relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child
RelBERT fine-tuned from roberta-base on
relbert/semeval2012_relational_similarity_v6.
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): 0.393048128342246
- Accuracy on SAT: 0.39465875370919884
- Accuracy on BATS: 0.5508615897720957
- Accuracy on U2: 0.4298245614035088
- Accuracy on U4: 0.42592592592592593
- Accuracy on Google: 0.73
- Lexical Relation Classification (dataset, full result):
- Micro F1 score on BLESS: 0.8234141931595599
- Micro F1 score on CogALexV: 0.7929577464788734
- Micro F1 score on EVALution: 0.6473456121343445
- Micro F1 score on K&H+N: 0.8730611393197468
- Micro F1 score on ROOT09: 0.8354747727984958
- Relation Mapping (dataset, full result):
- Accuracy on Relation Mapping: 0.7944444444444444
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/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child")
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: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- 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
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/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child
Evaluation results
- Accuracy on Relation Mappingself-reported0.794
- Accuracy on SAT fullself-reported0.393
- Accuracy on SATself-reported0.395
- Accuracy on BATSself-reported0.551
- Accuracy on Googleself-reported0.730
- Accuracy on U2self-reported0.430
- Accuracy on U4self-reported0.426
- F1 on BLESSself-reported0.823
- F1 (macro) on BLESSself-reported0.795
- F1 on CogALexVself-reported0.793