|
--- |
|
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](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli). |
|
|
|
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](Reggie/muppet-roberta-base-joke_detector). |
|
|
|
### 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) |
|
``` |
