README.md

---
datasets:
- multimolecule/rnacentral
library_name: multimolecule
license: agpl-3.0
mask_token: <mask>
pipeline_tag: fill-mask
tags:
- Biology
- RNA
- ncRNA
- rna
widget:
- example_title: microRNA 21
  mask_index: 11
  mask_index_1based: 12
  masked_char: A
  output:
  - label: '?'
    score: 0.2009
  - label: '*'
    score: 0.159673
  - label: '|'
    score: 0.118655
  - label: I
    score: 0.117554
  - label: .
    score: 0.093048
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: UAGCUUAUCAG<mask>CUGAUGUUGA
- example_title: microRNA 146a
  mask_index: 10
  mask_index_1based: 11
  masked_char: A
  output:
  - label: '?'
    score: 0.228405
  - label: '*'
    score: 0.181964
  - label: '|'
    score: 0.134386
  - label: I
    score: 0.132831
  - label: .
    score: 0.10698
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: UGAGAACUGA<mask>UUCCAUGGGUU
- example_title: microRNA 155
  mask_index: 15
  mask_index_1based: 16
  masked_char: A
  output:
  - label: '?'
    score: 0.235441
  - label: '*'
    score: 0.188319
  - label: '|'
    score: 0.141343
  - label: I
    score: 0.137523
  - label: .
    score: 0.111508
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: UUAAUGCUAAUCGUG<mask>UAGGGGUU
- example_title: RNA component of mitochondrial RNA processing endoribonuclease
  mask_index: 11
  mask_index_1based: 12
  masked_char: A
  output:
  - label: A
    score: 0.622152
  - label: G
    score: 0.074581
  - label: X
    score: 0.062167
  - label: '?'
    score: 0.059036
  - label: '*'
    score: 0.042494
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: GGUUCGUGCUG<mask>AGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA
- example_title: 7SK small nuclear RNA
  mask_index: 13
  mask_index_1based: 14
  masked_char: A
  output:
  - label: A
    score: 0.855574
  - label: '?'
    score: 0.037714
  - label: '*'
    score: 0.032782
  - label: I
    score: 0.025124
  - label: '|'
    score: 0.023974
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: GGAUGUGAGGGCG<mask>UCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU
- example_title: telomerase RNA component
  mask_index: 23
  mask_index_1based: 24
  masked_char: A
  output:
  - label: '?'
    score: 0.227095
  - label: '*'
    score: 0.180311
  - label: '|'
    score: 0.134258
  - label: I
    score: 0.133277
  - label: .
    score: 0.105863
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: GGGUUGCGGAGGGUGGGCCUGGG<mask>GGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC
- example_title: vault RNA 2-1
  mask_index: 12
  mask_index_1based: 13
  masked_char: A
  output:
  - label: A
    score: 0.282629
  - label: '?'
    score: 0.141381
  - label: '*'
    score: 0.11284
  - label: G
    score: 0.085753
  - label: '|'
    score: 0.084345
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: CGGGUCGGAGUU<mask>GCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
  mask_index: 18
  mask_index_1based: 19
  masked_char: A
  output:
  - label: A
    score: 0.585389
  - label: '?'
    score: 0.098956
  - label: '*'
    score: 0.085764
  - label: '|'
    score: 0.062997
  - label: I
    score: 0.060588
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: GGCCGGGCGCGGUGGCUC<mask>CGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU
- example_title: HIV-1 TAR-WT
  mask_index: 13
  mask_index_1based: 14
  masked_char: A
  output:
  - label: A
    score: 0.216119
  - label: '?'
    score: 0.172431
  - label: '*'
    score: 0.133169
  - label: '|'
    score: 0.107029
  - label: I
    score: 0.10469
  pipeline_tag: fill-mask
  sequence_type: ncRNA
  task: fill-mask
  text: GGUCUCUCUGGUU<mask>GACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
- example_title: prion protein (Kanno blood group)
  mask_index: 21
  mask_index_1based: 22
  masked_char: A
  output:
  - label: '?'
    score: 0.19308
  - label: '*'
    score: 0.152793
  - label: '|'
