---
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](https://arxiv.org/abs/2406.07835).

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:

```python
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](https://huggingface.co/datasets/allenai/SciRIFF-train-mix) dataset.

**Table of Contents**

- [Dataset details](#dataset-details)
- [License](#license)
- [Task provenance](#task-provenance)
- [Task metadata](#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](https://github.com/allenai/SciRIFF).
  - `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.
  - `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).

## 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](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})`.

| SciRIFF Name                                                      | Paper Link                                                                                                                                                                 | License    | Website / Download Link                                                                    | BigBio Subset      |
| :---------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------- | :----------------------------------------------------------------------------------------- | :----------------- |
| `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>                                               | `anat_em`          |
| `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/>               | `bc7_litcovid`     |
| `bioasq_{factoid,general,list,yesno}_qa`                          | [BioASQ](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0564-6)                                                                                   | CC BY      | <http://bioasq.org/>                                                                       | `bioasq`           |
| `biored_ner`                                                      | [BioRED](https://academic.oup.com/bib/article/23/5/bbac282/6645993)                                                                                                        | -          | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/>                                              | `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/>             | `bc5cdr`           |
| `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`         |
| `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`         |
| `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>                                      | `chia`             |
| `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` |
| `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>                                                     | `ddi_corpus`       |
| `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>                                                       | `jnlpba`           |
| `gnormplus_ner`                                                   | [GNormPlus](https://www.hindawi.com/journals/bmri/2015/918710/)                                                                                                            | -          | <https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/>                            | `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/>                                               | `linnaeus`         |
| `medmentions_ner`                                                 | [MedMentions](https://arxiv.org/abs/1902.09476)                                                                                                                            | CC 0       | <https://github.com/chanzuckerberg/MedMentions>                                            | `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/>                                  | `ncbi_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/>                                  | `nlmchem`          |
| `nlmgene_ner`                                                     | [NLM-Gene](https://pubmed.ncbi.nlm.nih.gov/33839304/)                                                                                                                      | CC 0       | <https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene/>                                             | `nlm_gene`         |
| `pico_ner`                                                        | [EBM-NLP PICO](https://aclanthology.org/P18-1019/)                                                                                                                         | -          | <https://github.com/bepnye/EBM-NLP>                                                        | `pico_extraction`  |
| `pubmedqa_qa`                                                     | [PubMedQA](https://aclanthology.org/D19-1259/)                                                                                                                             | MIT        | <https://github.com/pubmedqa/pubmedqa>                                                     | `pubmed_qa`        |
| `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>                                                       |                    |

## Task metadata

Below we include metadata on each task, as described in the metadata fields [above](#dataset-details).

| 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, <br> 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_`<br>`elife_single_doc_summ` | summarization               | biomedicine                                                        | multiple_paragraphs | single_source   | paragraph      |
| `scientific_lay_summarisation_`<br>`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       |