sarcasm_xlmr

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

sarcasm_xlmr

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1423
• Accuracy: 0.9498
• F1: 0.9546

Model description

Hinglish Sarcasm Detection Model (XLM-RoBERTa Base — Fine-Tuned)

Version: 1.0 Author: Amaan Shaikh (amaan00z) Language: Hinglish Labels: 0 = not_sarcastic 1 = sarcastic

---

✨ Model Summary

This is a Hinglish Sarcasm Classifier trained on a rich combination of:

✅ 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

Framework versions

• Transformers 4.57.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1