    score: 0.114088
  - label: I
    score: 0.113779
  - label: C
    score: 0.095156
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AUGGCGAACCUUGGCUGCUGG<mask>UGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC
- example_title: interleukin 10
  mask_index: 11
  mask_index_1based: 12
  masked_char: A
  output:
  - label: '?'
    score: 0.208715
  - label: '*'
    score: 0.166324
  - label: '|'
    score: 0.125006
  - label: I
    score: 0.123702
  - label: .
    score: 0.099905
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AUGCACAGCUC<mask>GCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC
- example_title: Zaire ebolavirus
  mask_index: 11
  mask_index_1based: 12
  masked_char: A
  output:
  - label: '?'
    score: 0.197839
  - label: '*'
    score: 0.153979
  - label: '|'
    score: 0.113605
  - label: I
    score: 0.111712
  - label: U
    score: 0.103835
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AAUGUUCAAAC<mask>CUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU
- example_title: SARS coronavirus
  mask_index: 14
  mask_index_1based: 15
  masked_char: A
  output:
  - label: U
    score: 0.194165
  - label: '?'
    score: 0.164707
  - label: '*'
    score: 0.129908
  - label: '|'
    score: 0.09555
  - label: I
    score: 0.094492
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AUGUUUAUUUUCUU<mask>UUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU
- example_title: insulin
  mask_index: 12
  mask_index_1based: 13
  masked_char: A
  output:
  - label: C
    score: 0.261781
  - label: '?'
    score: 0.167051
  - label: '*'
    score: 0.133536
  - label: I
    score: 0.09981
  - label: '|'
    score: 0.099001
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AUGGCCCUGUGG<mask>UGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG
- example_title: cyclin dependent kinase inhibitor 2A
  mask_index: 18
  mask_index_1based: 19
  masked_char: A
  output:
  - label: '?'
    score: 0.190741
  - label: '*'
    score: 0.150517
  - label: '|'
    score: 0.113423
  - label: I
    score: 0.111556
  - label: G
    score: 0.095289
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AUGGAGCCGGCGGCGGGG<mask>GCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA
- example_title: human papillomavirus type 16 E6
  mask_index: 10
  mask_index_1based: 11
  masked_char: A
  output:
  - label: '?'
    score: 0.169333
  - label: '*'
    score: 0.133592
  - label: A
    score: 0.11662
  - label: G
    score: 0.109444
  - label: '|'
    score: 0.10071
  pipeline_tag: fill-mask
  sequence_type: mRNA
  task: fill-mask
  text: AUGCACCAAA<mask>GAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA
- example_title: NRAS proto-oncogene
  mask_index: 36
  mask_index_1based: 37
  masked_char: A
  output:
  - label: '?'
    score: 0.20744
  - label: '*'
    score: 0.163305
  - label: '|'
    score: 0.122595
  - label: I
    score: 0.121137
  - label: C
    score: 0.108824
  pipeline_tag: fill-mask
  sequence_type: 5' UTR
  task: fill-mask
  text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUC<mask>UGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA
- example_title: amyloid beta precursor protein
  mask_index: 15
  mask_index_1based: 16
  masked_char: A
  output:
  - label: '?'
    score: 0.224441
  - label: '*'
    score: 0.176997
  - label: '|'
    score: 0.133761
  - label: I
    score: 0.131265
  - label: .
    score: 0.105797
  pipeline_tag: fill-mask
  sequence_type: 5' UTR
  task: fill-mask
  text: GUCAGUUUCCUCGGC<mask>GCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG
- example_title: RUNX family transcription factor 1
  mask_index: 15
  mask_index_1based: 16
  masked_char: A
  output:
  - label: '?'
    score: 0.206538
  - label: '*'
    score: 0.160702
  - label: '|'
    score: 0.119129
  - label: I
    score: 0.118095
  - label: .
    score: 0.094544
  pipeline_tag: fill-mask
  sequence_type: 5' UTR
