac6a3400004f had identical body with
+    589a246878275d0c4a000030. All relevant elements from both questions
+    are available in the merged question with id 589a246878275d0c4a000030.
+
+**** UPDATE 25/02/2020 *****
+The previous version of the dataset contained an inconsistency on question with
+id "5c9904eaecadf2e73f00002e", where the "ideal_answer" field was missing.
+This has been fixed.
+"""
+
+_BIOASQ_7B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ 7,
+which will take place during 2019. There is one file containing the data:
+ - BioASQ-trainingDataset7b.json
+
+The file contains the data of the first six editions of the challenge: 2747
+questions [1] with their relevant documents, snippets, concepts and RDF triples,
+exact and ideal answers.
+
+Differences with BioASQ-trainingDataset6b.json
+- 500 new questions added from BioASQ6
+    - 4 questions were removed
+        - The question with id 569ed752ceceede94d000004 had identical body with
+        a new question from BioASQ6. All relevant elements from both questions
+        are available in the merged question with id 5abd31e0fcf456587200002c
+        - 3 questions were removed as incomplete: 54d643023706e89528000007,
+        532819afd6d3ac6a3400000f, 517545168ed59a060a00002b
+    - 4 questions were revised for various confusions that have been identified
+        - In 2 questions the ideal answer has been revised :
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+        - In 4 questions the snippets and documents list has been revised :
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+        51593dc8d24251bc05000099, 5158a5b8d24251bc05000097
+        - In 198 questions the documents list has updated with missing
+        documents from the relevant snippets list. [2]
+
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+    5a95765bfcd1d6a10c000028, 5a96f40cfcd1d6a10c00002b, 5ab144fefcf4565872000012,
+    5aa67b4fd6d6b54f7900000f, 5abd5a62fcf4565872000031, 5abbe429fcf456587200001c,
+    5aaef38dfcf456587200000f, 5abce6acfcf4565872000022, 5aae6499fcf456587200000c
+"""
+
+_BIOASQ_6B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ 6,
+which will take place during 2018. There is one file containing the data:
+ - BioASQ-trainingDataset6b.json
+
+Differences with BioASQ-trainingDataset5b.json
+- 500 new questions added from BioASQ5
+    - 48 pairs of questions with identical bodies have been merged into one
+    question having only one question-id, but all the documents, snippets,
+    concepts, RDF triples and answers of both questions of the pair.
+        - This normalization lead to the removal of 48 deprecated question
+        ids [2] from the dataset and to the update of the 48 remaining
+        questions [3].
+        - In cases where a pair of questions with identical bodies had some
+        inconsistency (e.g. different question type), the inconsistency has
+        been solved merging the pair manually consulting the BioASQ expert team.
+    - 12 questions were revised for various confusions that have been
+    identified
+        - In 8 questions the question type has been changed to better suit to
+        the question body. The change of type lead to corresponding changes
+        in exact answers existence and format : 54fc4e2e6ea36a810c000003,
+        530b01a6970c65fa6b000008, 530cf54dab4de4de0c000009,
+        531b2fc3b166e2b80600003c, 532819afd6d3ac6a3400000f,
+        532aad53d6d3ac6a34000010, 5710ade4cf1c32585100002c,
+        52f65f372059c6d71c000027
+		- In 6 questions the ideal answer has been revised :
+        532aad53d6d3ac6a34000010, 5710ade4cf1c32585100002c,
+        53147b52e3eabad021000015, 5147c8a6d24251bc05000027,
+        5509bd6a1180f13250000002, 58bbb71f22d3005309000016
+		- In 5 questions the exact answer has been revised :
+        5314bd7ddae131f847000006, 53130a77e3eabad02100000f,
+        53148a07dae131f847000002, 53147b52e3eabad021000015,
+        5147c8a6d24251bc05000027
+		- In 2 questions the question body has been revised :
+        52f65f372059c6d71c000027, 5503145ee9bde69634000022
+    - In lists of ideal answers, documents, snippets, concepts and RDF triples
+    any duplicate identical elements have been removed.
+    - Ideal answers in format of one string have been converted to a list with
+    one element for consistency with cases where more than one golden ideal
+    answers are available. (i.e. "ideal_ans1" converted to ["ideal_ans1"])
+    - For yesno questions: All exact answers have been normalized to "yes" or
+    "no" (replacing "Yes", "YES" and "No")
+    - For factoid questions: The format of the exact answer was normalized to a
+    list of strings for each question, representing a set of synonyms
+    answering the question (i.e. [`ans1`, `syn11`, ... ]).
