emotion_xlmr3

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emotion_xlmr3

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: 0.0471
• Accuracy: 0.9875
• F1: 0.9850

Model description

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

Version: 1.0 Author: Amaan Shaikh (amaan00z) Languages: Hinglish (Hindi–English code-mixed) Labels: anger (0), fear (1), disgust (2), sadness (3), disapproval (4), surprise (5), admiration (6), joy (7), love (8), neutral (9)

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✨ Model Summary

This model is a Transformer-based Hinglish Emotion Classifier, fine-tuned on a high-quality 25,689-sample dataset designed specifically for real-world Indian social-media text.

It is built using XLM-RoBERTa Base, which performs strongly on multilingual and code-mixed text.

🎯 Labels

The model predicts 10 emotions:

ID Label

0 anger 1 fear 2 disgust 3 sadness 4 disapproval 5 surprise 6 admiration 7 joy 8 love 9 neutral

Training and evaluation data

This model was trained on a curated, cleaned, balanced Hinglish emotion dataset, including:

✅ 24,500 manually cleaned Hinglish emotion samples ✅ Additional 3,000 refined sarcastic emotion-enhanced Hinglish samples ✅ Removal of duplicates & noisy samples ✅ Emoji-heavy and Gen-Z slang patterns included

Dataset Source: (Add your Kaggle link)

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🎯 Labels

The model predicts 10 emotions:

ID Label

0 anger 1 fear 2 disgust 3 sadness 4 disapproval 5 surprise 6 admiration 7 joy 8 love 9 neutral

citation

Shaikh, Amaan. (2025). Hinglish Emotion Detection Model (XLM-R). https://huggingface.co/amaan00z/emotion-hinglish-xlmr

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
• mixed_precision_training: Native AMP

Training results

Framework versions

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