model update
Browse files- README.md +236 -0
- analogy.json +1 -0
- classification.json +1 -0
- config.json +1 -1
- pytorch_model.bin +2 -2
- relation_mapping.json +1 -0
- tokenizer_config.json +1 -1
- trainer_config.json +1 -0
- validation_loss.json +1 -0
README.md
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+
---
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datasets:
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- relbert/semeval2012_relational_similarity_v6
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model-index:
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- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-1-child-prototypical
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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: 0.7409920634920635
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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: 0.45454545454545453
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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: 0.456973293768546
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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: 0.5591995553085047
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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: 0.756
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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: 0.4780701754385965
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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: 0.5023148148148148
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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: 0.9026668675606448
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- name: F1 (macro)
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type: f1_macro
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value: 0.8999151651110613
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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: 0.8164319248826291
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- name: F1 (macro)
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type: f1_macro
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value: 0.5989549450924441
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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: 0.624593716143012
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- name: F1 (macro)
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type: f1_macro
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value: 0.6078221576184047
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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: 0.9597273422828129
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- name: F1 (macro)
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type: f1_macro
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value: 0.8849571863295462
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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: 0.8812284550297712
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- name: F1 (macro)
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type: f1_macro
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value: 0.8759049665311546
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---
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# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-1-child-prototypical
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RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
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[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
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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-base-semeval2012-v6-average-prompt-e-nce-1-child-prototypical/raw/main/analogy.json)):
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- Accuracy on SAT (full): 0.45454545454545453
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- Accuracy on SAT: 0.456973293768546
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- Accuracy on BATS: 0.5591995553085047
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- Accuracy on U2: 0.4780701754385965
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- Accuracy on U4: 0.5023148148148148
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- Accuracy on Google: 0.756
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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-semeval2012-v6-average-prompt-e-nce-1-child-prototypical/raw/main/classification.json)):
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- Micro F1 score on BLESS: 0.9026668675606448
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- Micro F1 score on CogALexV: 0.8164319248826291
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- Micro F1 score on EVALution: 0.624593716143012
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- Micro F1 score on K&H+N: 0.9597273422828129
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- Micro F1 score on ROOT09: 0.8812284550297712
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- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-1-child-prototypical/raw/main/relation_mapping.json)):
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- Accuracy on Relation Mapping: 0.7409920634920635
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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-semeval2012-v6-average-prompt-e-nce-1-child-prototypical")
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vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
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```
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### Training hyperparameters
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The following hyperparameters were used during training:
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- model: roberta-base
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- max_length: 64
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- mode: average
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- data: relbert/semeval2012_relational_similarity_v6
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- split: train
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- split_eval: validation
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- template_mode: manual
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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: 9
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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: 1
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- exclude_relation: None
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- n_sample: 320
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- gradient_accumulation: 8
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- relation_level: None
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- data_level: child_prototypical
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The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-1-child-prototypical/raw/main/trainer_config.json).
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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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@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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analogy.json
