Hate-Speech-RoBERTa

Copyright 2026 Harikrishna Srinivasan

RoBERTa-Large for Hate Speech Classifier (LoRA)

Summary

This model is a LoRA fine-tuned RoBERTa-Large uncased model for binary hate speech classification (Hate / Not Hate). It is optimized for efficient fine-tuning using Low-Rank Adaptation (LoRA) via the Hugging Face PEFT library.

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Details

Description

• Developed by: Harikrishna Srinivasan
• Model type: Fine-Tuned (LoRA) RoBERTa-Large uncased
• Task: Binary text classification
• Language(s): English
• License: Apache 2.0
• Finetuned from: FacebookAI/roberta-large

This model uses Low-Rank Adaptation (LoRA) to fine-tune only a subset of parameters, enabling efficient training while preserving the strong contextual representation capabilities of RoBERTa-Large.

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Sources

• Base Model: https://huggingface.co/FacebookAI/roberta-large
• Training Framework: Hugging Face Transformers + PEFT
• Repository: Not publicly linked (local / academic project)

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Uses

Direct Use

This model can be used directly for:

• Hate speech detection in English text
• Moderation pipelines
• Dataset auditing
• Research on implicit hate and biased language
• Pre-filtering content for human moderation

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Dataset Citation

@misc{srinivasan2026hatespeech,
  author       = {Harikrishna Srinivasan},
  title        = {Hate-Speech Dataset (Refined and Cleaned Version)},
  year         = {2026},
  publisher    = {Hugging Face Datasets},
  howpublished = {https://huggingface.co/datasets/Harikrishna-Srinivasan/Hate-Speech}
}

Example:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

MODEL_NAME = "Harikrishna-Srinivasan/Hate-Speech-RoBERTa"

model = PeftModel.from_pretrained(MODEL_NAME)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)