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# SciRIFF |
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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](link.todo). |
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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: |
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```python |
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import datasets |
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ds = datasets.load_dataset("allenai/SciRIFF", "4096") |
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``` |
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Code to create the dataset, train models on SciRIFF, and perform evaluation is available at our GitHub repo: https://github.com/allenai/SciRIFF. |
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## Dataset details |
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Each instance in SciRIFF has the following fields: |
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- `input`: Task input (i.e. user message). |
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- `output`: Task output (i.e. expected model response). |
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- `_instance_id`: A unique id for the instance, formatted like `{task_name}:{split}:{instance_id}`. For instance, `qasa_abstractive_qa:test:182`. |
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- `metadata`: Task metadata. More information on the schema for task metadata can be found in the [SciRIFF GitHub repo](https://github.com/allenai/SciRIFF). |
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- `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](https://github.com/allenai/SciRIFF) for more information. |
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- `domains`: Scientific field(s) that the task covers. Options include: `clinical_medicine`, `biomedicine`, `chemistry`, `artificial_intelligence`, `materials_science`, and `misc`. |
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- `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). |
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- `source_type`: Indicates whether the input comes from a single paper or multiple. Options include `single_source`, `multiple_source`. |
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- `output_context`: Options include: `label`, `sentence`, `paragraph`, `multiple_paragraphs`, `json`, `jsonlines`. |
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## License |
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SciRIFF is licensed under `ODC-By`. Licenses of the datasets from which SciRIFF is derived are listed [below](#task-provenance). |
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## Task provenance |
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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](https://github.com/bigscience-workshop/biomedical) 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})`. |
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| SciRIFF Name | Paper Link | License | Website / Download Link | BigBio Subset | |
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| :---------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------- | :----------------------------------------------------------------------------------------- | :----------------- | |
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| `acl_arc_intent_classification` | [ACL ARC](https://aclanthology.org/L08-1005/) | - | <https://github.com/allenai/scicite/> | | |
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| `anat_em_ner` | [AnatEM](https://academic.oup.com/bioinformatics/article/30/6/868/285282) | CC BY | <https://nactem.ac.uk/anatomytagger/#AnatEM> | `anat_em` | |
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| `annotated_materials_syntheses_events` | [Materials Science Procedural Text Corpus](https://aclanthology.org/W19-4007/) | MIT | <https://github.com/olivettigroup/annotated-materials-syntheses> | | |
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| `bc7_litcovid_topic_classification` | [BioCreative VII LitCOVID](https://pubmed.ncbi.nlm.nih.gov/36043400/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/> | `bc7_litcovid` | |
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| `bioasq_{factoid,general,list,yesno}_qa` | [BioASQ](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0564-6) | CC BY | <http://bioasq.org/> | `bioasq` | |
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| `biored_ner` | [BioRED](https://academic.oup.com/bib/article/23/5/bbac282/6645993) | - | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/> | `biored` | |
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| `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/> | `bc5cdr` | |
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| `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/> | `chemdner` | |
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| `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/> | `chemprot` | |
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| `chemsum_single_document_summarization` | [ChemSum](https://aclanthology.org/2023.acl-long.587/) | - | <https://github.com/griff4692/calibrating-summaries> | | |
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| `chemtables_te` | [ChemTables](https://arxiv.org/abs/2305.14336) | GPL 3.0 | <https://huggingface.co/datasets/fbaigt/schema-to-json> | | |
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| `chia_ner` | [Chia](https://www.nature.com/articles/s41597-020-00620-0) | CC BY | <https://github.com/WengLab-InformaticsResearch/CHIA> | `chia` | |
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| `covid_deepset_qa` | [COVID-QA](https://aclanthology.org/2020.nlpcovid19-acl.18/) | Apache 2.0 | <https://github.com/deepset-ai/COVID-QA> | `covid_qa_deepset` | |
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| `covidfact_entailment` | [CovidFact](https://aclanthology.org/2021.acl-long.165/) | - | <https://github.com/asaakyan/covidfact> | | |
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| `craftchem_ner` | [CRAFT-Chem](https://link.springer.com/chapter/10.1007/978-94-024-0881-2_53) | - | <https://huggingface.co/datasets/ghadeermobasher/CRAFT-Chem> | | |
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| `data_reco_mcq_{mc,sc}` | [DataFinder](https://aclanthology.org/2023.acl-long.573/) | Apache 2.0 | <https://github.com/viswavi/datafinder/tree/main> | | |
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| `ddi_ner` | [DDI](https://www.sciencedirect.com/science/article/pii/S1532046413001123) | CC BY | <https://github.com/isegura/DDICorpus> | `ddi_corpus` | |
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| `discomat_te` | [DISCoMaT](https://aclanthology.org/2023.acl-long.753/) | CC BY-SA | <https://github.com/M3RG-IITD/DiSCoMaT> | | |
