relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce
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): 0.6577540106951871
- Accuracy on SAT: 0.658753709198813
- Accuracy on BATS: 0.8193440800444691
- Accuracy on U2: 0.6491228070175439
- Accuracy on U4: 0.6759259259259259
- Accuracy on Google: 0.946
- Lexical Relation Classification (dataset, full result):
- Micro F1 score on BLESS: 0.9196926322133494
- Micro F1 score on CogALexV: 0.8715962441314554
- Micro F1 score on EVALution: 0.699349945828819
- Micro F1 score on K&H+N: 0.9533977881338248
- Micro F1 score on ROOT09: 0.9113130680037606
- Relation Mapping (dataset, full result):
- Accuracy on Relation Mapping: 0.926031746031746
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-no-mask-prompt-c-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_no_mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between and :
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 24
- 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.
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",
}