readme: ## BLEURT
  
  Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by 
  Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
  
  The code for model conversion was originated from [this notebook](https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing) mentioned [here](https://github.com/huggingface/datasets/issues/224).
  
  ## Usage Example
  
  ```python
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  import torch
  
  tokenizer = AutoTokenizer.from_pretrained("Elron/bleurt-large-512")
  model = AutoModelForSequenceClassification.from_pretrained("Elron/bleurt-large-512")
  model.eval()
  
  references = ["hello world", "hello world"]
  candidates = ["hi universe", "bye world"]
  
  with torch.no_grad():
    scores = model(**tokenizer(references, candidates, return_tensors='pt'))[0].squeeze()
  print(scores) # tensor([0.9877, 0.0475])
  ```
  