+    - For list questions: The format of the exact answer was normalized to a
+    list of lists. Each internal list represents one element of the answer
+    as a set of synonyms
+    (i.e. [[`ans1`, `syn11`, `syn12`], [`ans2`], [`ans3`, `syn31`] ...]).
+    - Empty elements, e.g. empty lists of documents have been removed.
+
+[1] 2251 questions : 619 factoid, 616 yesno, 531 summary, 485 list
+[2] The 48 deprecated question ids are : 52f8b2902059c6d71c000053,
+    52f11bf22059c6d71c000005, 52f77edb2059c6d71c000028, 52ed795098d0239505000032,
+    56d1a9baab2fed4a47000002, 52f7d3472059c6d71c00002f, 52fbe2bf2059c6d71c00006c,
+    52ec961098d023950500002a, 52e8e98298d0239505000020, 56cae5125795f9a73e000024,
+    530cefaaad0bf1360c000007, 530cefaaad0bf1360c000005, 52d63b2803868f1b0600003a,
+    530cefaaad0bf1360c00000a, 516425ff298dcd4e51000051, 55191149622b194345000010,
+    52fa70142059c6d71c000056, 52f77f4d2059c6d71c00002a, 52efc016c8da89891000001a,
+    52efc001c8da898910000019, 52f896ae2059c6d71c000045, 52eceada98d023950500002d,
+    52efc05cc8da89891000001c, 515e078e298dcd4e51000031, 52fe54252059c6d71c000079,
+    514217a6d24251bc05000005, 52d1389303868f1b06000032, 530cf4d5e2bfff940c000003,
+    52fc946d2059c6d71c000071, 52e8e99e98d0239505000021, 52ef7786c8da898910000015,
+    52d8494698d0239505000007, 530cf51d5610acba0c000001, 52f637972059c6d71c000025,
+    52e9f99798d0239505000025, 515de572298dcd4e51000021, 52fe4ad52059c6d71c000077,
+    52f65bf02059c6d71c000026, 52e8e9d298d0239505000022, 52fa74052059c6d71c00005a,
+    52ffbddf2059c6d71c00007d, 56bc932aac7ad1001900001c, 56c02883ef6e394741000017,
+    52d2b75403868f1b06000035, 52f118aa2059c6d71c000003, 52e929eb98d0239505000023,
+    532c12f2d6d3ac6a3400001d, 52d8466298d0239505000006'
+[3] The 48 questions resulting from merging with their pair have the
+    following ids: 5149aafcd24251bc05000045, 515db020298dcd4e51000011,
+    515db54c298dcd4e51000016, 51680a49298dcd4e51000062, 52b06a68f828ad283c000005,
+    52bf1aa503868f1b06000006, 52bf1af803868f1b06000008, 52bf1d6003868f1b0600000e,
+    52cb9b9b03868f1b0600002d, 52d2818403868f1b06000033, 52df887498d023950500000c,
+    52e0c9a298d0239505000010, 52e203bc98d0239505000011, 52e62bae98d0239505000015,
+    52e6c92598d0239505000019, 52e7bbf698d023950500001d, 52ea605098d0239505000028,
+    52ece29f98d023950500002c, 52ecf2dd98d023950500002e, 52ef7754c8da898910000014,
+    52f112bb2059c6d71c000002, 52f65f372059c6d71c000027, 52f77f752059c6d71c00002b,
+    52f77f892059c6d71c00002c, 52f89ee42059c6d71c00004d, 52f89f4f2059c6d71c00004e,
+    52f89fba2059c6d71c00004f, 52f89fc62059c6d71c000050, 52f89fd32059c6d71c000051,
+    52fa6ac72059c6d71c000055, 52fa73c62059c6d71c000058, 52fa73e82059c6d71c000059,
+    52fa74252059c6d71c00005b, 52fc8b772059c6d71c00006e, 52fc94572059c6d71c000070,
+    52fc94ae2059c6d71c000073, 52fc94db2059c6d71c000074, 52fe52702059c6d71c000078,
+    52fe58f82059c6d71c00007a, 530cefaaad0bf1360c000008, 530cefaaad0bf1360c000010,
+    533ba218fd9a95ea0d000007, 534bb147aeec6fbd07000014, 55167dec46478f2f2c00000a,
+    56c04412ef6e39474100001b, 56c1f01eef6e394741000046, 56c81fd15795f9a73e00000c,
+    587d016ed673c3eb14000002
+"""
+
+_BIOASQ_5B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ 5,
+which will take place during 2017. There is one file containing the data:
+ - BioASQ-trainingDataset5b.json
+
+The file contains the data of the first four editions of the challenge: 1799
+questions with their relevant documents, snippets, concepts and rdf triples,
+exact and ideal answers.
