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model update

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README.md ADDED
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+ ---
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+ datasets:
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+ - relbert/semeval2012_relational_similarity
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+ model-index:
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+ - name: relbert/relbert-roberta-large-nce-b-semeval2012
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+ results:
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+ - task:
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+ name: Relation Mapping
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+ type: sorting-task
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+ dataset:
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+ name: Relation Mapping
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+ args: relbert/relation_mapping
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+ type: relation-mapping
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.844047619047619
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+ - task:
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+ name: Analogy Questions (SAT full)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: SAT full
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5962566844919787
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+ - task:
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+ name: Analogy Questions (SAT)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: SAT
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5964391691394659
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+ - task:
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+ name: Analogy Questions (BATS)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: BATS
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7759866592551418
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+ - task:
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+ name: Analogy Questions (Google)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: Google
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.902
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+ - task:
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+ name: Analogy Questions (U2)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: U2
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5526315789473685
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+ - task:
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+ name: Analogy Questions (U4)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: U4
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5717592592592593
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+ - task:
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+ name: Analogy Questions (ConceptNet Analogy)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: ConceptNet Analogy
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.3850671140939597
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+ - task:
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+ name: Analogy Questions (TREX Analogy)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: TREX Analogy
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5901639344262295
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+ - task:
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+ name: Lexical Relation Classification (BLESS)
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+ type: classification
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+ dataset:
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+ name: BLESS
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: None
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: None
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+ - task:
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+ name: Lexical Relation Classification (CogALexV)
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+ type: classification
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+ dataset:
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+ name: CogALexV
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: None
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: None
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+ - task:
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+ name: Lexical Relation Classification (EVALution)
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+ type: classification
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+ dataset:
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+ name: BLESS
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: None
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: None
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+ - task:
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+ name: Lexical Relation Classification (K&H+N)
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+ type: classification
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+ dataset:
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+ name: K&H+N
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: None
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: None
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+ - task:
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+ name: Lexical Relation Classification (ROOT09)
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+ type: classification
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+ dataset:
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+ name: ROOT09
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: None
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: None
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+
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+ ---
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+ # relbert/relbert-roberta-large-nce-b-semeval2012
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+
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+ 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).
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+ This model achieves the following results on the relation understanding tasks:
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+ - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012/raw/main/analogy.forward.json)):
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+ - Accuracy on SAT (full): 0.5962566844919787
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+ - Accuracy on SAT: 0.5964391691394659
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+ - Accuracy on BATS: 0.7759866592551418
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+ - Accuracy on U2: 0.5526315789473685
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+ - Accuracy on U4: 0.5717592592592593
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+ - Accuracy on Google: 0.902
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+ - Accuracy on ConceptNet Analogy: 0.3850671140939597
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+ - Accuracy on T-Rex Analogy: 0.5901639344262295
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+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012/raw/main/classification.json)):
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+ - Micro F1 score on BLESS: None
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+ - Micro F1 score on CogALexV: None
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+ - Micro F1 score on EVALution: None
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+ - Micro F1 score on K&H+N: None
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+ - Micro F1 score on ROOT09: None
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+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012/raw/main/relation_mapping.json)):
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+ - Accuracy on Relation Mapping: 0.844047619047619
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+
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+
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+ ### Usage
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+ This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
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+ ```shell
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+ pip install relbert
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+ ```
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+ and activate model as below.
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+ ```python
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+ from relbert import RelBERT
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+ model = RelBERT("relbert/relbert-roberta-large-nce-b-semeval2012")
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+ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
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+ ```
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+
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+ ### Training hyperparameters
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+
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+ - model: roberta-large
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+ - max_length: 64
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+ - epoch: 10
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+ - batch: 32
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+ - random_seed: 0
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+ - lr: 5e-06
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+ - lr_warmup: 10
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+ - aggregation_mode: average_no_mask
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+ - data: relbert/semeval2012_relational_similarity
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+ - data_name: None
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+ - exclude_relation: None
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+ - split: train
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+ - split_valid: validation
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+ - loss_function: nce
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+ - classification_loss: False
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+ - loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
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+ - augment_negative_by_positive: True
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+
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+ See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012/raw/main/finetuning_config.json).
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+
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+ ### Reference
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+ If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
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+
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+ ```
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+
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+ @inproceedings{ushio-etal-2021-distilling,
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+ title = "Distilling Relation Embeddings from Pretrained Language Models",
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+ author = "Ushio, Asahi and
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+ Camacho-Collados, Jose and
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+ Schockaert, Steven",
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+ booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
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+ month = nov,
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+ year = "2021",
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+ address = "Online and Punta Cana, Dominican Republic",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2021.emnlp-main.712",
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+ doi = "10.18653/v1/2021.emnlp-main.712",
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+ pages = "9044--9062",
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+ abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
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+ }
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+
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+ ```
analogy.bidirection.json ADDED
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+ {"sat_full/test": 0.660427807486631, "sat/test": 0.6646884272997032, "u2/test": 0.6491228070175439, "u4/test": 0.6388888888888888, "google/test": 0.93, "bats/test": 0.783212896053363, "t_rex_relational_similarity/test": 0.6065573770491803, "conceptnet_relational_similarity/test": 0.43288590604026844, "sat/validation": 0.6216216216216216, "u2/validation": 0.5416666666666666, "u4/validation": 0.6458333333333334, "google/validation": 1.0, "bats/validation": 0.8542713567839196, "semeval2012_relational_similarity/validation": 0.7215189873417721, "t_rex_relational_similarity/validation": 0.2661290322580645, "conceptnet_relational_similarity/validation": 0.35071942446043164}
analogy.forward.json ADDED
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+ {"semeval2012_relational_similarity/validation": 0.7341772151898734, "sat_full/test": 0.5962566844919787, "sat/test": 0.5964391691394659, "u2/test": 0.5526315789473685, "u4/test": 0.5717592592592593, "google/test": 0.902, "bats/test": 0.7759866592551418, "t_rex_relational_similarity/test": 0.5901639344262295, "conceptnet_relational_similarity/test": 0.3850671140939597, "sat/validation": 0.5945945945945946, "u2/validation": 0.5416666666666666, "u4/validation": 0.6041666666666666, "google/validation": 0.96, "bats/validation": 0.8140703517587939, "t_rex_relational_similarity/validation": 0.2540322580645161, "conceptnet_relational_similarity/validation": 0.33363309352517984}
analogy.reverse.json ADDED
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+ {"sat_full/test": 0.6524064171122995, "sat/test": 0.655786350148368, "u2/test": 0.6228070175438597, "u4/test": 0.6319444444444444, "google/test": 0.92, "bats/test": 0.7398554752640356, "t_rex_relational_similarity/test": 0.6010928961748634, "conceptnet_relational_similarity/test": 0.35067114093959734, "sat/validation": 0.6216216216216216, "u2/validation": 0.5, "u4/validation": 0.6041666666666666, "google/validation": 0.96, "bats/validation": 0.8190954773869347, "semeval2012_relational_similarity/validation": 0.6329113924050633, "t_rex_relational_similarity/validation": 0.2782258064516129, "conceptnet_relational_similarity/validation": 0.2841726618705036}
config.json CHANGED
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  {
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- "_name_or_path": "relbert_output/ckpt/nce_semeval2012/template-b/epoch_9",
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  "architectures": [
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  "RobertaModel"
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  ],
 
