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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/t_rex_relational_similarity
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+ model-index:
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+ - name: relbert/relbert-roberta-large-nce-b-t-rex
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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: 0.6948809523809524
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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: 0.41711229946524064
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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: 0.42729970326409494
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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: 0.5380767092829349
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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: 0.71
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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: 0.4166666666666667
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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: 0.4375
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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: 0.16359060402684564
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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: 0.726775956284153
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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: 0.7083333333333334
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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: 0.9044749133644719
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.9009622162474221
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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: 0.8241784037558686
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6323153920116971
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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: 0.6386782231852655
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6260641269237556
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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: 0.9524240105724421
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.867163454762703
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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: 0.8849890316515199
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8834890504724978
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+
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+ ---
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+ # relbert/relbert-roberta-large-nce-b-t-rex
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+
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+ RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_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-t-rex/raw/main/analogy.forward.json)):
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+ - Accuracy on SAT (full): 0.41711229946524064
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+ - Accuracy on SAT: 0.42729970326409494
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+ - Accuracy on BATS: 0.5380767092829349
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+ - Accuracy on U2: 0.4166666666666667
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+ - Accuracy on U4: 0.4375
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+ - Accuracy on Google: 0.71
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+ - Accuracy on ConceptNet Analogy: 0.16359060402684564
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+ - Accuracy on T-Rex Analogy: 0.726775956284153
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+ - Accuracy on NELL-ONE Analogy: 0.7083333333333334
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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-t-rex/raw/main/classification.json)):
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+ - Micro F1 score on BLESS: 0.9044749133644719
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+ - Micro F1 score on CogALexV: 0.8241784037558686
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+ - Micro F1 score on EVALution: 0.6386782231852655
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+ - Micro F1 score on K&H+N: 0.9524240105724421
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+ - Micro F1 score on ROOT09: 0.8849890316515199
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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-t-rex/raw/main/relation_mapping.json)):
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+ - Accuracy on Relation Mapping: 0.6948809523809524
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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-t-rex")
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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/t_rex_relational_similarity
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+ - data_name: filter_unified.min_entity_4_max_predicate_10
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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-t-rex/raw/main/finetuning_config.json).
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+
247
+ ### 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
