
## Table of Contents
- [Dataset Description](#dataset-description)

- 
# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.

# S3D-v2 Summary
The S3D-v2 dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by a majority voting system of fine-tuned sarcasm detection models. The models used are 
our [roberta-large-finetuned-SARC-combined-DS](https://huggingface.co/surrey-nlp/roberta-large-finetuned-SARC-combined-DS), [bertweet-base-finetuned-SARC-DS](https://huggingface.co/surrey-nlp/bertweet-base-finetuned-SARC-DS)
and [bertweet-base-finetuned-SARC-combined-DS](https://huggingface.co/surrey-nlp/bertweet-base-finetuned-SARC-combined-DS) models.

S3D contains 13016 tweets labelled as sarcastic, and 86904 tweets labelled as not being sarcastic.

# Data Fields
- Text: The preprocessed tweet
- Label: A label to denote if a given tweet is sarcastic

# Data Splits
- Train: 70,000
- Valid: 15,000
- Test: 15,000
task_categories:text-classification annotations_creators:Jordan Painter, Diptesh Kanojia multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:en license:cc-by-sa-4.0 region:us
{"annotations_creators": ["Jordan Painter, Diptesh Kanojia"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "Utilising Weak Supervision to create S3D: A Sarcasm Annotated Dataset"}
TAGS
#task_categories-text-classification #annotations_creators-Jordan Painter, Diptesh Kanojia #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 


## Table of Contents
- Dataset Description

- 
# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.

# S3D-v2 Summary
The S3D-v2 dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by a majority voting system of fine-tuned sarcasm detection models. The models used are 
our roberta-large-finetuned-SARC-combined-DS, bertweet-base-finetuned-SARC-DS
and bertweet-base-finetuned-SARC-combined-DS models.

S3D contains 13016 tweets labelled as sarcastic, and 86904 tweets labelled as not being sarcastic.

# Data Fields
- Text: The preprocessed tweet
- Label: A label to denote if a given tweet is sarcastic

# Data Splits
- Train: 70,000
- Valid: 15,000
- Test: 15,000
## Table of Contents
- Dataset Description

- # Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models. # S3D-v2 Summary
The S3D-v2 dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by a majority voting system of fine-tuned sarcasm detection models. The models used are 
our roberta-large-finetuned-SARC-combined-DS, bertweet-base-finetuned-SARC-DS
and bertweet-base-finetuned-SARC-combined-DS models.

S3D contains 13016 tweets labelled as sarcastic, and 86904 tweets labelled as not being sarcastic. # Data Fields
- Text: The preprocessed tweet
- Label: A label to denote if a given tweet is sarcastic # Data Splits
- Train: 70,000
- Valid: 15,000
- Test: 15,000
TAGS
#task_categories-text-classification #annotations_creators-Jordan Painter, Diptesh Kanojia #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 
 ## Table of Contents
- Dataset Description

- # Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models. # S3D-v2 Summary
The S3D-v2 dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by a majority voting system of fine-tuned sarcasm detection models. The models used are 
our roberta-large-finetuned-SARC-combined-DS, bertweet-base-finetuned-SARC-DS
and bertweet-base-finetuned-SARC-combined-DS models.

S3D contains 13016 tweets labelled as sarcastic, and 86904 tweets labelled as not being sarcastic. # Data Fields
- Text: The preprocessed tweet
- Label: A label to denote if a given tweet is sarcastic # Data Splits
- Train: 70,000
- Valid: 15,000
- Test: 15,000
passage: TAGS
#task_categories-text-classification #annotations_creators-Jordan Painter, Diptesh Kanojia #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us 
## Table of Contents
- Dataset Description

-# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.# S3D-v2 Summary
The S3D-v2 dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by a majority voting system of fine-tuned sarcasm detection models. The models used are 
our roberta-large-finetuned-SARC-combined-DS, bertweet-base-finetuned-SARC-DS
and bertweet-base-finetuned-SARC-combined-DS models.

S3D contains 13016 tweets labelled as sarcastic, and 86904 tweets labelled as not being sarcastic.# Data Fields
- Text: The preprocessed tweet
- Label: A label to denote if a given tweet is sarcastic# Data Splits
- Train: 70,000
- Valid: 15,000
- Test: 15,000