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{"distance_function": "cosine_similarity", "sat/test": 0.456973293768546, "sat/valid": 0.43243243243243246, "u2/test": 0.4780701754385965, "u2/valid": 0.5, "u4/test": 0.5023148148148148, "u4/valid": 0.5, "google/test": 0.756, "google/valid": 0.84, "bats/test": 0.5591995553085047, "bats/valid": 0.5829145728643216, "sat_full": 0.45454545454545453}
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classification.json
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1 |
+
{"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": 0.9026668675606448, "test/f1_macro": 0.8999151651110613, "test/f1_micro": 0.9026668675606448, "test/p_macro": 0.895521757449707, "test/p_micro": 0.9026668675606448, "test/r_macro": 0.9053436481387277, "test/r_micro": 0.9026668675606448}, "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": 0.8164319248826291, "test/f1_macro": 0.5989549450924441, "test/f1_micro": 0.8164319248826291, "test/p_macro": 0.638213312514757, "test/p_micro": 0.8164319248826291, "test/r_macro": 0.5705560414681476, "test/r_micro": 0.8164319248826291}, "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": 0.624593716143012, "test/f1_macro": 0.6078221576184047, "test/f1_micro": 0.624593716143012, "test/p_macro": 0.6099995738179133, "test/p_micro": 0.624593716143012, "test/r_macro": 0.6088636621662724, "test/r_micro": 0.624593716143012}, "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": 0.9597273422828129, "test/f1_macro": 0.8849571863295462, "test/f1_micro": 0.9597273422828129, "test/p_macro": 0.9089084529413093, "test/p_micro": 0.9597273422828129, "test/r_macro": 0.8643820975632943, "test/r_micro": 0.9597273422828129}, "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": 0.8812284550297712, "test/f1_macro": 0.8759049665311546, "test/f1_micro": 0.8812284550297712, "test/p_macro": 0.8846082406613535, "test/p_micro": 0.8812284550297712, "test/r_macro": 0.8698212177678205, "test/r_micro": 0.8812284550297712}}
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config.json
CHANGED
@@ -1,5 +1,5 @@
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{
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"_name_or_path": "
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"architectures": [
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"RobertaModel"
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],
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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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pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size
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relation_mapping.json
ADDED
@@ -0,0 +1 @@
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|
|
|
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{"accuracy": 0.7409920634920635, "prediction": [{"source": ["solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity"], "true": ["atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism"], "pred": ["nucleus", "electron", "atom", "charge", "attracts", "revolves", "electromagnetism"], "alignment_match": false, "accuracy": 0.5714285714285714, "similarity": 0.8640188649889275, "similarity_true": 0.8503924966444448}, {"source": ["water", "flows", "pressure", "water tower", "bucket", "filling", "emptying", "hydrodynamics"], "true": ["heat", "transfers", "temperature", "burner", "kettle", "heating", "cooling", "thermodynamics"], "pred": ["heat", "transfers", "temperature", "burner", "kettle", "heating", "cooling", "thermodynamics"], "alignment_match": true, "accuracy": 1, "similarity": 0.8403915185233777, "similarity_true": 0.8403915185233777}, {"source": ["waves", "shore", "reflects", "water", "breakwater", "rough", "calm", "crashing"], "true": ["sounds", "wall", "echoes", "air", "insulation", "loud", "quiet", "vibrating"], "pred": ["echoes", "wall", "sounds", "air", "insulation", "loud", "quiet", "vibrating"], "alignment_match": false, "accuracy": 0.75, "similarity": 0.8407324053420951, "similarity_true": 0.8387133614846471}, {"source": ["combustion", "fire", "fuel", "burning", "hot", "intense", "oxygen", "carbon dioxide"], "true": ["respiration", "animal", "food", "breathing", "living", "vigorous", "oxygen", "carbon dioxide"], "pred": ["respiration", "animal", "food", "breathing", "living", "vigorous", "oxygen", "carbon dioxide"], "alignment_match": true, "accuracy": 1, "similarity": 0.8858986536615532, "similarity_true": 0.8858986536615532}, {"source": ["sound", "low", "high", "echoes", "loud", "quiet", "horn"], "true": ["light", "red", "violet", "reflects", "bright", "dim", "lens"], "pred": ["light", "red", "violet", "reflects", "bright", "dim", "lens"], "alignment_match": true, "accuracy": 1, "similarity": 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"container", "pressing", "moving", "cold", "hot"], "pred": ["molecules", "gas", "temperature", "container", "pressing", "moving", "cold", "hot"], "alignment_match": true, "accuracy": 1, "similarity": 0.8504612814608098, "similarity_true": 0.8504612814608098}, {"source": ["computer", "processing", "erasing", "write", "read", "memory", "outputs", "inputs", "bug"], "true": ["mind", "thinking", "forgetting", "memorize", "remember", "memory", "muscles", "senses", "mistake"], "pred": ["mind", "thinking", "forgetting", "remember", "memorize", "memory", "senses", "muscles", "mistake"], "alignment_match": false, "accuracy": 0.5555555555555556, "similarity": 0.8355841312905234, "similarity_true": 0.8336884135698523}, {"source": ["slot machines", "reels", "spinning", "winning", "losing"], "true": ["bacteria", "genes", "mutating", "reproducing", "dying"], "pred": ["genes", "bacteria", "mutating", "dying", "reproducing"], "alignment_match": false, "accuracy": 0.2, "similarity": 0.7489417912297349, 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tokenizer_config.json
CHANGED
@@ -6,7 +6,7 @@
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|
6 |
"errors": "replace",
|
7 |
"mask_token": "<mask>",
|
8 |
"model_max_length": 512,
|
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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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|
6 |
"errors": "replace",
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7 |
"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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|
trainer_config.json
ADDED
@@ -0,0 +1 @@
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|
|
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{"model": "roberta-base", "max_length": 64, "mode": "average", "data": "relbert/semeval2012_relational_similarity_v6", "split": "train", "split_eval": "validation", "template_mode": "manual", "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>\u2019s <mask>", "loss_function": "nce_logout", "classification_loss": false, "temperature_nce_constant": 0.05, "temperature_nce_rank": {"min": 0.01, "max": 0.05, "type": "linear"}, "epoch": 9, "batch": 128, "lr": 5e-06, "lr_decay": false, "lr_warmup": 1, "weight_decay": 0, "random_seed": 1, "exclude_relation": null, "n_sample": 320, "gradient_accumulation": 8, "relation_level": null, "data_level": "child_prototypical"}
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validation_loss.json
ADDED
@@ -0,0 +1 @@
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{"split": "validation", "loss": 6.0956643444712295, "data": "relbert/semeval2012_relational_similarity_v6", "exclude_relation": null, "relation_level": null, "level": "child_prototypical"}
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