  task: fill-mask
  text: ACUUCUUUGGGCCUC<mask>UAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG
- example_title: fragile X messenger ribonucleoprotein 1
  mask_index: 15
  mask_index_1based: 16
  masked_char: A
  output:
  - label: '?'
    score: 0.195416
  - label: '*'
    score: 0.154775
  - label: I
    score: 0.11592
  - label: '|'
    score: 0.115607
  - label: G
    score: 0.105457
  pipeline_tag: fill-mask
  sequence_type: 5' UTR
  task: fill-mask
  text: CUCAGUCAGGCGCUC<mask>GCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG
- example_title: MYC proto-oncogene
  mask_index: 10
  mask_index_1based: 11
  masked_char: A
  output:
  - label: '?'
    score: 0.202977
  - label: '*'
    score: 0.16026
  - label: '|'
    score: 0.119362
  - label: I
    score: 0.117585
  - label: .
    score: 0.094434
  pipeline_tag: fill-mask
  sequence_type: 5' UTR
  task: fill-mask
  text: AACUCGCUGU<mask>GUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG
- example_title: activating transcription factor 4
  mask_index: 20
  mask_index_1based: 21
  masked_char: A
  output:
  - label: '?'
    score: 0.238453
  - label: '*'
    score: 0.189471
  - label: '|'
    score: 0.141617
  - label: I
    score: 0.139835
  - label: .
    score: 0.112254
  pipeline_tag: fill-mask
  sequence_type: 5' UTR
  task: fill-mask
  text: CAUUUCUACUUUGCCCGCCC<mask>CAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC
- example_title: Human GPI protein p137
  mask_index: 11
  mask_index_1based: 12
  masked_char: A
  output:
  - label: '?'
    score: 0.203726
  - label: '*'
    score: 0.160149
  - label: '|'
    score: 0.119915
  - label: I
    score: 0.118089
  - label: A
    score: 0.100465
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: UUUUUAAAAGG<mask>AAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC
- example_title: nucleophosmin 1
  mask_index: 11
  mask_index_1based: 12
  masked_char: A
  output:
  - label: '?'
    score: 0.175375
  - label: '*'
    score: 0.136246
  - label: U
    score: 0.107147
  - label: '|'
    score: 0.102165
  - label: I
    score: 0.100268
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: GAAAAUAGUUU<mask>AACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA
- example_title: superoxide dismutase 1
  mask_index: 12
  mask_index_1based: 13
  masked_char: A
  output:
  - label: '?'
    score: 0.180425
  - label: '*'
    score: 0.142506
  - label: '|'
    score: 0.105775
  - label: I
    score: 0.105049
  - label: C
    score: 0.101959
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: ACAUUCCCUUGG<mask>UGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA
- example_title: hemoglobin subunit alpha 2
  mask_index: 13
  mask_index_1based: 14
  masked_char: A
  output:
  - label: '?'
    score: 0.174786
  - label: G
    score: 0.166439
  - label: '*'
    score: 0.139318
  - label: '|'
    score: 0.103622
  - label: I
    score: 0.101995
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: CUGGAGCCUCGGU<mask>GCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA
- example_title: BRAF proto-oncogene
  mask_index: 12
  mask_index_1based: 13
  masked_char: A
  output:
  - label: '?'
    score: 0.182087
  - label: '*'
    score: 0.148305
  - label: A
    score: 0.147731
  - label: '|'
    score: 0.110948
  - label: I
    score: 0.106279
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: AACAAAUGAGUG<mask>GAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG
- example_title: H3 clustered histone 1
  mask_index: 17
  mask_index_1based: 18
  masked_char: A
  output:
  - label: '?'
    score: 0.199656
  - label: '*'
    score: 0.155685
  - label: '|'
    score: 0.118332
  - label: I
    score: 0.116603
  - label: .
    score: 0.092642
  pipeline_tag: fill-mask
  sequence_type: 3' UTR
  task: fill-mask
  text: UUACUGUGGUCUCUCUG<mask>CGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC
---

