research-backup / relbert-roberta-base-nce-a-conceptnet

relbert/relbert-roberta-base-nce-a-conceptnet

RelBERT based on roberta-base fine-tuned on relbert/conceptnet_relational_similarity (see the relbert for more detail of fine-tuning). This model achieves the following results on the relation understanding tasks:

• Analogy Question ( dataset , full result ):
  • Accuracy on SAT (full): 0.4144385026737968
  • Accuracy on SAT: 0.41543026706231456
  • Accuracy on BATS: 0.5453029460811561
  • Accuracy on U2: 0.41228070175438597
  • Accuracy on U4: 0.4027777777777778
  • Accuracy on Google: 0.702
  • Accuracy on ConceptNet Analogy: 0.1686241610738255
  • Accuracy on T-Rex Analogy: 0.4207650273224044
  • Accuracy on NELL-ONE Analogy: 0.5383333333333333
• Lexical Relation Classification ( dataset , full result ):
  • Micro F1 score on BLESS: 0.9029682085279493
  • Micro F1 score on CogALexV: 0.8265258215962441
  • Micro F1 score on EVALution: 0.605092091007584
  • Micro F1 score on K&H+N: 0.9595186756625165
  • Micro F1 score on ROOT09: 0.8868693199623943
• Relation Mapping ( dataset , full result ):
  • Accuracy on Relation Mapping: 0.8482936507936508

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-nce-a-conceptnet")
vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (n_dim, )

Training hyperparameters

• model: roberta-base
• max_length: 64
• epoch: 5
• batch: 16
• random_seed: 0
• lr: 5e-06
• lr_warmup: 10
• aggregation_mode: average_no_mask
• data: relbert/conceptnet_relational_similarity
• data_name: None
• exclude_relation: None
• split: train
• split_valid: validation
• loss_function: nce
• classification_loss: False
• loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
• augment_negative_by_positive: True

See the full configuration at config file .

Reference

If you use any resource from RelBERT, please consider to cite our paper .

@inproceedings{ushio-etal-2021-distilling,
    title = "Distilling Relation Embeddings from Pretrained Language Models",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.712",
    doi = "10.18653/v1/2021.emnlp-main.712",
    pages = "9044--9062",
    abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}