metadata

language:
  - en
license: unknown
size_categories:
  - 10K<n<100K
task_categories:
  - feature-extraction
  - text-classification
  - image-classification
  - image-feature-extraction
  - zero-shot-classification
  - zero-shot-image-classification
pretty_name: mmsd_v2
tags:
  - sarcasm
  - sarcasm-detection
  - mulitmodal-sarcasm-detection
  - sarcasm detection
  - multimodao sarcasm detection
  - tweets
dataset_info:
  config_name: mmsd-v1
  features:
    - name: image
      dtype: image
    - name: text
      dtype: string
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 1797521611.232
      num_examples: 19557
    - name: validation
      num_bytes: 259452303.817
      num_examples: 2387
    - name: test
      num_bytes: 261557636.749
      num_examples: 2373
  download_size: 2667548595
  dataset_size: 2318531551.798
configs:
  - config_name: mmsd-v1
    data_files:
      - split: train
        path: mmsd-v1/train-*
      - split: validation
        path: mmsd-v1/validation-*
      - split: test
        path: mmsd-v1/test-*

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System

This is a copy of the dataset uploaded on Hugging Face for easy access. The original data comes from this work.

Usage

# usage
from datasets import load_dataset
from transformers import CLIPImageProcessor, CLIPTokenizer
from torch.utils.data import DataLoader

image_processor = CLIPImageProcessor.from_pretrained(clip_path)
tokenizer = CLIPTokenizer.from_pretrained(clip_path)

def tokenization(example):
  text_inputs = tokenizer(example["text"], truncation=True, padding=True, return_tensors="pt")
  image_inputs = image_processor(example["image"], return_tensors="pt")
  return {'pixel_values': image_inputs['pixel_values'],
          'input_ids': text_inputs['input_ids'],
          'attention_mask': text_inputs['attention_mask'],
          "label": example["label"]}

dataset = load_dataset('quaeast/multimodal_sarcasm_detection')
dataset.set_transform(tokenization)

# get torch dataloader
train_dl = DataLoader(dataset['train'], batch_size=256, shuffle=True)
test_dl = DataLoader(dataset['test'], batch_size=256, shuffle=True)
val_dl = DataLoader(dataset['validation'], batch_size=256, shuffle=True)