SciRIFF

The SciRIFF dataset includes 137K instruction-following demonstrations for 54 scientific literature understanding tasks. The tasks cover five essential scientific literature categories and span five domains. The dataset is described in our paper SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature.

There are three dataset configurations with different max context lengths: 4096, 8192, and 16384. All experiments in the paper are performed with the 4096 context window. You can load the dataset like:

import datasets
ds = datasets.load_dataset("allenai/SciRIFF", "4096")

Code to create the dataset, train models on SciRIFF, and perform evaluation is available at our GitHub repo: https://github.com/allenai/SciRIFF. To train models on SciRIFF data, you should use the SciRIFF train mix dataset.

Table of Contents

Dataset details
License
Task provenance
Task metadata

Dataset details

Each instance in SciRIFF has the following fields:

input: Task input (i.e. user message).
output: Task output (i.e. expected model response).
_instance_id: A unique id for the instance, formatted like {task_name}:{split}:{instance_id}. For instance, qasa_abstractive_qa:test:182.
metadata: Task metadata. More information on the schema for task metadata can be found in the SciRIFF GitHub repo.
- task_family: The category to which this task belongs. Options include summarization, ie, qa, entailment, and classification. Some categories have sub-categories which are largely self-explanatory; see the repo for more information.
- domains: Scientific field(s) that the task covers. Options include: clinical_medicine, biomedicine, chemistry, artificial_intelligence, materials_science, and misc.
- input_context: Whether the input is a paragraph, full text, etc. Options include: sentence, paragraph, multiple_paragraphs (including full paper text), and structured (e.g. code for a LaTex table).
- source_type: Indicates whether the input comes from a single paper or multiple. Options include single_source, multiple_source.
- output_context: Options include: label, sentence, paragraph, multiple_paragraphs, json, jsonlines.

License

SciRIFF is licensed under ODC-By. Licenses of the datasets from which SciRIFF is derived are listed below.

Task provenance

SciRIFF was created by repurposing existing scientific literature understanding datasets. Below we provide information on the source data for each SciRIFF task, including license information on individual datasets where available. Where possible, we leveraged the BigBIO collection as a starting point, rather than reprocessing datasets from scratch. In the table below, we include the name of the BigBio subset for all tasks available in BigBio; these can be loaded like datasets.load_dataset(bigbio/{bigbio_subset}).

