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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/conceptnet_relational_similarity
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+ model-index:
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+ - name: relbert/relbert-roberta-base-nce-d-conceptnet
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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: 0.8411507936507936
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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: 0.44919786096256686
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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: 0.4421364985163205
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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: 0.6197887715397443
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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: 0.81
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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: 0.42543859649122806
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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: 0.4097222222222222
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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: 0.2273489932885906
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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: 0.44808743169398907
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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: 0.6616666666666666
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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: 0.8880518306463764
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8803268244708621
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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: 0.828169014084507
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6164214086385178
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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: 0.628385698808234
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6170915381058782
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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: 0.9501982332892815
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8664191428414516
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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: 0.8884362268881228
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8877705847530848
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+
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+ ---
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+ # relbert/relbert-roberta-base-nce-d-conceptnet
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+
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+ RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_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-base-nce-d-conceptnet/raw/main/analogy.forward.json)):
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+ - Accuracy on SAT (full): 0.44919786096256686
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+ - Accuracy on SAT: 0.4421364985163205
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+ - Accuracy on BATS: 0.6197887715397443
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+ - Accuracy on U2: 0.42543859649122806
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+ - Accuracy on U4: 0.4097222222222222
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+ - Accuracy on Google: 0.81
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+ - Accuracy on ConceptNet Analogy: 0.2273489932885906
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+ - Accuracy on T-Rex Analogy: 0.44808743169398907
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+ - Accuracy on NELL-ONE Analogy: 0.6616666666666666
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+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-d-conceptnet/raw/main/classification.json)):
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+ - Micro F1 score on BLESS: 0.8880518306463764
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+ - Micro F1 score on CogALexV: 0.828169014084507
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+ - Micro F1 score on EVALution: 0.628385698808234
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+ - Micro F1 score on K&H+N: 0.9501982332892815
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+ - Micro F1 score on ROOT09: 0.8884362268881228
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+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-d-conceptnet/raw/main/relation_mapping.json)):
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+ - Accuracy on Relation Mapping: 0.8411507936507936
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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-base-nce-d-conceptnet")
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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-base
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+ - max_length: 64
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+ - epoch: 5
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+ - batch: 16
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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/conceptnet_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': 300, 'num_positive': 30}
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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-base-nce-d-conceptnet/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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+
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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",
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",
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+ }
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+
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+ ```
analogy.bidirection.json ADDED
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+ {"scan/test": 0.23824257425742573, "sat_full/test": 0.47058823529411764, "sat/test": 0.47774480712166173, "u2/test": 0.4473684210526316, "u4/test": 0.4375, "google/test": 0.82, "bats/test": 0.6236798221234019, "t_rex_relational_similarity/test": 0.5245901639344263, "conceptnet_relational_similarity/test": 0.2424496644295302, "nell_relational_similarity/test": 0.685, "scan/validation": 0.2303370786516854, "sat/validation": 0.40540540540540543, "u2/validation": 0.2916666666666667, "u4/validation": 0.5208333333333334, "google/validation": 0.92, "bats/validation": 0.6834170854271356, "semeval2012_relational_similarity/validation": 0.5316455696202531, "t_rex_relational_similarity/validation": 0.25, "conceptnet_relational_similarity/validation": 0.19514388489208634, "nell_relational_similarity/validation": 0.59}
analogy.forward.json ADDED
