Harikrishna-Srinivasan/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.

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.

Sources

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

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

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)