+"""
+
+_BIOASQ_4B_DESCRIPTION = """\
+The data are intended to be used as training and development data for BioASQ 4,
+which will take place during 2016. There is one file containing the data:
+ - BioASQ-trainingDataset4b.json
+
+The file contains the data of the first three editions of the challenge: 1307
+questions with their relevant documents, snippets, concepts and rdf triples,
+exact and ideal answers from the first two editions and 497 questions with
+similar annotations from the third editions of the challenge.
+"""
+
+_BIOASQ_3B_DESCRIPTION = """No README provided."""
+
+_BIOASQ_2B_DESCRIPTION = """No README provided."""
+
+_BIOASQ_BLURB_DESCRIPTION = """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.
+
+See 'Domain-Specific Language Model Pretraining for Biomedical
+Natural Language Processing' """
+
+_DESCRIPTION = {
+    "bioasq_11b": _BIOASQ_11B_DESCRIPTION,
+    "bioasq_10b": _BIOASQ_10B_DESCRIPTION,
+    "bioasq_9b": _BIOASQ_9B_DESCRIPTION,
+    "bioasq_8b": _BIOASQ_8B_DESCRIPTION,
+    "bioasq_7b": _BIOASQ_7B_DESCRIPTION,
+    "bioasq_6b": _BIOASQ_6B_DESCRIPTION,
+    "bioasq_5b": _BIOASQ_5B_DESCRIPTION,
+    "bioasq_4b": _BIOASQ_4B_DESCRIPTION,
+    "bioasq_3b": _BIOASQ_3B_DESCRIPTION,
+    "bioasq_2b": _BIOASQ_2B_DESCRIPTION,
+    "bioasq_blurb": _BIOASQ_BLURB_DESCRIPTION,
+}
+
+_HOMEPAGE = "http://participants-area.bioasq.org/datasets/"
+
+# Data access reqires registering with BioASQ.
+# See http://participants-area.bioasq.org/accounts/register/
+_LICENSE = "NLM_LICENSE"
+
+_URLs = {
+    "bioasq_11b": ["BioASQ-training11b.zip", "Task11BGoldenEnriched.zip"],
+    "bioasq_10b": ["BioASQ-training10b.zip", "Task10BGoldenEnriched.zip"],
+    "bioasq_9b": ["BioASQ-training9b.zip", "Task9BGoldenEnriched.zip"],
+    "bioasq_8b": ["BioASQ-training8b.zip", "Task8BGoldenEnriched.zip"],
+    "bioasq_7b": ["BioASQ-training7b.zip", "Task7BGoldenEnriched.zip"],
+    "bioasq_6b": ["BioASQ-training6b.zip", "Task6BGoldenEnriched.zip"],
+    "bioasq_5b": ["BioASQ-training5b.zip", "Task5BGoldenEnriched.zip"],
+    "bioasq_4b": ["BioASQ-training4b.zip", "Task4BGoldenEnriched.zip"],
+    "bioasq_3b": ["BioASQ-trainingDataset3b.zip", "Task3BGoldenEnriched.zip"],
+    "bioasq_2b": ["BioASQ-trainingDataset2b.zip", "Task2BGoldenEnriched.zip"],
+    "bioasq_blurb": ["BioASQ-training7b.zip", "Task7BGoldenEnriched.zip"],
+}
+
+# BLURB train and dev contain all yesno questions from the offical training split
+# test is all yesno question from the official test split
+_BLURB_SPLITS = {
+    "dev": {
+        "5313b049e3eabad021000013",
+        "553a8d78f321868558000003",
+        "5158a5b8d24251bc05000097",
+        "571e3d42bb137a4b0c000007",
+        "5175b97a8ed59a060a00002f",
+        "56c9e9d15795f9a73e00001d",
