readme: ## UIE(Universal Information Extraction)
  
  ### Introduction
  
  UIE(Universal Information Extraction) is an SOTA method in PaddleNLP, you can see details [here](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/uie).
  
  ### How to use
  
  We save the UIE model as a entire model(Ernie 3.0 backbone + start/end layers), so you need to load model as:
  
  #### 1. clone this model to your local file
  
  ```sh
  git lfs install
  git clone https://huggingface.co/Pky/uie-base
  ```
  
  If you don't have [`git-lfs`], you can also:
  
    * Download manually by click [`Files and versions`] at Top Of This Card.
    * Use code to download files, here is my [code example](https://github.com/HarderThenHarder/transformers_tasks/blob/063b9f4bd7c9efc1e851a2506a2dc9d082faec45/UIE/train.py#L99).
  
  #### 2. load this model from local
  
  ```python
  import os
  import torch
  from transformers import AutoTokenizer
  
  uie_model = 'uie-base-zh'
  model = torch.load(os.path.join(uie_model, 'pytorch_model.bin'))        # load UIE model
  tokenizer = AutoTokenizer.from_pretrained('uie-base')                   # load tokenizer
  ...
  
  start_prob, end_prob = model(input_ids=batch['input_ids'],
                              token_type_ids=batch['token_type_ids'],
                              attention_mask=batch['attention_mask']))
  print(f'start_prob ({type(start_prob)}): {start_prob.size()}')          # start_prob
  print(f'end_prob ({type(end_prob)}): {end_prob.size()}')                # end_prob
  ...
  ```
  
  Here is the output of model (with batch_size=16, max_seq_len=256):
  ```python
  start_prob (<class 'torch.Tensor'>): torch.Size([16, 256])
  end_prob (<class 'torch.Tensor'>): torch.Size([16, 256])
  ```