readme: ---
  language:
  - es
  - fr
  - ru
  - en
  - it
  tags:
  - token-classification
  - fill-mask
  license: mit
  datasets:
  - iit-cdip
  ---
  
  
  This model is the pretrained infoxlm checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding".
  
   Original repository: https://github.com/jpWang/LiLT
   
  To use it, it is necessary to fork the modeling and configuration files from the original repository, and load the pretrained model from the corresponding classes (LiLTRobertaLikeConfig, LiLTRobertaLikeForRelationExtraction, LiLTRobertaLikeForTokenClassification, LiLTRobertaLikeModel).
  They can also be preloaded with the AutoConfig/model factories as such:
  
  ```python
  from transformers import AutoModelForTokenClassification, AutoConfig
  
  from path_to_custom_classes import (
      LiLTRobertaLikeConfig,
      LiLTRobertaLikeForRelationExtraction,
      LiLTRobertaLikeForTokenClassification,
      LiLTRobertaLikeModel
      )
  
  
  def patch_transformers():
      AutoConfig.register("liltrobertalike", LiLTRobertaLikeConfig)
      AutoModel.register(LiLTRobertaLikeConfig, LiLTRobertaLikeModel)
      AutoModelForTokenClassification.register(LiLTRobertaLikeConfig, LiLTRobertaLikeForTokenClassification)
      # etc...
   ```
   
   To load the model, it is then possible to use:
   ```python
   # patch_transformers() must have been executed beforehand
  
  tokenizer = AutoTokenizer.from_pretrained("microsoft/infoxlm-base")
  model = AutoModel.from_pretrained("manu/lilt-infoxlm-base")
  model = AutoModelForTokenClassification.from_pretrained("manu/lilt-infoxlm-base") # to be fine-tuned on a token classification task
   ```