README.md

metadata

dataset_info:
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configs:
  - config_name: '16384'
    data_files:
      - split: train
        path: 16384/train-*
      - split: validation
        path: 16384/validation-*
      - split: test
        path: 16384/test-*
  - config_name: '4096'
    data_files:
      - split: train
        path: 4096/train-*
      - split: validation
        path: 4096/validation-*
      - split: test
        path: 4096/test-*
  - config_name: '8192'
    data_files:
      - split: train
        path: 8192/train-*
      - split: validation
        path: 8192/validation-*
      - split: test
        path: 8192/test-*
license: odc-by
language:
  - en
tags:
  - chemistry
  - biomedicine
  - clinical medicine
  - artificial intelligence
  - materials science
size_categories:
  - 100K<n<1M

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