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

relbert/roberta-large-semeval2012-average-prompt-d-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): 0.6737967914438503
  • Accuracy on SAT: 0.6795252225519288
  • Accuracy on BATS: 0.7859922178988327
  • Accuracy on U2: 0.6535087719298246
  • Accuracy on U4: 0.6550925925925926
  • Accuracy on Google: 0.938
• Lexical Relation Classification ( dataset , full result ):
  • Micro F1 score on BLESS: 0.9252674401084827
  • Micro F1 score on CogALexV: 0.8692488262910798
  • Micro F1 score on EVALution: 0.7009750812567714
  • Micro F1 score on K&H+N: 0.9683522292550601
  • Micro F1 score on ROOT09: 0.9009714822939517
• Relation Mapping ( dataset , full result ):
  • Accuracy on Relation Mapping: 0.8641666666666666

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-d-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 is the
• 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",
}