research-backup / roberta-large-semeval2012-average-prompt-c-nce-classification-conceptnet-validated

relbert/roberta-large-semeval2012-average-prompt-c-nce-classification-conceptnet-validated

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): 0.31283422459893045
  • Accuracy on SAT: 0.3086053412462908
  • Accuracy on BATS: 0.46192329071706506
  • Accuracy on U2: 0.34649122807017546
  • Accuracy on U4: 0.3611111111111111
  • Accuracy on Google: 0.63
• Lexical Relation Classification ( dataset , full result ):
  • Micro F1 score on BLESS: 0.8457134247400934
  • Micro F1 score on CogALexV: 0.846244131455399
  • Micro F1 score on EVALution: 0.6262188515709642
  • Micro F1 score on K&H+N: 0.9545802323155039
  • Micro F1 score on ROOT09: 0.9044186775305547
• Relation Mapping ( dataset , full result ):
  • Accuracy on Relation Mapping: 0.6846626984126984

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-nce-classification-conceptnet-validated")
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: relbert/semeval2012_relational_similarity
• split: train
• data_eval: relbert/conceptnet_high_confidence
• split_eval: full
• template_mode: manual
• template: Today, I finally discovered the relation between and :
• loss_function: nce_logout
• classification_loss: True
• temperature_nce_constant: 0.05
• temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
• epoch: 1
• batch: 128
• lr: 5e-06
• lr_decay: False
• lr_warmup: 1
• weight_decay: 0
• random_seed: 0
• exclude_relation: None
• exclude_relation_eval: 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",
}