File size: 35,716 Bytes
cc2a0eb 6c960d1 c62a75c cc2a0eb c62a75c cc2a0eb 6c960d1 cc2a0eb c62a75c baa78c6 ee551b1 eddf83d a8997fc ac6d02a d450c49 501fca8 e4aa581 a8997fc d450c49 a8997fc ee551b1 d450c49 ee551b1 d450c49 a8997fc ee551b1 a8997fc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 |
---
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. 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 |
|