File size: 19,595 Bytes
cc2a0eb 6c960d1 c62a75c cc2a0eb c62a75c cc2a0eb 6c960d1 cc2a0eb c62a75c baa78c6 ee551b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
---
dataset_info:
- config_name: '16384'
features:
- name: input
dtype: string
- name: output
dtype: string
- name: metadata
struct:
- name: domains
sequence: string
- name: input_context
dtype: string
- name: output_context
dtype: string
- name: source_type
dtype: string
- name: task_family
dtype: string
- name: _instance_id
dtype: string
splits:
- name: train
num_bytes: 651887545
num_examples: 72646
- name: validation
num_bytes: 316306085
num_examples: 34621
- name: test
num_bytes: 422473879
num_examples: 41909
download_size: 623896235
dataset_size: 1390667509
- config_name: '4096'
features:
- name: input
dtype: string
- name: output
dtype: string
- name: metadata
struct:
- name: domains
sequence: string
- name: input_context
dtype: string
- name: output_context
dtype: string
- name: source_type
dtype: string
- name: task_family
dtype: string
- name: _instance_id
dtype: string
splits:
- name: train
num_bytes: 388072842
num_examples: 70521
- name: validation
num_bytes: 147030710
num_examples: 30736
- name: test
num_bytes: 186329809
num_examples: 35875
download_size: 308815650
dataset_size: 721433361
- config_name: '8192'
features:
- name: input
dtype: string
- name: output
dtype: string
- name: metadata
struct:
- name: domains
sequence: string
- name: input_context
dtype: string
- name: output_context
dtype: string
- name: source_type
dtype: string
- name: task_family
dtype: string
- name: _instance_id
dtype: string
splits:
- name: train
num_bytes: 546901470
num_examples: 72367
- name: validation
num_bytes: 252982177
num_examples: 34001
- name: test
num_bytes: 313157272
num_examples: 40064
download_size: 491399393
dataset_size: 1113040919
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.
## License
SciRIFF is licensed under `ODC-By`.
## 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.
| SciRIFF Name | Paper Link | License | Website / Download Link |
| :---------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------- | :----------------------------------------------------------------------------------------- |
| `acl_arc_intent_classification` | [ACL ARC](https://aclanthology.org/L08-1005/) | - | <https://github.com/allenai/scicite/> |
| `anat_em_ner` | [AnatEM](https://academic.oup.com/bioinformatics/article/30/6/868/285282) | CC BY | <https://nactem.ac.uk/anatomytagger/#AnatEM> |
| `annotated_materials_syntheses_events` | [Materials Science Procedural Text Corpus](https://aclanthology.org/W19-4007/) | MIT | <https://github.com/olivettigroup/annotated-materials-syntheses> |
| `bc7_litcovid_topic_classification` | [BioCreative VII LitCOVID](https://pubmed.ncbi.nlm.nih.gov/36043400/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/> |
| `bioasq_{factoid,general,list,yesno}_qa` | [BioASQ](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0564-6) | CC BY | <http://bioasq.org/> |
| `biored_ner` | [BioRED](https://academic.oup.com/bib/article/23/5/bbac282/6645993) | - | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/> |
| `cdr_ner` | [BioCreative V CDR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/> |
| `chemdner_ner` | [CHEMDNER](https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-7-S1-S2) | - | <https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/> |
| `chemprot_{ner,re}` | [BioCreative VI ChemProt](https://www.semanticscholar.org/paper/Overview-of-the-BioCreative-VI-chemical-protein-Krallinger-Rabal/eed781f498b563df5a9e8a241c67d63dd1d92ad5) | - | <https://biocreative.bioinformatics.udel.edu/news/corpora/chemprot-corpus-biocreative-vi/> |
| `chemsum_single_document_summarization` | [ChemSum](https://aclanthology.org/2023.acl-long.587/) | - | <https://github.com/griff4692/calibrating-summaries> |
| `chemtables_te` | [ChemTables](https://arxiv.org/abs/2305.14336) | GPL 3.0 | <https://huggingface.co/datasets/fbaigt/schema-to-json> |
| `chia_ner` | [Chia](https://www.nature.com/articles/s41597-020-00620-0) | CC BY | <https://github.com/WengLab-InformaticsResearch/CHIA> |
| `covid_deepset_qa` | [COVID-QA](https://aclanthology.org/2020.nlpcovid19-acl.18/) | Apache 2.0 | <https://github.com/deepset-ai/COVID-QA> |
| `covidfact_entailment` | [CovidFact](https://aclanthology.org/2021.acl-long.165/) | - | <https://github.com/asaakyan/covidfact> |