# RNA-FM

Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective.

## Disclaimer

This is an UNOFFICIAL implementation of the [Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions](https://doi.org/10.1101/2022.08.06.503062) by Jiayang Chen, Zhihang Hu, Siqi Sun, et al.

The OFFICIAL repository of RNA-FM is at [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM).

> [!TIP]
> The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.

**The team releasing RNA-FM did not write this model card for this model so this model card has been written by the MultiMolecule team.**

## Model Details

RNA-FM is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process.

### Variants

- **[multimolecule/rnafm](https://huggingface.co/multimolecule/rnafm)**: The RNA-FM model pre-trained on non-coding RNA sequences.
- **[multimolecule/mrnafm](https://huggingface.co/multimolecule/mrnafm)**: The RNA-FM model pre-trained on messenger RNA sequences.

### Model Specification

<table>
<thead>
  <tr>
    <th>Variants</th>
    <th>Num Layers</th>
    <th>Hidden Size</th>
    <th>Num Heads</th>
    <th>Intermediate Size</th>
    <th>Num Parameters (M)</th>
    <th>FLOPs (G)</th>
    <th>MACs (G)</th>
    <th>Max Num Tokens</th>
  </tr>
</thead>
<tbody>
  <tr>
    <td>mRNA-FM</td>
    <td rowspan="2">12</td>
    <td>1280</td>
    <td rowspan="2">20</td>
    <td rowspan="2">5120</td>
    <td>239.26</td>
    <td>258.08</td>
    <td>128.85</td>
    <td rowspan="2">1024</td>
  </tr>
  <tr>
    <td><b>RNA-FM</b></td>
    <td>640</td>
    <td>99.52</td>
    <td>109.02</td>
    <td>54.36</td>
  </tr>
</tbody>
</table>

### Links

- **Code**: [multimolecule.rnafm](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rnafm)
- **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral)
- **Paper**: [Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions](https://doi.org/10.1101/2022.08.06.503062)
- **Developed by**: Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li
- **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ESM](https://huggingface.co/facebook/esm2_t48_15B_UR50D)
- **Original Repository**: [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM)

## Usage

The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip:

```bash
pip install multimolecule
```

### Direct Use

#### Masked Language Modeling

You can use this model directly with a pipeline for masked language modeling:

```python
import multimolecule  # you must import multimolecule to register models
from transformers import pipeline

predictor = pipeline("fill-mask", model="multimolecule/rnafm")
output = predictor("gguc<mask>cucugguuagaccagaucugagccu")
```

### Downstream Use

#### Extract Features

Here is how to use this model to get the features of a given sequence in PyTorch:

```python
from multimolecule import RnaTokenizer, RnaFmModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmModel.from_pretrained("multimolecule/rnafm")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")

output = model(**input)
```

#### Sequence Classification / Regression

> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.

Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:

```python
import torch
from multimolecule import RnaTokenizer, RnaFmForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForSequencePrediction.from_pretrained("multimolecule/rnafm")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])

output = model(**input, labels=label)
```

#### Token Classification / Regression

> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.

Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:

```python
import torch
from multimolecule import RnaTokenizer, RnaFmForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForTokenPrediction.from_pretrained("multimolecule/rnafm")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))

output = model(**input, labels=label)
```

#### Contact Classification / Regression

> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.

Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:

```python
import torch
from multimolecule import RnaTokenizer, RnaFmForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForContactPrediction.from_pretrained("multimolecule/rnafm")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))

output = model(**input, labels=label)
```

## Training Details

RNA-FM used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.

### Training Data

The RNA-FM model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral).
RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types.

RNA-FM applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral. The final dataset contains 23.7 million non-redundant RNA sequences.

RNA-FM preprocessed all tokens by replacing "U"s with "T"s.

Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`.

### Training Procedure

#### Preprocessing

RNA-FM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:

- Mask rate: 15%
- Replacement: `<mask>` for 80% of masked tokens
- Replacement: random token for 10% of masked tokens
- Replacement: unchanged token for 10% of masked tokens

#### Pre-training

The model was trained on 8 NVIDIA A100 GPUs with 80GiB memories.

- Learning rate: 1e-4
- Learning rate scheduler: Inverse square root
- Learning rate warm-up: 10,000 steps
- Weight decay: 0.01

## Citation

```bibtex
@article{chen2022interpretable,
  title={Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions},
  author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and King, Irwin and others},
  journal={arXiv preprint arXiv:2204.00300},
  year={2022}
}
```

> [!NOTE]
> The artifacts distributed in this repository are part of the MultiMolecule project.
> If MultiMolecule supports your research, please cite the MultiMolecule project as follows:

```bibtex
@software{chen_2024_12638419,
  author    = {Chen, Zhiyuan and Zhu, Sophia Y.},
  title     = {MultiMolecule},
  doi       = {10.5281/zenodo.12638419},
  publisher = {Zenodo},
  url       = {https://doi.org/10.5281/zenodo.12638419},
  year      = 2024,
  month     = may,
  day       = 4
}
```

## Contact

Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card.

Please contact the authors of the [RNA-FM paper](https://doi.org/10.1101/2022.08.06.503062) for questions or comments on the paper/model.

## License

This model implementation is licensed under the [GNU Affero General Public License](license.md).

For additional terms and clarifications, please refer to our [License FAQ](license-faq.md).

```spdx
SPDX-License-Identifier: AGPL-3.0-or-later
```