Hugging Face's logo

studio-ousia
/
mluke-base-lite

Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Inference Endpoints
Model card Files Community
mluke-base-lite / README.md
ryo0634's picture
ryo0634
Update README.md
3775c9b
preview code
|
raw
history blame contribute delete
2.49 kB
metadata
language:
  - multilingual
  - ar
  - bn
  - de
  - el
  - en
  - es
  - fi
  - fr
  - hi
  - id
  - it
  - ja
  - ko
  - nl
  - pl
  - pt
  - ru
  - sv
  - sw
  - te
  - th
  - tr
  - vi
  - zh
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
  - luke
  - named entity recognition
  - relation classification
  - question answering
license: apache-2.0

mLUKE

mLUKE (multilingual LUKE) is a multilingual extension of LUKE.

Please check the official repository for more details and updates.

This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number of parameters in this model is 279M. The model was initialized with the weights of XLM-RoBERTa(base) and trained using December 2020 version of Wikipedia in 24 languages.

This model is a lite-weight version of studio-ousia/mluke-base, without Wikipedia entity embeddings but only with special entities such as [MASK].

Note

When you load the model from AutoModel.from_pretrained with the default configuration, you will see the following warning:

Some weights of the model checkpoint at studio-ousia/mluke-base-lite were not used when initializing LukeModel: [
'luke.encoder.layer.0.attention.self.w2e_query.weight', 'luke.encoder.layer.0.attention.self.w2e_query.bias', 
'luke.encoder.layer.0.attention.self.e2w_query.weight', 'luke.encoder.layer.0.attention.self.e2w_query.bias', 
'luke.encoder.layer.0.attention.self.e2e_query.weight', 'luke.encoder.layer.0.attention.self.e2e_query.bias', 
...]

These weights are the weights for entity-aware attention (as described in the LUKE paper). This is expected because use_entity_aware_attention is set to false by default, but the pretrained weights contain the weights for it in case you enable use_entity_aware_attention and have the weights loaded into the model.

Citation

If you find mLUKE useful for your work, please cite the following paper:

@inproceedings{ri-etal-2022-mluke,
    title = "m{LUKE}: {T}he Power of Entity Representations in Multilingual Pretrained Language Models",
    author = "Ri, Ryokan  and
      Yamada, Ikuya  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    year = "2022",
    url = "https://aclanthology.org/2022.acl-long.505",