readme: ---
  datasets:
  - relbert/semeval2012_relational_similarity_v6
  model-index:
  - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1-child
    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: 0.8028968253968254
    - 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: 0.3770053475935829
    - 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: 0.3798219584569733
    - 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: 0.5041689827682045
    - 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: 0.634
    - 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: 0.39035087719298245
    - 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: 0.4097222222222222
    - 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: 0.7852945607955402
      - name: F1 (macro)
        type: f1_macro
        value: 0.7368732802407124
    - 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: 0.7643192488262912
      - name: F1 (macro)
        type: f1_macro
        value: 0.41730642887218183
    - 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: 0.6446370530877573
      - name: F1 (macro)
        type: f1_macro
        value: 0.6397488201890542
    - 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: 0.9114557974542672
      - name: F1 (macro)
        type: f1_macro
        value: 0.7869533397393593
    - 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: 0.8395487308053902
      - name: F1 (macro)
        type: f1_macro
        value: 0.8455958090420891
  
  ---
  # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1-child
  
  RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on  
  [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
  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/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1-child/raw/main/analogy.json)):
      - Accuracy on SAT (full): 0.3770053475935829 
      - Accuracy on SAT: 0.3798219584569733
      - Accuracy on BATS: 0.5041689827682045
      - Accuracy on U2: 0.39035087719298245
      - Accuracy on U4: 0.4097222222222222
      - Accuracy on Google: 0.634
  - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1-child/raw