SciRIFF Name	Paper Link	License	Website / Download Link	BigBio Subset
`acl_arc_intent_classification`	ACL ARC	-	https://github.com/allenai/scicite/
`anat_em_ner`	AnatEM	CC BY	https://nactem.ac.uk/anatomytagger/#AnatEM	`anat_em`
`annotated_materials_syntheses_events`	Materials Science Procedural Text Corpus	MIT	https://github.com/olivettigroup/annotated-materials-syntheses
`bc7_litcovid_topic_classification`	BioCreative VII LitCOVID	-	https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/	`bc7_litcovid`
`bioasq_{factoid,general,list,yesno}_qa`	BioASQ	CC BY	http://bioasq.org/	`bioasq`
`biored_ner`	BioRED	-	https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/	`biored`
`cdr_ner`	BioCreative V CDR	-	https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/	`bc5cdr`
`chemdner_ner`	CHEMDNER	-	https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/	`chemdner`
`chemprot_{ner,re}`	BioCreative VI ChemProt	-	https://biocreative.bioinformatics.udel.edu/news/corpora/chemprot-corpus-biocreative-vi/	`chemprot`
`chemsum_single_document_summarization`	ChemSum	-	https://github.com/griff4692/calibrating-summaries
`chemtables_te`	ChemTables	GPL 3.0	https://huggingface.co/datasets/fbaigt/schema-to-json
`chia_ner`	Chia	CC BY	https://github.com/WengLab-InformaticsResearch/CHIA	`chia`
`covid_deepset_qa`	COVID-QA	Apache 2.0	https://github.com/deepset-ai/COVID-QA	`covid_qa_deepset`
`covidfact_entailment`	CovidFact	-	https://github.com/asaakyan/covidfact
`craftchem_ner`	CRAFT-Chem	-	https://huggingface.co/datasets/ghadeermobasher/CRAFT-Chem
`data_reco_mcq_{mc,sc}`	DataFinder	Apache 2.0	https://github.com/viswavi/datafinder/tree/main
`ddi_ner`	DDI	CC BY	https://github.com/isegura/DDICorpus	`ddi_corpus`
`discomat_te`	DISCoMaT	CC BY-SA	https://github.com/M3RG-IITD/DiSCoMaT
`drug_combo_extraction_re`	Drug Combinations	-	https://github.com/allenai/drug-combo-extraction
`evidence_inference`	Evidence inference	MIT	https://evidence-inference.ebm-nlp.com/
`genia_ner`	JNLPBA	CC BY	https://github.com/spyysalo/jnlpba	`jnlpba`
`gnormplus_ner`	GNormPlus	-	https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/	`gnormplus`
`healthver_entailment`	HealthVer	nan	https://github.com/sarrouti/healthver
`linnaeus_ner`	LINNAEUS	CC BY	https://sourceforge.net/projects/linnaeus/	`linnaeus`
`medmentions_ner`	MedMentions	CC 0	https://github.com/chanzuckerberg/MedMentions	`medmentions`
`mltables_te`	AxCell	Apache 2.0	https://github.com/paperswithcode/axcell
`mslr2022_cochrane_multidoc_summarization`	Cochrane	Apache 2.0	https://github.com/allenai/mslr-shared-task
`mslr2022_ms2_multidoc_summarization`	MS^2	Apache 2.0	https://github.com/allenai/mslr-shared-task
`multicite_intent_classification`	MultiCite	CC BY-NC	https://github.com/allenai/multicite
`multixscience_multidoc_summarization`	Multi-XScience	MIT	https://github.com/yaolu/Multi-XScience
`mup_single_document_summarization`	MUP	Apache 2.0	https://github.com/allenai/mup
`ncbi_ner`	NCBI Disease	CC 0	https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/	`ncbi_disease`
`nlmchem_ner`	NLM-Chem	CC 0	https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/	`nlmchem`
`nlmgene_ner`	NLM-Gene	CC 0	https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene/	`nlm_gene`
`pico_ner`	EBM-NLP PICO	-	https://github.com/bepnye/EBM-NLP	`pico_extraction`
`pubmedqa_qa`	PubMedQA	MIT	https://github.com/pubmedqa/pubmedqa	`pubmed_qa`
`qasa_abstractive_qa`	QASA	MIT	https://github.com/lgresearch/QASA
`qasper_{abstractive,extractive}_qa`	Qasper	CC BY	https://allenai.org/data/qasper
`scicite_classification`	SciCite	-	https://allenai.org/data/scicite
`scientific_lay_summarisation_` `{elife,plos}_single_doc_summ`	Lay Summarisation	-	https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
`scientific_papers_summarization_` `single_doc_{arxiv,pubmed}`	Scientific Papers	-	https://huggingface.co/datasets/armanc/scientific_papers
`scierc_{ner,re}`	SciERC	-	http://nlp.cs.washington.edu/sciIE/
`scifact_entailment`	SciFact	CC BY-NC	https://allenai.org/data/scifact
`scireviewgen_multidoc_summarization`	SciReviewGen	CC BY-NC	https://github.com/tetsu9923/SciReviewGen
`scitldr_aic`	SciTLDR	Apache 2.0	https://github.com/allenai/scitldr

Task metadata

Below we include metadata on each task, as described in the metadata fields above.

