readme: ---
  datasets:
  - semeval2012
  model-index:
  - name: relbert/roberta-large-semeval2012-mask-prompt-d-triplet
    results:
    - task:
        name: Relation Mapping
        type: sorting-task
      dataset:
        name: Relation Mapping
        args: relbert/relation_mapping
        type: relation-mapping
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8038492063492063
    - task:
        name: Analogy Questions (SAT full)
        type: multiple-choice-qa
      dataset:
        name: SAT full
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6122994652406417
    - task:
        name: Analogy Questions (SAT)
        type: multiple-choice-qa
      dataset:
        name: SAT
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6231454005934718
    - task:
        name: Analogy Questions (BATS)
        type: multiple-choice-qa
      dataset:
        name: BATS
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7665369649805448
    - task:
        name: Analogy Questions (Google)
        type: multiple-choice-qa
      dataset:
        name: Google
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.948
    - task:
        name: Analogy Questions (U2)
        type: multiple-choice-qa
      dataset:
        name: U2
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6096491228070176
    - task:
        name: Analogy Questions (U4)
        type: multiple-choice-qa
      dataset:
        name: U4
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6388888888888888
    - task:
        name: Lexical Relation Classification (BLESS)
        type: classification
      dataset:
        name: BLESS
        args: relbert/lexical_relation_classification
        type: relation-classification
      metrics:
      - name: F1
        type: f1
        value: 0.9065843001356034
      - name: F1 (macro)
        type: f1_macro
        value: 0.9034391812068588
    - task:
        name: Lexical Relation Classification (CogALexV)
        type: classification
      dataset:
        name: CogALexV
        args: relbert/lexical_relation_classification
        type: relation-classification
      metrics:
      - name: F1
        type: f1
        value: 0.8814553990610329
      - name: F1 (macro)
        type: f1_macro
        value: 0.7370945104669245
    - task:
        name: Lexical Relation Classification (EVALution)
        type: classification
      dataset:
        name: BLESS
        args: relbert/lexical_relation_classification
        type: relation-classification
      metrics:
      - name: F1
        type: f1
        value: 0.7193932827735645
      - name: F1 (macro)
        type: f1_macro
        value: 0.6939557303629665
    - task:
        name: Lexical Relation Classification (K&H+N)
        type: classification
      dataset:
        name: K&H+N
        args: relbert/lexical_relation_classification
        type: relation-classification
      metrics:
      - name: F1
        type: f1
        value: 0.9527022327328372
      - name: F1 (macro)
        type: f1_macro
        value: 0.8646220605131613
    - task:
        name: Lexical Relation Classification (ROOT09)
        type: classification
      dataset:
        name: ROOT09
        args: relbert/lexical_relation_classification
        type: relation-classification
      metrics:
      - name: F1
        type: f1
        value: 0.900658100908806
      - name: F1 (macro)
        type: f1_macro
        value: 0.90318517559646
  
  ---
  # relbert/roberta-large-semeval2012-mask-prompt-d-triplet
  
  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on  
  [semeval2012](https://huggingface.co/datasets/semeval2012).
  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
  It achieves the following results on the relation understanding tasks:
  - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-triplet/raw/main/analogy.json)):
      - Accuracy on SAT (full): 0.6122994652406417 
      - Accuracy on SAT: 0.6231454005934718
      - Accuracy on BATS: 0.7665369649805448
      - Accuracy on U2: 0.6096491228070176
      - Accuracy on U4: 0.6388888888888888
      - Accuracy on Google: 0.948
  - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-triplet/raw/main/classification.json)):
      - Micro F1 score on BLESS: 0.9065843001356034
      - Micro F1 score on CogALexV: 0.8814553990610329
      - Micro F1 score on EVALution: 0.7193932827735645
   