| `craftchem_ner` | [CRAFT-Chem](https://link.springer.com/chapter/10.1007/978-94-024-0881-2_53) | - | <https://huggingface.co/datasets/ghadeermobasher/CRAFT-Chem> |
| `data_reco_mcq_{mc,sc}` | [DataFinder](https://aclanthology.org/2023.acl-long.573/) | Apache 2.0 | <https://github.com/viswavi/datafinder/tree/main> |
| `ddi_ner` | [DDI](https://www.sciencedirect.com/science/article/pii/S1532046413001123) | CC BY | <https://github.com/isegura/DDICorpus> |
| `discomat_te` | [DISCoMaT](https://aclanthology.org/2023.acl-long.753/) | CC BY-SA | <https://github.com/M3RG-IITD/DiSCoMaT> |
| `drug_combo_extraction_re` | [Drug Combinations](https://aclanthology.org/2022.naacl-main.233/) | - | <https://github.com/allenai/drug-combo-extraction> |
| `evidence_inference` | [Evidence inference](https://aclanthology.org/2020.bionlp-1.13/) | MIT | <https://evidence-inference.ebm-nlp.com/> |
| `genia_ner` | [JNLPBA](https://aclanthology.org/W04-1213/) | CC BY | <https://github.com/spyysalo/jnlpba> |
| `gnormplus_ner` | [GNormPlus](https://www.hindawi.com/journals/bmri/2015/918710/) | - | <https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/> |
| `healthver_entailment` | [HealthVer](https://aclanthology.org/2021.findings-emnlp.297/) | nan | <https://github.com/sarrouti/healthver> |
| `linnaeus_ner` | [LINNAEUS](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85) | CC BY | <https://sourceforge.net/projects/linnaeus/> |
| `medmentions_ner` | [MedMentions](https://arxiv.org/abs/1902.09476) | CC 0 | <https://github.com/chanzuckerberg/MedMentions> |
| `mltables_te` | [AxCell](https://aclanthology.org/2020.emnlp-main.692/) | Apache 2.0 | <https://github.com/paperswithcode/axcell> |
| `mslr2022_cochrane_multidoc_summarization` | [Cochrane](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378607/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> |
| `mslr2022_ms2_multidoc_summarization` | [MS^2](https://aclanthology.org/2021.emnlp-main.594/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> |
| `multicite_intent_classification` | [MultiCite](https://aclanthology.org/2022.naacl-main.137/) | CC BY-NC | <https://github.com/allenai/multicite> |
| `multixscience_multidoc_summarization` | [Multi-XScience](https://aclanthology.org/2020.emnlp-main.648/) | MIT | <https://github.com/yaolu/Multi-XScience> |
| `mup_single_document_summarization` | [MUP](https://aclanthology.org/2022.sdp-1.32/) | Apache 2.0 | <https://github.com/allenai/mup> |
| `ncbi_ner` | [NCBI Disease](https://pubmed.ncbi.nlm.nih.gov/24393765/) | CC 0 | <https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/> |
| `nlmchem_ner` | [NLM-Chem](https://pubmed.ncbi.nlm.nih.gov/33767203/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/> |
| `nlmgene_ner` | [NLM-Gene](https://pubmed.ncbi.nlm.nih.gov/33839304/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene/> |
| `pico_ner` | [EBM-NLP PICO](https://aclanthology.org/P18-1019/) | - | <https://github.com/bepnye/EBM-NLP> |
| `pubmedqa_qa` | [PubMedQA](https://aclanthology.org/D19-1259/) | MIT | <https://github.com/pubmedqa/pubmedqa> |
| `qasa_abstractive_qa` | [QASA](https://proceedings.mlr.press/v202/lee23n) | MIT | <https://github.com/lgresearch/QASA> |
| `qasper_{abstractive,extractive}_qa` | [Qasper](https://aclanthology.org/2021.naacl-main.365/) | CC BY | <https://allenai.org/data/qasper> |
| `scicite_classification` | [SciCite](https://aclanthology.org/N19-1361/) | - | <https://allenai.org/data/scicite> |
| `scientific_lay_summarisation_`<br>`{elife,plos}_single_doc_summ` | [Lay Summarisation](https://aclanthology.org/2022.emnlp-main.724/) | - | <https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation> |
| `scientific_papers_summarization_`<br>`single_doc_{arxiv,pubmed}` | [Scientific Papers](https://aclanthology.org/N18-2097/) | - | <https://huggingface.co/datasets/armanc/scientific_papers> |
| `scierc_{ner,re}` | [SciERC](https://aclanthology.org/D18-1360/) | - | <http://nlp.cs.washington.edu/sciIE/> |
| `scifact_entailment` | [SciFact](https://aclanthology.org/2020.emnlp-main.609/) | CC BY-NC | <https://allenai.org/data/scifact> |
| `scireviewgen_multidoc_summarization` | [SciReviewGen](https://aclanthology.org/2023.findings-acl.418/) | CC BY-NC | <https://github.com/tetsu9923/SciReviewGen> |
| `scitldr_aic` | [SciTLDR](https://aclanthology.org/2020.findings-emnlp.428/) | Apache 2.0 | <https://github.com/allenai/scitldr> |
|