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+ {"conceptnet_relational_similarity/validation": 0.210431654676259, "scan/test": 0.21782178217821782, "sat_full/test": 0.44919786096256686, "sat/test": 0.4421364985163205, "u2/test": 0.42543859649122806, "u4/test": 0.4097222222222222, "google/test": 0.81, "bats/test": 0.6197887715397443, "t_rex_relational_similarity/test": 0.44808743169398907, "conceptnet_relational_similarity/test": 0.2273489932885906, "nell_relational_similarity/test": 0.6616666666666666, "scan/validation": 0.2303370786516854, "sat/validation": 0.5135135135135135, "u2/validation": 0.3333333333333333, "u4/validation": 0.4791666666666667, "google/validation": 0.86, "bats/validation": 0.6582914572864321, "semeval2012_relational_similarity/validation": 0.5443037974683544, "t_rex_relational_similarity/validation": 0.2157258064516129, "nell_relational_similarity/validation": 0.5075}
analogy.reverse.json ADDED
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+ {"scan/test": 0.21225247524752475, "sat_full/test": 0.45187165775401067, "sat/test": 0.456973293768546, "u2/test": 0.44298245614035087, "u4/test": 0.42824074074074076, "google/test": 0.774, "bats/test": 0.5892162312395776, "t_rex_relational_similarity/test": 0.4808743169398907, "conceptnet_relational_similarity/test": 0.18036912751677853, "nell_relational_similarity/test": 0.6633333333333333, "scan/validation": 0.19101123595505617, "sat/validation": 0.40540540540540543, "u2/validation": 0.3333333333333333, "u4/validation": 0.5833333333333334, "google/validation": 0.86, "bats/validation": 0.6331658291457286, "semeval2012_relational_similarity/validation": 0.4936708860759494, "t_rex_relational_similarity/validation": 0.20766129032258066, "conceptnet_relational_similarity/validation": 0.1447841726618705, "nell_relational_similarity/validation": 0.545}
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": 0.8880518306463764, "test/f1_macro": 0.8803268244708621, "test/f1_micro": 0.8880518306463764, "test/p_macro": 0.9004876805299403, "test/p_micro": 0.8880518306463764, "test/r_macro": 0.8661721033380787, "test/r_micro": 0.8880518306463764}, "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": 0.828169014084507, "test/f1_macro": 0.6164214086385178, "test/f1_micro": 0.828169014084507, "test/p_macro": 0.64146448484567, "test/p_micro": 0.828169014084507, "test/r_macro": 0.5966409692885033, "test/r_micro": 0.828169014084507}, "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": 0.628385698808234, "test/f1_macro": 0.6170915381058782, "test/f1_micro": 0.628385698808234, "test/p_macro": 0.6256173998030887, "test/p_micro": 0.628385698808234, "test/r_macro": 0.6177473867157622, "test/r_micro": 0.628385698808234}, "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": 0.9501982332892815, "test/f1_macro": 0.8664191428414516, "test/f1_micro": 0.9501982332892815, "test/p_macro": 0.885748000499009, "test/p_micro": 0.9501982332892815, "test/r_macro": 0.8507763135824798, "test/r_micro": 0.9501982332892815}, "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": 0.8884362268881228, "test/f1_macro": 0.8877705847530848, "test/f1_micro": 0.8884362268881228, "test/p_macro": 0.8824346825741686, "test/p_micro": 0.8884362268881228, "test/r_macro": 0.8944034100133793, "test/r_micro": 0.8884362268881228}}
config.json ADDED
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+ {
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+ "_name_or_path": "roberta-base",
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+ "architectures": [
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+ "RobertaModel"
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+ ],
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "relbert_config": {
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+ "aggregation_mode": "average_no_mask",
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+ "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>"
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+ },
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.26.1",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 50265
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+ }
finetuning_config.json ADDED
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+ {
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+ "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>",
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+ "model": "roberta-base",
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+ "lr": 5e-06,
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+ "data": "relbert/conceptnet_relational_similarity",
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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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+ "temperature": 0.05,
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+ "num_negative": 300,
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+ "num_positive": 30
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+ },
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+ "augment_negative_by_positive": true
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+ }
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relation_mapping.json ADDED
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special_tokens_map.json ADDED
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+ {
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+ "bos_token": "<s>",
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+ "cls_token": "<s>",
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+ "eos_token": "</s>",
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+ "mask_token": {
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+ "content": "<mask>",
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+ "lstrip": true,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "pad_token": "<pad>",
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+ "sep_token": "</s>",
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+ "unk_token": "<unk>"
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+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {
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+ "add_prefix_space": false,
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+ "bos_token": "<s>",
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+ "cls_token": "<s>",
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+ "eos_token": "</s>",
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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-base",
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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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+ "tokenizer_class": "RobertaTokenizer",
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+ "trim_offsets": true,
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+ "unk_token": "<unk>"
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+ }
vocab.json ADDED
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