readme: ---
  datasets:
  - semeval2012
  model-index:
  - name: relbert/roberta-large-semeval2012-average-prompt-a-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: 0.868452380952381
    - 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: 0.606951871657754
    - 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: 0.6172106824925816
    - 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: 0.7420789327404114
    - 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: 0.898
    - 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: 0.6140350877192983
    - 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: 0.6365740740740741
    - 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: 0.8803676359801115
      - name: F1 (macro)
        type: f1_macro
        value: 0.873232855849543
    - 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: 0.8657276995305164
      - name: F1 (macro)
        type: f1_macro
        value: 0.7056969287828492
    - 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: 0.6841820151679306
      - name: F1 (macro)
        type: f1_macro
        value: 0.6709409425919224
    - 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: 0.9678653404743688
      - name: F1 (macro)
        type: f1_macro
        value: 0.8919612565868119
    - 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: 0.8962707615167659
      - name: F1 (macro)
        type: f1_macro
        value: 0.8890052485683796
  
  ---
  # relbert/roberta-large-semeval2012-average-prompt-a-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-average-prompt-a-triplet/raw/main/analogy.json)):
      - Accuracy on SAT (full): 0.606951871657754 
      - Accuracy on SAT: 0.6172106824925816
      - Accuracy on BATS: 0.7420789327404114
      - Accuracy on U2: 0.6140350877192983
      - Accuracy on U4: 0.6365740740740741
      - Accuracy on Google: 0.898
  - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-triplet/raw/main/classification.json)):
      - Micro F1 score on BLESS: 0.8803676359801115
      - Micro F1 score on CogALexV: 0.8657276995305164
      - Micro F1 score on EVALution: 0.6841820