SciRIFF Name	Task Family	Domains	Input Context	Source Type	Output Context
`acl_arc_intent_classification`	classification	artificial_intelligence	multiple_paragraphs	single_source	label
`anat_em_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`annotated_materials_syntheses_events`	ie.event_extraction	materials_science	paragraph	single_source	json
`bc7_litcovid_topic_classification`	classification	clinical_medicine	paragraph	single_source	json
`bioasq_factoid_qa`	qa.abstractive	biomedicine	multiple_paragraphs	multiple_source	sentence
`bioasq_general_qa`	qa.abstractive	biomedicine	multiple_paragraphs	multiple_source	sentence
`bioasq_list_qa`	qa.abstractive	biomedicine	multiple_paragraphs	multiple_source	json
`bioasq_yesno_qa`	qa.yes_no	biomedicine	multiple_paragraphs	multiple_source	label
`biored_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`cdr_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`chemdner_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`chemprot_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`chemprot_re`	ie.relation_extraction	biomedicine	paragraph	single_source	json
`chemsum_single_document_summarization`	summarization	chemistry	multiple_paragraphs	single_source	paragraph
`chemtables_te`	ie.structure_to_json	chemistry	structured	single_source	jsonlines
`chia_ner`	ie.named_entity_recognition	clinical_medicine	paragraph	single_source	json
`covid_deepset_qa`	qa.extractive	biomedicine	paragraph	single_source	sentence
`covidfact_entailment`	entailment	biomedicine, clinical_medicine	paragraph	single_source	json
`craftchem_ner`	ie.named_entity_recognition	biomedicine	sentence	single_source	json
`data_reco_mcq_mc`	qa.multiple_choice	artificial_intelligence	multiple_paragraphs	multiple_source	json
`data_reco_mcq_sc`	qa.multiple_choice	artificial_intelligence	multiple_paragraphs	multiple_source	label
`ddi_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`discomat_te`	ie.structure_to_json	materials_science	structured	single_source	jsonlines
`drug_combo_extraction_re`	ie.relation_extraction	clinical_medicine	paragraph	single_source	json
`evidence_inference`	ie.relation_extraction	clinical_medicine	paragraph	single_source	json
`genia_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`gnormplus_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`healthver_entailment`	entailment	clinical_medicine	paragraph	single_source	json
`linnaeus_ner`	ie.named_entity_recognition	biomedicine	multiple_paragraphs	single_source	json
`medmentions_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`mltables_te`	ie.structure_to_json	artificial_intelligence	structured	single_source	jsonlines
`mslr2022_cochrane_multidoc_summarization`	summarization	clinical_medicine	paragraph	multiple_source	paragraph
`mslr2022_ms2_multidoc_summarization`	summarization	clinical_medicine	paragraph	multiple_source	paragraph
`multicite_intent_classification`	classification	artificial_intelligence	paragraph	single_source	json
`multixscience_multidoc_summarization`	summarization	artificial_intelligence, biomedicine, materials_science, misc	multiple_paragraphs	multiple_source	paragraph
`mup_single_document_summarization`	summarization	artificial_intelligence	multiple_paragraphs	single_source	paragraph
`ncbi_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`nlmchem_ner`	ie.named_entity_recognition	biomedicine	multiple_paragraphs	single_source	json
`nlmgene_ner`	ie.named_entity_recognition	biomedicine	paragraph	single_source	json
`pico_ner`	ie.named_entity_recognition	clinical_medicine	paragraph	single_source	json
`pubmedqa_qa`	qa.yes_no	biomedicine	paragraph	single_source	label
`qasa_abstractive_qa`	qa.abstractive	artificial_intelligence	multiple_paragraphs	single_source	paragraph
`qasper_abstractive_qa`	qa.abstractive	artificial_intelligence	multiple_paragraphs	single_source	json
`qasper_extractive_qa`	qa.extractive	artificial_intelligence	multiple_paragraphs	single_source	json
`scicite_classification`	classification	artificial_intelligence	paragraph	single_source	label
`scientific_lay_summarisation_` `elife_single_doc_summ`	summarization	biomedicine	multiple_paragraphs	single_source	paragraph
`scientific_lay_summarisation_` `plos_single_doc_summ`	summarization	biomedicine	multiple_paragraphs	single_source	paragraph
`scientific_papers_summarization_single_doc_arxiv`	summarization	artificial_intelligence, misc	multiple_paragraphs	single_source	paragraph
`scientific_papers_summarization_single_doc_pubmed`	summarization	biomedicine	multiple_paragraphs	single_source	paragraph
`scierc_ner`	ie.named_entity_recognition	artificial_intelligence	paragraph	single_source	json
`scierc_re`	ie.relation_extraction	artificial_intelligence	paragraph	single_source	json
`scifact_entailment`	entailment	biomedicine, clinical_medicine	paragraph	single_source	json
`scireviewgen_multidoc_summarization`	summarization	artificial_intelligence	multiple_paragraphs	multiple_source	paragraph
`scitldr_aic`	summarization	artificial_intelligence	multiple_paragraphs	single_source	sentence