readme: ---
  datasets:
  - relbert/semeval2012_relational_similarity_v2
  model-index:
  - name: relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce
    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: 0.911547619047619
    - 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: 0.5935828877005348
    - 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: 0.6023738872403561
    - 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: 0.7498610339077265
    - 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: 0.868
    - 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: 0.618421052631579
    - 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: 0.9190899502787404
      - name: F1 (macro)
        type: f1_macro
        value: 0.9137760457433256
    - 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: 0.854225352112676
      - name: F1 (macro)
        type: f1_macro
        value: 0.6960792498811619
    - 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: 0.6738894907908992
      - name: F1 (macro)
        type: f1_macro
        value: 0.6683142084374337
    - 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: 0.9637615636085414
      - name: F1 (macro)
        type: f1_macro
        value: 0.890107974704234
    - 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: 0.9053588216859918
      - name: F1 (macro)
        type: f1_macro
        value: 0.9023263285944801
  
  ---
  # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce
  
  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on  
  [relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
  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-v2-average-no-mask-prompt-e-nce/raw/main/analogy.json)):
      - Accuracy on SAT (full): 0.5935828877005348 
      - Accuracy on SAT: 0.6023738872403561
      - Accuracy on BATS: 0.7498610339077265
      - Accuracy on U2: 0.618421052631579
      - Accuracy on U4: 0.6365740740740741
      - Accuracy on Google: 0.868
  - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce/raw/main/classification.json)):
      - Micro F1 score 