anik-owl/roberta_norm_classifier

Normative Statement Classifier — RoBERTa Fine-tunes

A collection of fine-tuned RoBERTa models for detecting normative statements in text — sentences and documents that express social norms, obligations, prohibitions, or moral judgments (e.g. "people should remove their shoes before entering").

Github link for the full project: Git

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Models in this repository

Subfolder	Base	Description
roberta-base-classifier-v01	roberta-base	Baseline fine-tune on norm classification
roberta-base-tapt	roberta-base	Task-Adaptive Pre-Training (TAPT) checkpoint
roberta-large-classifier-v01	roberta-large	Larger model fine-tune for higher capacity
roberta-tapt-classifier-v01	roberta-base-tapt	Fine-tuned on top of the TAPT checkpoint

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Usage — roberta-base-classifier-v01

Load the model

from huggingface_hub import snapshot_download
from transformers import RobertaForSequenceClassification, RobertaTokenizer
import torch

# Download from HF Hub
snapshot_download(
    repo_id="anik-owl/roberta_norm_classifier",
    allow_patterns="roberta-base-classifier-v01/*",
    local_dir="./artifacts",
)

# Load model + tokenizer
model = RobertaForSequenceClassification.from_pretrained(
    "./artifacts/roberta-base-classifier-v01",
    num_labels=2,
)
tokenizer = RobertaTokenizer.from_pretrained("FacebookAI/roberta-base")

model.eval()

Inference

def predict(text: str, model, tokenizer, threshold: float = 0.5):
    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        padding=True,
        max_length=256,
    )

    with torch.no_grad():
        logits = model(**inputs).logits

    probs = torch.softmax(logits, dim=-1)
    prob_norm = probs[0][1].item()

    return {
        "label": "NORMATIVE" if prob_norm >= threshold else "NOT NORMATIVE",
        "score": round(prob_norm, 4),
    }


# Example
text = "People should always greet elders with respect."
result = predict(text, model, tokenizer)
print(result)
# {'label': 'NORMATIVE', 'score': 0.9341}

Labels

ID	Label
0	NOT NORMATIVE
1	NORMATIVE

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Intended use

These models are intended for research on computational social science, normative reasoning, and deontic language detection. They were developed as part of a thesis project on identifying normative statements in natural language.

Not intended for high-stakes automated decision-making without human review.

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Limitations

• Trained on a specific dataset of normative statements — may not generalise to all domains or languages
• Short, context-free sentences may be harder to classify accurately
• Models may reflect biases present in the training data

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Citation

If you use these models in your work, please cite this repository:

@misc{anik-owl-normclsf,
  author       = {anik-owl},
  title        = {Normative Statement Classifier — RoBERTa Fine-tunes},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/anik-owl/roberta_norm_classifier}},
}