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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-semeval2012-1
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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: 0.7540079365079365
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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: 0.6844919786096256
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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: 0.685459940652819
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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: 0.7971095052807116
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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: 0.934
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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: 0.6535087719298246
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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: 0.6504629629629629
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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: 0.44798657718120805
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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: 0.6502732240437158
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+ - task:
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+ name: Analogy Questions (NELL-ONE Analogy)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: NELL-ONE 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: 0.6733333333333333
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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: 0.9243634172065692
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.9210584903501834
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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: 0.8647887323943662
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.7127446509718055
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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: 0.6895991332611051
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6739419788779412
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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: 0.9527022327328372
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8701681183593395
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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: 0.8953306173613286
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8920689938172552
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+
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+ ---
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+ # relbert/relbert-roberta-large-nce-semeval2012-1
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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-semeval2012-1/raw/main/analogy.forward.json)):
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+ - Accuracy on SAT (full): 0.6844919786096256
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+ - Accuracy on SAT: 0.685459940652819
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+ - Accuracy on BATS: 0.7971095052807116
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+ - Accuracy on U2: 0.6535087719298246
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+ - Accuracy on U4: 0.6504629629629629
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+ - Accuracy on Google: 0.934
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+ - Accuracy on ConceptNet Analogy: 0.44798657718120805
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+ - Accuracy on T-Rex Analogy: 0.6502732240437158
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+ - Accuracy on NELL-ONE Analogy: 0.6733333333333333
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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-semeval2012-1/raw/main/classification.json)):
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+ - Micro F1 score on BLESS: 0.9243634172065692
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+ - Micro F1 score on CogALexV: 0.8647887323943662
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+ - Micro F1 score on EVALution: 0.6895991332611051
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+ - Micro F1 score on K&H+N: 0.9527022327328372
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+ - Micro F1 score on ROOT09: 0.8953306173613286
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+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-semeval2012-1/raw/main/relation_mapping.json)):
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+ - Accuracy on Relation Mapping: 0.7540079365079365
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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-semeval2012-1")
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+ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
223
+ ```
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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: 1
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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': 100, '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-semeval2012-1/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/).
249
+
250
+ ```
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+
252
+ @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
255
+ 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",
260
+ address = "Online and Punta Cana, Dominican Republic",
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+ publisher = "Association for Computational Linguistics",
262
+ 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",
266
+ }
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+
268
+ ```
analogy.forward.json ADDED
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+ {"semeval2012_relational_similarity/validation": 0.7468354430379747, "scan/test": 0.27042079207920794, "sat_full/test": 0.6844919786096256, "sat/test": 0.685459940652819, "u2/test": 0.6535087719298246, "u4/test": 0.6504629629629629, "google/test": 0.934, "bats/test": 0.7971095052807116, "t_rex_relational_similarity/test": 0.6502732240437158, "conceptnet_relational_similarity/test": 0.44798657718120805, "nell_relational_similarity/test": 0.6733333333333333}
classification.json ADDED
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+ {"lexical_relation_classification/BLESS": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9243634172065692, "test/f1_macro": 0.9210584903501834, "test/f1_micro": 0.9243634172065692, "test/p_macro": 0.9105248465838572, "test/p_micro": 0.9243634172065692, "test/r_macro": 0.9330081928345754, "test/r_micro": 0.9243634172065692, "test/f1/attri": 0.9138888888888889, "test/p/attri": 0.8844086021505376, "test/r/attri": 0.9454022988505747, "test/f1/coord": 0.9608621667612025, "test/p/coord": 0.9614074914869466, "test/r/coord": 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"hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.6895991332611051, "test/f1_macro": 0.6739419788779412, "test/f1_micro": 0.6895991332611051, "test/p_macro": 0.7007265450978665, "test/p_micro": 0.6895991332611051, "test/r_macro": 0.6565955304970702, "test/r_micro": 0.6895991332611051, "test/f1/Antonym": 0.8098765432098765, "test/p/Antonym": 0.830379746835443, "test/r/Antonym": 0.7903614457831325, "test/f1/HasA": 0.6466165413533835, "test/p/HasA": 0.6935483870967742, "test/r/HasA": 0.6056338028169014, "test/f1/HasProperty": 0.8377125193199382, "test/p/HasProperty": 0.8338461538461538, "test/r/HasProperty": 0.8416149068322981, "test/f1/IsA": 0.6430622009569378, "test/p/IsA": 0.5733788395904437, "test/r/IsA": 0.7320261437908496, "test/f1/MadeOf": 0.6357615894039734, "test/p/MadeOf": 0.7384615384615385, "test/r/MadeOf": 0.5581395348837209, "test/f1/PartOf": 0.7292418772563177, "test/p/PartOf": 0.7651515151515151, "test/r/PartOf": 0.696551724137931, "test/f1/Synonym": 0.4153225806451613, "test/p/Synonym": 0.4703196347031963, "test/r/Synonym": 0.37184115523465705}, "lexical_relation_classification/K&H+N": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9527022327328372, "test/f1_macro": 0.8701681183593395, "test/f1_micro": 0.9527022327328372, "test/p_macro": 0.8796338008860408, "test/p_micro": 0.9527022327328372, "test/r_macro": 0.8612759108696288, "test/r_micro": 0.9527022327328372, "test/f1/false": 0.9632255206025698, "test/p/false": 0.9596821659799882, "test/r/false": 0.9667951378594722, "test/f1/hypo": 0.9145679012345679, "test/p/hypo": 0.9420142421159715, "test/r/hypo": 0.8886756238003839, "test/f1/mero": 0.6439232409381662, "test/p/mero": 0.6593886462882096, "test/r/mero": 0.6291666666666667, "test/f1/sibl": 0.9589558106620538, "test/p/sibl": 0.9574501491599937, "test/r/sibl": 0.9604662151519925}, "lexical_relation_classification/ROOT09": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8953306173613287, "test/f1_macro": 0.8920689938172552, "test/f1_micro": 0.8953306173613286, "test/p_macro": 0.8973007374529117, "test/p_micro": 0.8953306173613287, "test/r_macro": 0.8883308156737318, "test/r_micro": 0.8953306173613287, "test/f1/COORD": 0.9720534629404617, "test/p/COORD": 0.963855421686747, "test/r/COORD": 0.9803921568627451, "test/f1/HYPER": 0.8044041450777202, "test/p/HYPER": 0.8448979591836735, "test/r/HYPER": 0.7676143386897404, "test/f1/RANDOM": 0.899749373433584, "test/p/RANDOM": 0.8831488314883149, "test/r/RANDOM": 0.9169859514687101}}
config.json ADDED
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1
+ {
2
+ "_name_or_path": "roberta-large",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "hidden_act": "gelu",
11
+ "hidden_dropout_prob": 0.1,
12
+ "hidden_size": 1024,
13
+ "initializer_range": 0.02,
14
+ "intermediate_size": 4096,
15
+ "layer_norm_eps": 1e-05,
16
+ "max_position_embeddings": 514,
17
+ "model_type": "roberta",
18
+ "num_attention_heads": 16,
19
+ "num_hidden_layers": 24,
20
+ "pad_token_id": 1,
21
+ "position_embedding_type": "absolute",
22
+ "relbert_config": {
23
+ "aggregation_mode": "average_no_mask",
24
+ "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>"
25
+ },
26
+ "torch_dtype": "float32",
27
+ "transformers_version": "4.26.1",
28
+ "type_vocab_size": 1,
29
+ "use_cache": true,
30
+ "vocab_size": 50265
31
+ }
finetuning_config.json ADDED
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