relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2-child

relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-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): 0.3770053475935829
  • Accuracy on SAT: 0.37388724035608306
  • Accuracy on BATS: 0.4802668148971651
  • Accuracy on U2: 0.33771929824561403
  • Accuracy on U4: 0.34953703703703703
  • Accuracy on Google: 0.558
• Lexical Relation Classification (dataset, full result):
  • Micro F1 score on BLESS: 0.8898598764502034
  • Micro F1 score on CogALexV: 0.7859154929577464
  • Micro F1 score on EVALution: 0.566088840736728
  • Micro F1 score on K&H+N: 0.9492940112679975
  • Micro F1 score on ROOT09: 0.8480100282043247
• Relation Mapping (dataset, full result):
  • Accuracy on Relation Mapping: 0.6670436507936508

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-e-nce-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: nce_logout
• 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",
}