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| `drug_combo_extraction_re` | [Drug Combinations](https://aclanthology.org/2022.naacl-main.233/) | - | <https://github.com/allenai/drug-combo-extraction> | | |
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| `evidence_inference` | [Evidence inference](https://aclanthology.org/2020.bionlp-1.13/) | MIT | <https://evidence-inference.ebm-nlp.com/> | | |
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| `genia_ner` | [JNLPBA](https://aclanthology.org/W04-1213/) | CC BY | <https://github.com/spyysalo/jnlpba> | `jnlpba` | |
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| `gnormplus_ner` | [GNormPlus](https://www.hindawi.com/journals/bmri/2015/918710/) | - | <https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/> | `gnormplus` | |
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| `healthver_entailment` | [HealthVer](https://aclanthology.org/2021.findings-emnlp.297/) | nan | <https://github.com/sarrouti/healthver> | | |
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| `linnaeus_ner` | [LINNAEUS](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85) | CC BY | <https://sourceforge.net/projects/linnaeus/> | `linnaeus` | |
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| `medmentions_ner` | [MedMentions](https://arxiv.org/abs/1902.09476) | CC 0 | <https://github.com/chanzuckerberg/MedMentions> | `medmentions` | |
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| `mltables_te` | [AxCell](https://aclanthology.org/2020.emnlp-main.692/) | Apache 2.0 | <https://github.com/paperswithcode/axcell> | | |
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| `mslr2022_cochrane_multidoc_summarization` | [Cochrane](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378607/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> | | |
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| `mslr2022_ms2_multidoc_summarization` | [MS^2](https://aclanthology.org/2021.emnlp-main.594/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> | | |
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| `multicite_intent_classification` | [MultiCite](https://aclanthology.org/2022.naacl-main.137/) | CC BY-NC | <https://github.com/allenai/multicite> | | |
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| `multixscience_multidoc_summarization` | [Multi-XScience](https://aclanthology.org/2020.emnlp-main.648/) | MIT | <https://github.com/yaolu/Multi-XScience> | | |
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| `mup_single_document_summarization` | [MUP](https://aclanthology.org/2022.sdp-1.32/) | Apache 2.0 | <https://github.com/allenai/mup> | | |
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| `ncbi_ner` | [NCBI Disease](https://pubmed.ncbi.nlm.nih.gov/24393765/) | CC 0 | <https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/> | `ncbi_disease` | |
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| `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/> | `nlmchem` | |
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| `nlmgene_ner` | [NLM-Gene](https://pubmed.ncbi.nlm.nih.gov/33839304/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene/> | `nlm_gene` | |
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| `pico_ner` | [EBM-NLP PICO](https://aclanthology.org/P18-1019/) | - | <https://github.com/bepnye/EBM-NLP> | `pico_extraction` | |
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| `pubmedqa_qa` | [PubMedQA](https://aclanthology.org/D19-1259/) | MIT | <https://github.com/pubmedqa/pubmedqa> | `pubmed_qa` | |
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| `qasa_abstractive_qa` | [QASA](https://proceedings.mlr.press/v202/lee23n) | MIT | <https://github.com/lgresearch/QASA> | | |
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| `qasper_{abstractive,extractive}_qa` | [Qasper](https://aclanthology.org/2021.naacl-main.365/) | CC BY | <https://allenai.org/data/qasper> | | |
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| `scicite_classification` | [SciCite](https://aclanthology.org/N19-1361/) | - | <https://allenai.org/data/scicite> | | |
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| `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> | | |
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| `scientific_papers_summarization_`<br>`single_doc_{arxiv,pubmed}` | [Scientific Papers](https://aclanthology.org/N18-2097/) | - | <https://huggingface.co/datasets/armanc/scientific_papers> | | |
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| `scierc_{ner,re}` | [SciERC](https://aclanthology.org/D18-1360/) | - | <http://nlp.cs.washington.edu/sciIE/> | | |
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| `scifact_entailment` | [SciFact](https://aclanthology.org/2020.emnlp-main.609/) | CC BY-NC | <https://allenai.org/data/scifact> | | |
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| `scireviewgen_multidoc_summarization` | [SciReviewGen](https://aclanthology.org/2023.findings-acl.418/) | CC BY-NC | <https://github.com/tetsu9923/SciReviewGen> | | |
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| `scitldr_aic` | [SciTLDR](https://aclanthology.org/2020.findings-emnlp.428/) | Apache 2.0 | <https://github.com/allenai/scitldr> | | |
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## Task metadata |
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Below we include metadata on each task, as described in the metadata fields [above](#dataset-details). |
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| SciRIFF Name | Task Family | Domains | Input Context | Source Type | Output Context | |
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| :--------------------------------------------------------- | :-------------------------- | :----------------------------------------------------------------- | :------------------ | :-------------- | :------------- | |
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| `acl_arc_intent_classification` | classification | artificial_intelligence | multiple_paragraphs | single_source | label | |
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| `anat_em_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `annotated_materials_syntheses_events` | ie.event_extraction | materials_science | paragraph | single_source | json | |
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| `bc7_litcovid_topic_classification` | classification | clinical_medicine | paragraph | single_source | json | |
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| `bioasq_factoid_qa` | qa.abstractive | biomedicine | multiple_paragraphs | multiple_source | sentence | |
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| `bioasq_general_qa` | qa.abstractive | biomedicine | multiple_paragraphs | multiple_source | sentence | |
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| `bioasq_list_qa` | qa.abstractive | biomedicine | multiple_paragraphs | multiple_source | json | |
