readme: ---
  license: mit
  language:
  - en
  widget:
  - text: >-
      A nervous passenger is about to book a flight ticket, and he asks the
      airlines' ticket seller, 'I hope your planes are safe. Do they have a good
      track record for safety?' The airline agent replies, 'Sir, I can guarantee
      you, we've never had a plane that has crashed more than once.'
    example_title: A joke
  - text: >-
      Let me, however, hasten to assure that I am the same Gandhi as I was in
      1920. I have not changed in any fundamental respect. I attach the same
      importance to nonviolence that I did then. If at all, my emphasis on it has
      grown stronger. There is no real contradiction between the present
      resolution and my previous writings and utterances.
    example_title: Not a joke
  tags:
  - roberta
  ---
  
  ### What is this?
  This model has been developed to detect "narrative-style" jokes, stories and anecdotes (i.e. they are narrated as a story) spoken during speeches or conversations etc. It works best when jokes/anecdotes are at least 40 words or longer. It is based on Facebook's [RoBerta-MUPPET](https://huggingface.co/facebook/muppet-roberta-base). 
  
  The training dataset was a private collection of around 2000 jokes. This model has not been trained or tested on one-liners, puns or Reddit-style language-manipulation jokes such as knock-knock, Q&A jokes etc.
  
  See the example in the inference widget or How to use section for what constitues a narrative-style joke.
  
  ### Install these first
  You'll need to pip install transformers & maybe sentencepiece
  
  ### How to use
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch, time
  device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
  model_name = 'Reggie/muppet-roberta-base-joke_detector'
  max_seq_len = 510
  
  tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=max_seq_len)
  model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
  
  premise = """A nervous passenger is about to book a flight ticket, and he asks the airlines' ticket seller, "I hope your planes are safe. Do they have a good track record for safety?" The airline agent replies, "Sir, I can guarantee you, we've never had a plane that has crashed more than once." """
  hypothesis = ""
  
  input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
  output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
  prediction = torch.softmax(output["logits"][0], -1).tolist()
  is_joke = True if prediction[0] < prediction[1] else False
  
  print(is_joke)
  ```