research-backup / roberta-large-semeval2012-average-prompt-c-triplet

relbert/roberta-large-semeval2012-average-prompt-c-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): 0.5748663101604278
  • Accuracy on SAT: 0.5786350148367952
  • Accuracy on BATS: 0.820455808782657
  • Accuracy on U2: 0.6140350877192983
  • Accuracy on U4: 0.6319444444444444
  • Accuracy on Google: 0.918
• Lexical Relation Classification ( dataset , full result ):
  • Micro F1 score on BLESS: 0.924815428657526
  • Micro F1 score on CogALexV: 0.8718309859154929
  • Micro F1 score on EVALution: 0.704225352112676
  • Micro F1 score on K&H+N: 0.9667524518327885
  • Micro F1 score on ROOT09: 0.9015982450642431
• Relation Mapping ( dataset , full result ):
  • Accuracy on Relation Mapping: 0.8742857142857143

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-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: Today, I finally discovered the relation between and :
• 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",
}