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| `bioasq_yesno_qa` | qa.yes_no | biomedicine | multiple_paragraphs | multiple_source | label | |
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| `biored_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `cdr_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `chemdner_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `chemprot_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `chemprot_re` | ie.relation_extraction | biomedicine | paragraph | single_source | json | |
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| `chemsum_single_document_summarization` | summarization | chemistry | multiple_paragraphs | single_source | paragraph | |
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| `chemtables_te` | ie.structure_to_json | chemistry | structured | single_source | jsonlines | |
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| `chia_ner` | ie.named_entity_recognition | clinical_medicine | paragraph | single_source | json | |
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| `covid_deepset_qa` | qa.extractive | biomedicine | paragraph | single_source | sentence | |
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| `covidfact_entailment` | entailment | biomedicine, clinical_medicine | paragraph | single_source | json | |
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| `craftchem_ner` | ie.named_entity_recognition | biomedicine | sentence | single_source | json | |
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| `data_reco_mcq_mc` | qa.multiple_choice | artificial_intelligence | multiple_paragraphs | multiple_source | json | |
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| `data_reco_mcq_sc` | qa.multiple_choice | artificial_intelligence | multiple_paragraphs | multiple_source | label | |
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| `ddi_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `discomat_te` | ie.structure_to_json | materials_science | structured | single_source | jsonlines | |
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| `drug_combo_extraction_re` | ie.relation_extraction | clinical_medicine | paragraph | single_source | json | |
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| `evidence_inference` | ie.relation_extraction | clinical_medicine | paragraph | single_source | json | |
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| `genia_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `gnormplus_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `healthver_entailment` | entailment | clinical_medicine | paragraph | single_source | json | |
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| `linnaeus_ner` | ie.named_entity_recognition | biomedicine | multiple_paragraphs | single_source | json | |
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| `medmentions_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `mltables_te` | ie.structure_to_json | artificial_intelligence | structured | single_source | jsonlines | |
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| `mslr2022_cochrane_multidoc_summarization` | summarization | clinical_medicine | paragraph | multiple_source | paragraph | |
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| `mslr2022_ms2_multidoc_summarization` | summarization | clinical_medicine | paragraph | multiple_source | paragraph | |
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| `multicite_intent_classification` | classification | artificial_intelligence | paragraph | single_source | json | |
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| `multixscience_multidoc_summarization` | summarization | artificial_intelligence, biomedicine, <br> materials_science, misc | multiple_paragraphs | multiple_source | paragraph | |
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| `mup_single_document_summarization` | summarization | artificial_intelligence | multiple_paragraphs | single_source | paragraph | |
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| `ncbi_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `nlmchem_ner` | ie.named_entity_recognition | biomedicine | multiple_paragraphs | single_source | json | |
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| `nlmgene_ner` | ie.named_entity_recognition | biomedicine | paragraph | single_source | json | |
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| `pico_ner` | ie.named_entity_recognition | clinical_medicine | paragraph | single_source | json | |
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| `pubmedqa_qa` | qa.yes_no | biomedicine | paragraph | single_source | label | |
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| `qasa_abstractive_qa` | qa.abstractive | artificial_intelligence | multiple_paragraphs | single_source | paragraph | |
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| `qasper_abstractive_qa` | qa.abstractive | artificial_intelligence | multiple_paragraphs | single_source | json | |
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| `qasper_extractive_qa` | qa.extractive | artificial_intelligence | multiple_paragraphs | single_source | json | |
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| `scicite_classification` | classification | artificial_intelligence | paragraph | single_source | label | |
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| `scientific_lay_summarisation_`<br>`elife_single_doc_summ` | summarization | biomedicine | multiple_paragraphs | single_source | paragraph | |
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| `scientific_lay_summarisation_`<br>`plos_single_doc_summ` | summarization | biomedicine | multiple_paragraphs | single_source | paragraph | |
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| `scientific_papers_summarization_single_doc_arxiv` | summarization | artificial_intelligence, misc | multiple_paragraphs | single_source | paragraph | |
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| `scientific_papers_summarization_single_doc_pubmed` | summarization | biomedicine | multiple_paragraphs | single_source | paragraph | |
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| `scierc_ner` | ie.named_entity_recognition | artificial_intelligence | paragraph | single_source | json | |
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| `scierc_re` | ie.relation_extraction | artificial_intelligence | paragraph | single_source | json | |
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| `scifact_entailment` | entailment | biomedicine, clinical_medicine | paragraph | single_source | json | |
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| `scireviewgen_multidoc_summarization` | summarization | artificial_intelligence | multiple_paragraphs | multiple_source | paragraph | |
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| `scitldr_aic` | summarization | artificial_intelligence | multiple_paragraphs | single_source | sentence | |
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