Yash22CSU192/davidson-roberta-hatespeech

Davidson RoBERTa Hate Speech Classifier

• Model: roberta-large fine-tuned for 3-way classification (toxic, neutral, non-toxic).
• Dataset: tdavidson/hate_speech_offensive (Twitter), split into train/val/test locally.
• Metrics (test): paste from metrics.json.
• Intended use: content moderation research/demos; not for deployment without bias/fairness review.
• Limitations/risks: social bias, dataset age/domain mismatch; errors possible on slang/irony.
• How to use:

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

mid = "Yash22CSU192/davidson-roberta-hatespeech"

Load tokenizer and model

tok = AutoTokenizer.from_pretrained(mid) mdl = AutoModelForSequenceClassification.from_pretrained(mid)

Create a text-classification pipeline

clf = pipeline("text-classification", model=mdl, tokenizer=tok, return_all_scores=True)

Test the classifier

print(clf("Have a nice day."))

Files

• model.safetensors, config.json, tokenizer.json, tokenizer_config.json, vocab.json, merges.txt, special_tokens_map.json
• training_args.bin (Trainer settings), metrics.json (evaluation summary)