license: mit datasets: - custom metrics: - accuracy - f1 model-index: - name: XLM-RoBERTa Base Roman Urdu + Urdu Sentiment results: - task: type: text-classification name: Sentiment Analysis dataset: name: Custom Urdu + Roman Urdu Dataset type: text metrics: - name: Validation Loss type: loss value: 0.3735 - name: Training Loss type: loss value: 0.4441
🌍 XLM-RoBERTa Base — Urdu + Roman Urdu Sentiment Analysis
This model is fine-tuned from xlm-roberta-base for sentiment classification in Urdu and Roman Urdu.
It predicts Positive, Negative, and Neutral sentiments.
📊 Training Details
- Base Model: xlm-roberta-base
- Dataset: Custom Urdu + Roman Urdu dataset
- Classes: Positive, Negative, Neutral
- Epochs: 3
- Training Loss: 0.4441
- Validation Loss (best): 0.3735
🚀 Usage
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="tahamueed/xlm-roberta-roman-urdu-sentiment")
print(classifier("yeh movie bohot zabardast hai!"))
# [{'label': 'Positive', 'score': 0.92}]
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