roberta-large-semeval2012-average-prompt-e-triplet

relbert/roberta-large-semeval2012-average-prompt-e-triplet

RelBERT fine-tuned from roberta-large on
semeval2012. 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): 0.550802139037433
  • Accuracy on SAT: 0.5548961424332344
  • Accuracy on BATS: 0.754863813229572
  • Accuracy on U2: 0.631578947368421
  • Accuracy on U4: 0.5740740740740741
  • Accuracy on Google: 0.872
• Lexical Relation Classification (dataset, full result):
  • Micro F1 score on BLESS: 0.9031188790116016
  • Micro F1 score on CogALexV: 0.8692488262910798
  • Micro F1 score on EVALution: 0.6933911159263272
  • Micro F1 score on K&H+N: 0.9611184530847882
  • Micro F1 score on ROOT09: 0.9088060169225948
• Relation Mapping (dataset, full result):
  • Accuracy on Relation Mapping: 0.9255952380952381

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-e-triplet")
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: semeval2012
• n_sample: 10
• custom_template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
• template: None
• softmax_loss: True
• in_batch_negative: True
• parent_contrast: True
• mse_margin: 1
• epoch: 1
• lr_warmup: 10
• batch: 64
• lr: 2e-05
• lr_decay: False
• weight_decay: 0
• optimizer: adam
• momentum: 0.9
• fp16: False
• random_seed: 0

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",
}