readme: ---
  language:
  - "multilingual"
  - "en"
  tags:
  - "sentiment-analysis"
  - "testing"
  - "unit tests"
  ---
  # DistilBert Dummy Sentiment Model
  
  ## Purpose
  
  This is a dummy model that can be used for testing the transformers `pipeline` with the task `sentiment-analysis`.  It should always give random results (i.e. `{"label": "negative", "score": 0.5}`).
  
  ## How to use
  
  ```python
  classifier = pipeline("sentiment-analysis", "dhpollack/distilbert-dummy-sentiment")
  results  = classifier(["this is a test", "another test"])
  ```
  
  ## Notes
  
  This was created as follows:
  
  1. Create a vocab.txt file (in /tmp/vocab.txt in this example).
  
  ```
  [UNK]
  [SEP]
  [PAD]
  [CLS]
  [MASK]
  ```
  
  2. Open a python shell:
  
  ```python
  import transformers
  config = transformers.DistilBertConfig(vocab_size=5, n_layers=1, n_heads=1, dim=1, hidden_dim=4 * 1, num_labels=2, id2label={0: "negative", 1: "positive"}, label2id={"negative": 0, "positive": 1})
  model = transformers.DistilBertForSequenceClassification(config)
  tokenizer = transformers.DistilBertTokenizer("/tmp/vocab.txt", model_max_length=512)
  config.save_pretrained(".")
  model.save_pretrained(".")
  tokenizer.save_pretrained(".")
  ```
  