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Models/lemm. inc. + early dmg

lemm. inc. + early dmg

Reported on 10 benchmarks across 2 tasks · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing10 results

  • Nested Term RecognitiononRuTermEval (Track 3)
    Scoreboard Class-agnostic F1· 2025-04-22
    0.5875
    SOTA
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Nested Term RecognitiononRuTermEval (Track 3)
    Scoreboard Weighted F1· 2025-04-22
    0.4547
    SOTA
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Nested Term RecognitiononRuTermEval (Track 1)
    Scoreboard F1· 2025-04-22
    0.7281
    SOTA
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Nested Term RecognitiononRuTermEval (Track 2)
    Scoreboard Class-agnostic F1· 2025-04-22
    0.7337
    SOTA
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Nested Term RecognitiononRuTermEval (Track 2)
    Scoreboard Weighted F1· 2025-04-22
    0.631
    SOTA
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Term ExtractiononRuTermEval (Track 3)
    Scoreboard Class-agnostic F1· 2025-04-22
    0.5875
    best: 0.6 (full nested)
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Term ExtractiononRuTermEval (Track 3)
    Scoreboard Weighted F1· 2025-04-22
    0.4547
    best: 0.4823 (full nested)
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Term ExtractiononRuTermEval (Track 1)
    Scoreboard F1· 2025-04-22
    0.7281
    best: 0.794 (full nested)
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Term ExtractiononRuTermEval (Track 2)
    Scoreboard Class-agnostic F1· 2025-04-22
    0.7337
    best: 0.78 (full nested)
    Methods for Recognizing Nested TermsarXiv:2504.16007
  • Term ExtractiononRuTermEval (Track 2)
    Scoreboard Weighted F1· 2025-04-22
    0.631
    best: 0.6997 (full nested)
    Methods for Recognizing Nested TermsarXiv:2504.16007