readme: ---
  datasets:
  - relbert/semeval2012_relational_similarity
  model-index:
  - name: relbert/relbert-roberta-large-triplet-e-semeval2012
    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: 0.8003373015873015
    - 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: 0.56951871657754
    - 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: 0.5786350148367952
    - 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: 0.7476375764313508
    - 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: 0.902
    - 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: 0.5949074074074074
    - task:
        name: Analogy Questions (ConceptNet Analogy)
        type: multiple-choice-qa
      dataset:
        name: ConceptNet Analogy
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.3145973154362416
    - task:
        name: Analogy Questions (TREX Analogy)
        type: multiple-choice-qa
      dataset:
        name: TREX Analogy
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.5683060109289617
    - task:
        name: Analogy Questions (NELL-ONE Analogy)
        type: multiple-choice-qa
      dataset:
        name: NELL-ONE Analogy
        args: relbert/analogy_questions
        type: analogy-questions
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.61
    - 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: 0.9026668675606448
      - name: F1 (macro)
        type: f1_macro
        value: 0.901099542864961
    - 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: 0.8607981220657277
      - name: F1 (macro)
        type: f1_macro
        value: 0.703607951808097
    - 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: 0.6917659804983749
      - name: F1 (macro)
        type: f1_macro
        value: 0.690966207619381
    - 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: 0.9673088961535786
      - name: F1 (macro)
        type: f1_macro
        value: 0.8870352978589001
    - 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: 0.8975242870573488
      - name: F1 (macro)
        type: f1_macro
        value: 0.8979403889476659
  
  ---
  # relbert/relbert-roberta-large-triplet-e-semeval2012
  
  RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
  This model achieves the following