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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": 0.46524064171123, "sat/test": 0.4658753709198813, "u2/test": 0.4166666666666667, "u4/test": 0.4675925925925926, "google/test": 0.724, "bats/test": 0.5108393551973318, "t_rex_relational_similarity/test": 0.7704918032786885, "conceptnet_relational_similarity/test": 0.17114093959731544, "nell_relational_similarity/test": 0.7383333333333333, "sat/validation": 0.4594594594594595, "u2/validation": 0.4166666666666667, "u4/validation": 0.5208333333333334, "google/validation": 0.74, "bats/validation": 0.5829145728643216, "semeval2012_relational_similarity/validation": 0.5316455696202531, "t_rex_relational_similarity/validation": 0.34274193548387094, "conceptnet_relational_similarity/validation": 0.14028776978417265, "nell_relational_similarity/validation": 0.5825}
analogy.forward.json ADDED
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+ {"t_rex_relational_similarity/validation": 0.3548387096774194, "sat_full/test": 0.41711229946524064, "sat/test": 0.42729970326409494, "u2/test": 0.4166666666666667, "u4/test": 0.4375, "google/test": 0.71, "bats/test": 0.5380767092829349, "t_rex_relational_similarity/test": 0.726775956284153, "conceptnet_relational_similarity/test": 0.16359060402684564, "nell_relational_similarity/test": 0.7083333333333334, "sat/validation": 0.32432432432432434, "u2/validation": 0.4583333333333333, "u4/validation": 0.4791666666666667, "google/validation": 0.7, "bats/validation": 0.5829145728643216, "semeval2012_relational_similarity/validation": 0.46835443037974683, "conceptnet_relational_similarity/validation": 0.14658273381294964, "nell_relational_similarity/validation": 0.585}
analogy.reverse.json ADDED
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+ {"sat_full/test": 0.4732620320855615, "sat/test": 0.4688427299703264, "u2/test": 0.41228070175438597, "u4/test": 0.4398148148148148, "google/test": 0.692, "bats/test": 0.4502501389660923, "t_rex_relational_similarity/test": 0.6885245901639344, "conceptnet_relational_similarity/test": 0.1476510067114094, "nell_relational_similarity/test": 0.62, "sat/validation": 0.5135135135135135, "u2/validation": 0.375, "u4/validation": 0.4791666666666667, "google/validation": 0.74, "bats/validation": 0.46733668341708545, "semeval2012_relational_similarity/validation": 0.5822784810126582, "t_rex_relational_similarity/validation": 0.23790322580645162, "conceptnet_relational_similarity/validation": 0.11151079136690648, "nell_relational_similarity/validation": 0.465}
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": 0.9044749133644719, "test/f1_macro": 0.9009622162474221, "test/f1_micro": 0.9044749133644719, "test/p_macro": 0.897560788538689, "test/p_micro": 0.9044749133644719, "test/r_macro": 0.9050118659118588, "test/r_micro": 0.9044749133644719}, "lexical_relation_classification/CogALexV": {"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": 0.8241784037558686, "test/f1_macro": 0.6323153920116971, "test/f1_micro": 0.8241784037558686, "test/p_macro": 0.6691337266405071, "test/p_micro": 0.8241784037558686, "test/r_macro": 0.6049471289136319, "test/r_micro": 0.8241784037558686}, "lexical_relation_classification/EVALution": {"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": 0.6386782231852655, "test/f1_macro": 0.6260641269237556, "test/f1_micro": 0.6386782231852655, "test/p_macro": 0.6291507280848447, "test/p_micro": 0.6386782231852655, "test/r_macro": 0.6262617518564334, "test/r_micro": 0.6386782231852655}, "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": 0.9524240105724421, "test/f1_macro": 0.867163454762703, "test/f1_micro": 0.9524240105724421, "test/p_macro": 0.8638609958779123, "test/p_micro": 0.9524240105724421, "test/r_macro": 0.8705756654288939, "test/r_micro": 0.9524240105724421}, "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": 0.8849890316515199, "test/f1_macro": 0.8834890504724978, "test/f1_micro": 0.8849890316515199, "test/p_macro": 0.8805420887840407, "test/p_micro": 0.8849890316515199, "test/r_macro": 0.8866807344978983, "test/r_micro": 0.8849890316515199}}
config.json CHANGED
@@ -1,5 +1,5 @@
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  {
2
- "_name_or_path": "relbert_output/ckpt/nce_t_rex_filter_unified.min_entity_4_max_predicate_10/template-b/epoch_7",
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  "architectures": [
4
  "RobertaModel"
5
  ],
 
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  {
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+ "_name_or_path": "roberta-large",
3
  "architectures": [
4
  "RobertaModel"
5
  ],
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",
4
+ "max_length": 64,
5
+ "epoch": 10,
6
+ "batch": 32,
7
+ "random_seed": 0,
8
+ "lr": 5e-06,
9
+ "lr_warmup": 10,
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+ "aggregation_mode": "average_no_mask",
11
+ "data": "relbert/t_rex_relational_similarity",
12
+ "data_name": "filter_unified.min_entity_4_max_predicate_10",
13
+ "exclude_relation": null,
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+ "split": "train",
15
+ "split_valid": "validation",
16
+ "loss_function": "nce",
17
+ "classification_loss": false,
18
+ "loss_function_config": {
19
+ "temperature": 0.05,
20
+ "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_t_rex_filter_unified.min_entity_4_max_predicate_10/template-b/epoch_7",
10
  "pad_token": "<pad>",
11
  "sep_token": "</s>",
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  "special_tokens_map_file": null,
 
6
  "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>",
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  "special_tokens_map_file": null,