readme: This model provides a MobileBERT [(Sun et al., 2020)](https://arxiv.org/abs/2004.02984) fine-tuned on the SST data with three sentiments (0 -- negative, 1 -- neutral, and 2 -- positive). 
  
  ## Example Usage
  
  Below, we provide illustrations on how to use this model to make sentiment predictions. 
  
  
  ```python
  import torch
  from transformers import AutoTokenizer, AutoConfig, MobileBertForSequenceClassification
  # load model
  model_name = r'cambridgeltl/sst_mobilebert-uncased'
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  config = AutoConfig.from_pretrained(model_name)
  model = MobileBertForSequenceClassification.from_pretrained(model_name, config=config)
  model.eval()
  '''
      labels: 
          0 -- negative
          1 -- neutral
          2 -- positive
  '''
  
  # prepare exemplar sentences
  batch_sentences = [
      "in his first stab at the form , jacquot takes a slightly anarchic approach that works only sporadically .",
      "a valueless kiddie paean to pro basketball underwritten by the nba .",
      "a very well-made , funny and entertaining picture .",
  ]
  
  # prepare input
  inputs = tokenizer(batch_sentences, max_length=256, truncation=True, padding=True, return_tensors='pt')
  input_ids, attention_mask = inputs.input_ids, inputs.attention_mask
  
  # make predictions
  outputs = model(input_ids=input_ids, attention_mask=attention_mask)
  predictions = torch.argmax(outputs.logits, dim = -1)
  print (predictions)
  # tensor([1, 0, 2])
  ``` 
  
  ## Citation:
  If you find this model useful, please kindly cite our model as
  
  ```bibtex
  @misc{susstmobilebert,
    author = {Su, Yixuan},
    title = {A MobileBERT Fine-tuned on SST},
    howpublished = {\url{https://huggingface.co/cambridgeltl/sst_mobilebert-uncased}},
    year = 2022
  }
  ```