+        "56d19ffaab2fed4a47000001",
+        "518ccac0310faafe0800000b",
+        "56f12ca92ac5ed145900000e",
+        "51680a49298dcd4e51000062",
+        "5339ed7bd6d3ac6a34000060",
+        "516e5f33298dcd4e5100007e",
+        "5327139ad6d3ac6a3400000d",
+        "54e12ae3ae9738404b000004",
+        "5321b8579b2d7acc7e000008",
+        "514a4679d24251bc0500005b",
+        "54c12fd1f693c3b16b000001",
+        "52df887498d023950500000c",
+        "52f20d802059c6d71c00000a",
+        "532f0c4ed6d3ac6a3400002e",
+        "52b2f3b74003448f5500000c",
+        "52b2f1724003448f5500000b",
+        "515d9a42298dcd4e5100000d",
+        "5159b990d24251bc050000a3",
+        "54e12c30ae9738404b000005",
+        "553a6a9fbc4f83e82800001c",
+        "5509ec41c2af5d5b70000006",
+        "56cae40b5795f9a73e000022",
+        "51680b0e298dcd4e51000065",
+        "515df89e298dcd4e5100002f",
+        "54f49e56d0d681a040000004",
+        "571e3e2abb137a4b0c000008",
+        "515debe7298dcd4e51000026",
+        "56f6ab7009dd18d46b00000d",
+        "53302bced6d3ac6a34000039",
+        "5322de919b2d7acc7e000012",
+        "5709f212cf1c325851000020",
+        "5502abd1e9bde69634000008",
+        "516c220e298dcd4e51000071",
+        "5894597e7d9090f353000004",
+        "5895ec5e7d9090f353000015",
+        "58bbb8ae22d3005309000018",
+        "58bc58c302b8c60953000001",
+        "58c276bc02b8c60953000020",
+        "58c0825502b8c6095300001b",
+        "58ab1f6c9ef3c34033000002",
+        "58adbe999ef3c34033000005",
+        "58df3e408acda3452900002d",
+        "58dfec676fddd3e83e000006",
+        "58d8d0cc8acda34529000008",
+        "58b67fae22d3005309000009",
+        "58dbbbf08acda3452900001d",
+        "58dbba438acda3452900001c",
+        "58dbbdac8acda3452900001e",
+        "58dcbb8c8acda34529000021",
+        "5a468785966455904c00000d",
+        "5a70de5199e2c3af26000005",
+        "5a67a550b750ff4455000009",
+        "5a679875b750ff4455000004",
+        "5a7a44b4faa1ab7d2e000010",
+        "5a67ade5b750ff445500000c",
+        "5a8881118cb19eca6b000006",
+        "5a67b48cb750ff4455000010",
+        "5a679be1b750ff4455000005",
+        "5a7340962dc08e987e000017",
+        "5a737e233b9d13c70800000d",
+        "5a8dc57ffcd1d6a10c000025",
+        "5a6d186db750ff4455000031",
+        "5a70d43b99e2c3af26000003",
+        "5a70ec6899e2c3af2600000c",
+        "5a9ac4161d1251d03b000010",
+        "5a733d2a2dc08e987e000015",
+        "5a74acd80384be9551000006",
+        "5aa6800ad6d6b54f79000011",
+        "5a9d9ab94e03427e73000003",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class BioasqTaskBDataset(datasets.GeneratorBasedBuilder):
+    """
+    BioASQ Task B On Biomedical Semantic QA.
+    Creates configs for BioASQ2 through BioASQ10.
