--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7953373015873015 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4197860962566845 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41839762611275966 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.594774874930517 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.774 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41898148148148145 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9005574807895134 - name: F1 (macro) type: f1_macro value: 0.8957958532235768 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8077464788732395 - name: F1 (macro) type: f1_macro value: 0.5900936140399187 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6359696641386782 - name: F1 (macro) type: f1_macro value: 0.6206497970461441 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9577797871600473 - name: F1 (macro) type: f1_macro value: 0.8748819835358477 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8633657160764651 - name: F1 (macro) type: f1_macro value: 0.8605769477843292 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4197860962566845 - Accuracy on SAT: 0.41839762611275966 - Accuracy on BATS: 0.594774874930517 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.41898148148148145 - Accuracy on Google: 0.774 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9005574807895134 - Micro F1 score on CogALexV: 0.8077464788732395 - Micro F1 score on EVALution: 0.6359696641386782 - Micro F1 score on K&H+N: 0.9577797871600473 - Micro F1 score on ROOT09: 0.8633657160764651 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7953373015873015 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - 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: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```