readme: This model is based on a custom Transformer model that can be installed with:
  
  ```bash
  pip install git+https://github.com/lucadiliello/bleurt-pytorch.git
  ```
  
  Now load the model and make predictions with:
  
  ```python
  import torch
  from bleurt_pytorch import BleurtConfig, BleurtForSequenceClassification, BleurtTokenizer
  
  config = BleurtConfig.from_pretrained('lucadiliello/bleurt-base-512')
  model = BleurtForSequenceClassification.from_pretrained('lucadiliello/bleurt-base-512')
  tokenizer = BleurtTokenizer.from_pretrained('lucadiliello/bleurt-base-512')
  
  references = ["a bird chirps by the window", "this is a random sentence"]
  candidates = ["a bird chirps by the window", "this looks like a random sentence"]
  
  model.eval()
  with torch.no_grad():
      inputs = tokenizer(references, candidates, padding='longest', return_tensors='pt')
      res = model(**inputs).logits.flatten().tolist()
  print(res)
  # [1.1482683420181274, 0.7443546056747437]
  ```
  
  Take a look at this [repository](https://github.com/lucadiliello/bleurt-pytorch) for the definition of `BleurtConfig`, `BleurtForSequenceClassification` and `BleurtTokenizer` in PyTorch.