Model Card for Model ID
Multilingual Hate Speech Detection Model
This is a Multilingual Hate Speech Detection model built by fine-tuning XLM-RoBERTa-Base on a Kaggle multilingual hate speech dataset. The model is trained for binary text classification, distinguishing between Hate Speech and Non-Hate content across different languages.
Model Details
- Base Model: xlm-roberta-base
- Task: Text Classification (Binary)
- Languages Supported: Multilingual (100+ languages as supported by XLM-R)
- Framework: PyTorch, Transformers
Model Description
Labels
LABEL_0
: Non-HateLABEL_1
: Hate
Example Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="samyak159/xlmroberta-hate-speech-detection")
result = classifier("This is an example input sentence.")
print(result)
Intended Use
This model is intended for research and educational purposes to explore hate speech detection across languages. For production use, further fine-tuning and validation on specific languages and contexts are recommended.
Limitations
The model may show biases present in the dataset.
Performance may vary across languages not well represented in training data.
Not intended for deployment in sensitive or legal decision-making systems without further verification.
Training Data
The dataset used is sourced from Kaggle's Multilingual Hate Speech Dataset, containing text samples labeled as Hate or Non-Hate across various languages.
Citation
If you use this model or work, please cite:
Samyak Darshan Nayak. Multilingual Hate Speech Detection using Transformer-Based Models. 2025.
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [Samyak Darshan Nayak]
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
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Training Procedure
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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