amaan00z/sarcasm_xlmr

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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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: 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