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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/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated |
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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: 0.8476587301587302 |
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- task: |
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name: Analogy Questions (SAT full) |
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type: multiple-choice-qa |
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dataset: |
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name: SAT full |
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args: relbert/analogy_questions |
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type: analogy-questions |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5962566844919787 |
|
- task: |
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name: Analogy Questions (SAT) |
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type: multiple-choice-qa |
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dataset: |
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name: SAT |
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args: relbert/analogy_questions |
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type: analogy-questions |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5964391691394659 |
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- task: |
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name: Analogy Questions (BATS) |
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type: multiple-choice-qa |
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dataset: |
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name: BATS |
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args: relbert/analogy_questions |
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type: analogy-questions |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7559755419677598 |
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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: 0.87 |
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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: 0.5043859649122807 |
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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: 0.5902777777777778 |
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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: 0.9135151423836071 |
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- name: F1 (macro) |
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type: f1_macro |
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value: 0.9077476621792441 |
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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: 0.8568075117370892 |
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- name: F1 (macro) |
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type: f1_macro |
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value: 0.6862949146842514 |
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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 |
|
type: f1 |
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value: 0.6793066088840737 |
|
- name: F1 (macro) |
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type: f1_macro |
|
value: 0.6733689760415943 |
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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: 0.9559713431174793 |
|
- name: F1 (macro) |
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type: f1_macro |
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value: 0.8691131481598299 |
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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: 0.8934503290504543 |
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- name: F1 (macro) |
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type: f1_macro |
|
value: 0.8925413349776822 |
|
|
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--- |
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# relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated |
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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/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated/raw/main/analogy.json)): |
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- Accuracy on SAT (full): 0.5962566844919787 |
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- Accuracy on SAT: 0.5964391691394659 |
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- Accuracy on BATS: 0.7559755419677598 |
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- Accuracy on U2: 0.5043859649122807 |
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- Accuracy on U4: 0.5902777777777778 |
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- Accuracy on Google: 0.87 |
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- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated/raw/main/classification.json)): |
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- Micro F1 score on BLESS: 0.9135151423836071 |
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- Micro F1 score on CogALexV: 0.8568075117370892 |
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- Micro F1 score on EVALution: 0.6793066088840737 |
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- Micro F1 score on K&H+N: 0.9559713431174793 |
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- Micro F1 score on ROOT09: 0.8934503290504543 |
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- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated/raw/main/relation_mapping.json)): |
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- Accuracy on Relation Mapping: 0.8476587301587302 |
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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/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated") |
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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_no_mask |
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- data: relbert/semeval2012_relational_similarity |
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- split: train |
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- data_eval: relbert/conceptnet_high_confidence |
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- split_eval: full |
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- template_mode: manual |
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- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask> |
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- loss_function: nce_logout |
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- classification_loss: False |
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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: 29 |
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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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- exclude_relation_eval: 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/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated/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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@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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} |
|
|
|
``` |
|
|