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  {
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+ "_name_or_path": "roberta-large",
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  "architectures": [
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  "RobertaModel"
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  ],
finetuning_config.json ADDED
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+ {
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+ "template": "Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>",
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+ "model": "roberta-large",
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+ "max_length": 64,
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+ "epoch": 10,
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+ "batch": 32,
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+ "random_seed": 0,
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+ "lr": 5e-06,
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+ "lr_warmup": 10,
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+ "aggregation_mode": "average_no_mask",
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+ "data": "relbert/semeval2012_relational_similarity",
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+ "data_name": null,
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+ "exclude_relation": null,
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+ "split": "train",
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+ "split_valid": "validation",
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+ "loss_function": "nce",
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+ "classification_loss": false,
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+ "loss_function_config": {
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+ "temperature": 0.05,
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+ "num_negative": 400,
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+ "num_positive": 10
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+ },
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+ "augment_negative_by_positive": true
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+ }
relation_mapping.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json CHANGED
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  "errors": "replace",
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  "mask_token": "<mask>",
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  "model_max_length": 512,
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- "name_or_path": "relbert_output/ckpt/nce_semeval2012/template-b/epoch_9",
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  "pad_token": "<pad>",
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  "sep_token": "</s>",
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  "special_tokens_map_file": null,
 
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  "errors": "replace",
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  "mask_token": "<mask>",
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  "model_max_length": 512,
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+ "name_or_path": "roberta-large",
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  "pad_token": "<pad>",
11
  "sep_token": "</s>",
12
  "special_tokens_map_file": null,