readme: ---
  language: 
  - en
  datasets:
  - imdb
  metrics:
  - accuracy
  ---
  
  # bert-imdb-1hidden
  
  ## Model description
  
  A `bert-base-uncased` model was restricted to 1 hidden layer and
  fine-tuned for sequence classification on the 
  imdb dataset loaded using the `datasets` library.
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  pretrained = "lannelin/bert-imdb-1hidden"
  
  tokenizer = AutoTokenizer.from_pretrained(pretrained)
  
  model = AutoModelForSequenceClassification.from_pretrained(pretrained)
  
  LABELS = ["negative", "positive"]
  
  def get_sentiment(text: str):
      inputs = tokenizer.encode_plus(text, return_tensors='pt')
  
      output = model(**inputs)[0].squeeze()
  
      return LABELS[(output.argmax())]
  
  print(get_sentiment("What a terrible film!"))
  ```
  
  #### Limitations and bias
  
  No special consideration given to limitations and bias. 
  
  Any bias held by the imdb dataset may be reflected in the model's output.
  
  ## Training data
  
  Initialised with [bert-base-uncased](https://huggingface.co/bert-base-uncased)
  
  Fine tuned on [imdb](https://huggingface.co/datasets/imdb)
  
  
  ## Training procedure
  
   The model was fine-tuned for 1 epoch with a batch size of 64, 
   a learning rate of 5e-5, and a maximum sequence length of 512.
  
  ## Eval results
  
  Accuracy on imdb test set: 0.87132