relbert/roberta-large-semeval2012-average-prompt-c-nce-classification-conceptnet-validated
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accuracy on SAT (full): 0.31283422459893045
- Accuracy on SAT: 0.3086053412462908
- Accuracy on BATS: 0.46192329071706506
- Accuracy on U2: 0.34649122807017546
- Accuracy on U4: 0.3611111111111111
- Accuracy on Google: 0.63
- Lexical Relation Classification (dataset, full result):
- Micro F1 score on BLESS: 0.8457134247400934
- Micro F1 score on CogALexV: 0.846244131455399
- Micro F1 score on EVALution: 0.6262188515709642
- Micro F1 score on K&H+N: 0.9545802323155039
- Micro F1 score on ROOT09: 0.9044186775305547
- Relation Mapping (dataset, full result):
- Accuracy on Relation Mapping: 0.6846626984126984
Usage
This model can be used through the relbert library. Install the library via pip
pip install relbert
and activate model as below.
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-c-nce-classification-conceptnet-validated")
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/semeval2012_relational_similarity
- split: train
- data_eval: relbert/conceptnet_high_confidence
- split_eval: full
- template_mode: manual
- template: Today, I finally discovered the relation between and :
- loss_function: nce_logout
- classification_loss: True
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 1
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- exclude_relation_eval: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at fine-tuning parameter file.
Reference
If you use any resource from RelBERT, please consider to cite our paper.
@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",
}
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Dataset used to train research-backup/roberta-large-semeval2012-average-prompt-c-nce-classification-conceptnet-validated
Evaluation results
- Accuracy on Relation Mappingself-reported0.685
- Accuracy on SAT fullself-reported0.313
- Accuracy on SATself-reported0.309
- Accuracy on BATSself-reported0.462
- Accuracy on Googleself-reported0.630
- Accuracy on U2self-reported0.346
- Accuracy on U4self-reported0.361
- F1 on BLESSself-reported0.846
- F1 (macro) on BLESSself-reported0.821
- F1 on CogALexVself-reported0.846