+    """
+
+    DEFAULT_CONFIG_NAME = "bioasq_9b_source"
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    # BioASQ2 through BioASQ11
+    BUILDER_CONFIGS = []
+    for version in range(2, 12):
+        BUILDER_CONFIGS.append(
+            BigBioConfig(
+                name=f"bioasq_{version}b_source",
+                version=SOURCE_VERSION,
+                description=f"bioasq{version} Task B source schema",
+                schema="source",
+                subset_id=f"bioasq_{version}b",
+            )
+        )
+
+        BUILDER_CONFIGS.append(
+            BigBioConfig(
+                name=f"bioasq_{version}b_bigbio_qa",
+                version=BIGBIO_VERSION,
+                description=f"bioasq{version} Task B in simplified BigBio schema",
+                schema="bigbio_qa",
+                subset_id=f"bioasq_{version}b",
+            )
+        )
+
+    # BLURB Benchmark config https://microsoft.github.io/BLURB/
+    BUILDER_CONFIGS.append(
+        BigBioConfig(
+            name=f"bioasq_blurb_bigbio_qa",
+            version=BIGBIO_VERSION,
+            description=f"BLURB benchmark in simplified BigBio schema",
+            schema="bigbio_qa",
+            subset_id=f"bioasq_blurb",
+        )
+    )
+
+    def _info(self):
+
+        # BioASQ Task B source schema
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                    "body": datasets.Value("string"),
+                    "documents": datasets.Sequence(datasets.Value("string")),
+                    "concepts": datasets.Sequence(datasets.Value("string")),
+                    "ideal_answer": datasets.Sequence(datasets.Value("string")),
+                    "exact_answer": datasets.Sequence(datasets.Value("string")),
+                    "triples": [
+                        {
+                            "p": datasets.Value("string"),
+                            "s": datasets.Value("string"),
+                            "o": datasets.Value("string"),
+                        }
+                    ],
+                    "snippets": [
+                        {
+                            "offsetInBeginSection": datasets.Value("int32"),
+                            "offsetInEndSection": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                            "beginSection": datasets.Value("string"),
+                            "endSection": datasets.Value("string"),
+                            "document": datasets.Value("string"),
+                        }
+                    ],
+                }
+            )
+        # simplified schema for QA tasks
+        elif self.config.schema == "bigbio_qa":
+            features = qa_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION[self.config.subset_id],
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _dump_gold_json(self, data_dir):
+        """
+        BioASQ test data is split into multiple records {9B1_golden.json,...,9B5_golden.json}
+        We combine these files into a single test set file 9Bx_golden.json
+        """
+        # BLURB is based on version 7
+        version = (
+            re.search(r"bioasq_([0-9]+)b", self.config.subset_id).group(1)
+            if "blurb" not in self.config.name
+            else "7"
+        )
+        gold_fpath = os.path.join(
+            data_dir, f"Task{version}BGoldenEnriched/bx_golden.json"
+        )
+
+        if not os.path.exists(gold_fpath):
+            # combine all gold json files
+            filelist = glob.glob(os.path.join(data_dir, "*/*.json"))
+            data = {"questions": []}
+            for fname in sorted(filelist):
+                with open(fname, "rt", encoding="utf-8") as file:
+                    data["questions"].extend(json.load(file)["questions"])
+            # dump gold to json
+            with open(gold_fpath, "wt", encoding="utf-8") as file:
+                json.dump(data, file, indent=2)
+
+        return f"Task{version}BGoldenEnriched/bx_golden.json"
+
+    def _blurb_split_generator(self, train_dir, test_dir):
+        """
+        Create splits for BLURB Benchmark
+        """
+        gold_fpath = self._dump_gold_json(test_dir)
+
+        # create train/dev splits from yesno questions
+        train_fpath = os.path.join(train_dir, "blurb_bioasq_train.json")
+        dev_fpath = os.path.join(train_dir, "blurb_bioasq_dev.json")
+
+        blurb_splits = {
+            "train": {"questions": []},
+            "dev": {"questions": []},
+            "test": {"questions": []},
+        }
+
+        if not os.path.exists(train_fpath):
+            data_fpath = os.path.join(train_dir, "BioASQ-training7b/trainining7b.json")
+            with open(data_fpath, "rt", encoding="utf-8") as file:
+                data = json.load(file)
+
+            for record in data["questions"]:
+                if record["type"] != "yesno":
+                    continue
+                if record["id"] in _BLURB_SPLITS["dev"]:
+                    blurb_splits["dev"]["questions"].append(record)
+                else:
+                    blurb_splits["train"]["questions"].append(record)
+
+            with open(train_fpath, "wt", encoding="utf-8") as file:
+                json.dump(blurb_splits["train"], file, indent=2)
+
+            with open(dev_fpath, "wt", encoding="utf-8") as file:
+                json.dump(blurb_splits["dev"], file, indent=2)
+
+        # create test split from yesno questions
+        with open(os.path.join(test_dir, gold_fpath), "rt", encoding="utf-8") as file:
+            data = json.load(file)
+
+        for record in data["questions"]:
+            if record["type"] != "yesno":
+                continue
+            blurb_splits["test"]["questions"].append(record)
+
+        test_fpath = os.path.join(test_dir, "blurb_bioasq_test.json")
+        with open(test_fpath, "wt", encoding="utf-8") as file:
+            json.dump(blurb_splits["test"], file, indent=2)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_fpath,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dev_fpath,
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_fpath,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+
+        train_dir, test_dir = dl_manager.download_and_extract(
+            [
+                os.path.join(self.config.data_dir, _url)
+                for _url in _URLs[self.config.subset_id]
+            ]
+        )
+        # create gold dump and get path
+        gold_fpath = self._dump_gold_json(test_dir)
+
+        # older versions of bioasq have different folder formats
+        train_fpaths = {
+            "bioasq_2b": "BioASQ_2013_TaskB/BioASQ-trainingDataset2b.json",
+            "bioasq_3b": "BioASQ-trainingDataset3b.json",
+            "bioasq_4b": "BioASQ-training4b/BioASQ-trainingDataset4b.json",
+            "bioasq_5b": "BioASQ-training5b/BioASQ-trainingDataset5b.json",
+            "bioasq_6b": "BioASQ-training6b/BioASQ-trainingDataset6b.json",
+            "bioasq_7b": "BioASQ-training7b/trainining7b.json",
+            "bioasq_8b": "training8b.json",  # HACK - this zipfile strips the dirname
+            "bioasq_9b": "BioASQ-training9b/training9b.json",
+            "bioasq_10b": "training10b.json",
+            "bioasq_11b": "BioASQ-training11b/training11b.json",
+        }
+
+        # BLURB has custom train/dev/test splits based on Task 7B
+        if "blurb" in self.config.name:
+            return self._blurb_split_generator(train_dir, test_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        train_dir, train_fpaths[self.config.subset_id]
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(test_dir, gold_fpath),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _get_exact_answer(self, record):
+        """The value exact_answer can be in different formats based on question type."""
+        if record["type"] == "yesno":
+            exact_answer = [record["exact_answer"]]
+        elif record["type"] == "summary":
+            exact_answer = []
+            # summary question types only have an ideal answer, so use that for bigbio
+            if self.config.schema == "bigbio_qa":
+                exact_answer = (
+                    record["ideal_answer"]
+                    if isinstance(record["ideal_answer"], list)
+                    else [record["ideal_answer"]]
+                )
+
+        elif record["type"] == "list":
+            exact_answer = record["exact_answer"]
+        elif record["type"] == "factoid":
+            # older version of bioasq sometimes represent this as as string
+            exact_answer = (
+                record["exact_answer"]
+                if isinstance(record["exact_answer"], list)
+                else [record["exact_answer"]]
+            )
+        return exact_answer
+
+    @staticmethod
+    def _normalize_yesno(yesno):
+        assert len(yesno) == 1, "There should be only one answer."
+        yesno = yesno[0]
+        # normalize answers like "Yes."
+        yesno = yesno.lower()
+        if yesno.startswith('yes'):
+            return ['yes']
+        elif yesno.startswith('no'):
+            return ['no']
+        else:
+            raise ValueError(f'Unrecognized yesno value: {yesno}')
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            with open(filepath, encoding="utf-8") as file:
+                data = json.load(file)
+                for i, record in enumerate(data["questions"]):
+                    yield i, {
+                        "id": record["id"],
+                        "type": record["type"],
+                        "body": record["body"],
+                        "documents": record["documents"],
+                        "concepts": record["concepts"] if "concepts" in record else [],
+                        "triples": record["triples"] if "triples" in record else [],
+                        "ideal_answer": record["ideal_answer"]
+                        if isinstance(record["ideal_answer"], list)
+                        else [record["ideal_answer"]],
+                        "exact_answer": self._get_exact_answer(record),
+                        "snippets": record["snippets"] if "snippets" in record else [],
+                    }
+
+        elif self.config.schema == "bigbio_qa":
+            # NOTE: Years 2014-2016 (BioASQ2-BioASQ4) have duplicate records
+            cache = set()
+            with open(filepath, encoding="utf-8") as file:
+                uid = 0
+                data = json.load(file)
+                for record in data["questions"]:
+                    # for questions that do not have snippets, skip
+                    if "snippets" not in record:
+                        continue
+
+                    choices = []
+                    answer = self._get_exact_answer(record)
+                    if record["type"] == 'yesno':
+                        choices = ['yes', 'no']
+                        answer = self._normalize_yesno(answer)
+
+                    for i, snippet in enumerate(record["snippets"]):
+                        key = f'{record["id"]}_{i}'
+                        # ignore duplicate records
+                        if key not in cache:
+                            cache.add(key)
+                            yield uid, {
+                                "id": key,
+                                "document_id": snippet["document"],
+                                "question_id": record["id"],
+                                "question": record["body"],
+                                "type": record["type"],
+                                "choices": choices,
+                                "context": snippet["text"],
+                                "answer": answer,
+                            }
+                            uid += 1

bioasq/task_b/training14b.json

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