✅ MUStARD Hinglish Dialogues ✅ Swami et al. Hinglish Twitter dataset ✅ 4,000 generated Gen-Z + political + meme sarcasm samples ✅ 2,500 real non-sarcastic Hinglish social-media samples ✅ Reddit & general Hinglish sarcasm templates ✅ Cleaned + deduplicated final dataset: 9,594 samples

Model backbone: XLM-RoBERTa Base

📘 Labels

ID Label

0 not_sarcastic 1 sarcastic

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2244	1.0	505	0.1979	0.9220	0.9291
0.2034	2.0	1010	0.1418	0.9387	0.9448
0.1759	3.0	1515	0.1423	0.9498	0.9546
0.1009	4.0	2020	0.2033	0.9465	0.9509

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

amaan00z/sarcasm_xlmr

sarcasm_xlmr

Model description