--- datasets: - relbert/conceptnet_high_confidence model-index: - name: relbert/roberta-large-conceptnet-hc-average-prompt-b-nce 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: 0.8097222222222222 - 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: 0.5106951871657754 - 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: 0.49554896142433236 - 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: 0.7982212340188994 - 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: 0.926 - 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: 0.5350877192982456 - 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: 0.6064814814814815 - 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: 0.9061322886846467 - name: F1 (macro) type: f1_macro value: 0.8998351544602654 - 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: 0.8483568075117371 - name: F1 (macro) type: f1_macro value: 0.6691324528607947 - 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: 0.6538461538461539 - name: F1 (macro) type: f1_macro value: 0.6461615360778927 - 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: 0.9576406760798497 - name: F1 (macro) type: f1_macro value: 0.8666219776970888 - 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: 0.8934503290504543 - name: F1 (macro) type: f1_macro value: 0.8921114555442471 --- # relbert/roberta-large-conceptnet-hc-average-prompt-b-nce RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence). 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/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5106951871657754 - Accuracy on SAT: 0.49554896142433236 - Accuracy on BATS: 0.7982212340188994 - Accuracy on U2: 0.5350877192982456 - Accuracy on U4: 0.6064814814814815 - Accuracy on Google: 0.926 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9061322886846467 - Micro F1 score on CogALexV: 0.8483568075117371 - Micro F1 score on EVALution: 0.6538461538461539 - Micro F1 score on K&H+N: 0.9576406760798497 - Micro F1 score on ROOT09: 0.8934503290504543 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8097222222222222 ### 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/roberta-large-conceptnet-hc-average-prompt-b-nce") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: average - data: relbert/conceptnet_high_confidence - template_mode: manual - template: Today, I finally discovered the relation between and : is 's - loss_function: nce_logout - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 87 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/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", } ```