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model update

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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-semeval2012-average-prompt-e-nce
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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: 93.57142857142857
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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: 0.6016042780748663
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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: 0.6023738872403561
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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: 0.7476375764313508
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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: 0.86
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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: 0.5482456140350878
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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: 0.6111111111111112
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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: 0.9193912912460449
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.9171163163754675
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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: 0.8481220657276995
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6734502135237685
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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: 0.685807150595883
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.679750083279063
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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: 0.962092230646171
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8868721386428041
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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: 0.898464431212786
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8953388906170653
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+
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+ ---
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+ # relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce
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+
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+ RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
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+ [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
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+ Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
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+ It 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-semeval2012-average-prompt-e-nce/raw/main/analogy.json)):
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+ - Accuracy on SAT (full): 0.6016042780748663
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+ - Accuracy on SAT: 0.6023738872403561
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+ - Accuracy on BATS: 0.7476375764313508
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+ - Accuracy on U2: 0.5482456140350878
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+ - Accuracy on U4: 0.6111111111111112
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+ - Accuracy on Google: 0.86
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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-semeval2012-average-prompt-e-nce/raw/main/classification.json)):
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+ - Micro F1 score on BLESS: 0.9193912912460449
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+ - Micro F1 score on CogALexV: 0.8481220657276995
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+ - Micro F1 score on EVALution: 0.685807150595883
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+ - Micro F1 score on K&H+N: 0.962092230646171
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+ - Micro F1 score on ROOT09: 0.898464431212786
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+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce/raw/main/relation_mapping.json)):
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+ - Accuracy on Relation Mapping: 93.57142857142857
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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-semeval2012-average-prompt-e-nce")
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+ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
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+ ```
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - model: roberta-large
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+ - max_length: 64
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+ - mode: average
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+ - data: relbert/semeval2012_relational_similarity
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+ - template_mode: manual
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+ - template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
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+ - loss_function: nce_logout
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+ - temperature_nce_constant: 0.05
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+ - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
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+ - epoch: 21
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+ - batch: 128
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+ - lr: 5e-06
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+ - lr_decay: False
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+ - lr_warmup: 1
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+ - weight_decay: 0
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+ - random_seed: 0
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+ - exclude_relation: None
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+ - n_sample: 640
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+ - gradient_accumulation: 8
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+
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+ The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-e-nce/raw/main/trainer_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.eacl-demos.7/).
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+
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+ ```
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+
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+ @inproceedings{ushio-etal-2021-distilling-relation-embeddings,
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+ title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
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+ author = "Ushio, Asahi and
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+ Schockaert, Steven and
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+ Camacho-Collados, Jose",
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+ booktitle = "EMNLP 2021",
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+ year = "2021",
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+ address = "Online",
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+ publisher = "Association for Computational Linguistics",
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+ }
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+
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+ ```