Datasets:

coderchen01
/

MMSD2.0

	---
	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: multimodal-sarcasm-dataset
	tags:
	- sarcasm
	- sarcasm-detection
	- mulitmodal-sarcasm-detection
	- sarcasm detection
	- multimodao sarcasm detection
	- tweets
	dataset_info:
	- config_name: mmsd-clean
	features:
	- name: image
	dtype: image
	- name: text
	dtype: string
	- name: label
	dtype: int64
	- name: id
	dtype: string
	splits:
	- name: train
	num_bytes: 1797951865.232
	num_examples: 19557
	- name: validation
	num_bytes: 259504817.817
	num_examples: 2387
	- name: test
	num_bytes: 261609842.749
	num_examples: 2373
	download_size: 2668004199
	dataset_size: 2319066525.798
	- config_name: mmsd-original
	features:
	- name: image
	dtype: image
	- name: text
	dtype: string
	- name: label
	dtype: int64
	- name: id
	dtype: string
	splits:
	- name: train
	num_bytes: 1816845826.384
	num_examples: 19816
	- name: validation
	num_bytes: 260077790.0
	num_examples: 2410
	- name: test
	num_bytes: 262679920.717
	num_examples: 2409
	download_size: 2690517598
	dataset_size: 2339603537.101
	- config_name: mmsd-v1
	features:
	- name: image
	dtype: image
	- name: text
	dtype: string
	- name: label
	dtype: int64
	- name: id
	dtype: string
	splits:
	- name: train
	num_bytes: 1816845826.384
	num_examples: 19816
	- name: validation
	num_bytes: 260077790.0
	num_examples: 2410
	- name: test
	num_bytes: 262679920.717
	num_examples: 2409
	download_size: 2690517598
	dataset_size: 2339603537.101
	- config_name: mmsd-v2
	features:
	- name: image
	dtype: image
	- name: text
	dtype: string
	- name: label
	dtype: int64
	- name: id
	dtype: string
	splits:
	- name: train
	num_bytes: 1816541209.384
	num_examples: 19816
	- name: validation
	num_bytes: 260043003.0
	num_examples: 2410
	- name: test
	num_bytes: 262641462.717
	num_examples: 2409
	download_size: 2690267623
	dataset_size: 2339225675.101
	configs:
	- config_name: mmsd-clean
	data_files:
	- split: train
	path: mmsd-clean/train-*
	- split: validation
	path: mmsd-clean/validation-*
	- split: test
	path: mmsd-clean/test-*
	- config_name: mmsd-original
	data_files:
	- split: train
	path: mmsd-original/train-*
	- split: validation
	path: mmsd-original/validation-*
	- split: test
	path: mmsd-original/test-*
	- config_name: mmsd-v1
	data_files:
	- split: train
	path: mmsd-v1/train-*
	- split: validation
	path: mmsd-v1/validation-*
	- split: test
	path: mmsd-v1/test-*
	- config_name: mmsd-v2
	data_files:
	- split: train
	path: mmsd-v2/train-*
	- split: validation
	path: mmsd-v2/validation-*
	- split: test
	path: mmsd-v2/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](https://aclanthology.org/2023.findings-acl.689/), which is an improvement upon a [previous study](https://aclanthology.org/P19-1239).

	## Usage

	```python
	from typing import TypedDict, cast

	import pytorch_lightning as pl
	from datasets import Dataset, load_dataset
	from torch import Tensor
	from torch.utils.data import DataLoader
	from transformers import CLIPProcessor


	class MMSDModelInput(TypedDict):
	pixel_values: Tensor
	input_ids: Tensor
	attention_mask: Tensor
	label: Tensor
	id: list[str]


	class MMSDDatasetModule(pl.LightningDataModule):

	def __init__(
	self,
	clip_ckpt_name: str = "openai/clip-vit-base-patch32",
	dataset_version: str = "mmsd-v2",
	max_length: int = 77,
	train_batch_size: int = 32,
	val_batch_size: int = 32,
	test_batch_size: int = 32,
	num_workers: int = 19,
	) -> None:
	super().__init__()
	self.clip_ckpt_name = clip_ckpt_name
	self.dataset_version = dataset_version
	self.train_batch_size = train_batch_size
	self.val_batch_size = val_batch_size
	self.test_batch_size = test_batch_size
	self.num_workers = num_workers
	self.max_length = max_length

	def setup(self, stage: str) -> None:
	processor = CLIPProcessor.from_pretrained(self.clip_ckpt_name)

	def preprocess(example):
	inputs = processor(
	text=example["text"],
	images=example["image"],
	return_tensors="pt",
	padding="max_length",
	truncation=True,
	max_length=self.max_length,
	)

	return {
	"pixel_values": inputs["pixel_values"],
	"input_ids": inputs["input_ids"],
	"attention_mask": inputs["attention_mask"],
	"label": example["label"],
	}

	self.raw_dataset = cast(
	Dataset,
	load_dataset("coderchen01/MMSD2.0", name=self.dataset_version),
	)
	self.dataset = self.raw_dataset.map(
	preprocess,
	batched=True,
	remove_columns=["text", "image"],
	)

	def train_dataloader(self) -> DataLoader:
	return DataLoader(
	self.dataset["train"],
	batch_size=self.train_batch_size,
	shuffle=True,
	num_workers=self.num_workers,
	)

	def val_dataloader(self) -> DataLoader:
	return DataLoader(
	self.dataset["validation"],
	batch_size=self.val_batch_size,
	num_workers=self.num_workers,
	)

	def test_dataloader(self) -> DataLoader:
	return DataLoader(
	self.dataset["test"],
	batch_size=self.test_batch_size,
	num_workers=self.num_workers,
	)

	```

	## References

	[1] Yitao Cai, Huiyu Cai, and Xiaojun Wan. 2019. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2506–2515, Florence, Italy. Association for Computational Linguistics.

	[2] Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, and Ruifeng Xu. 2023. MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10834–10845, Toronto, Canada. Association for Computational Linguistics.