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:
- deberta
- deberta-v3
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 Moritz Laurer's DeBERTa-v3.
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.
For a slightly less accurate model (0.4% less) that is 65% faster at inference, see the Roberta model.
Install these first
You'll need to pip install transformers & maybe sentencepiece
How to use
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)