Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+- text-classification
+- roberta
+- normativity
+- deontic-logic
+- social-norms
+base_model:
+- FacebookAI/roberta-base
+- FacebookAI/roberta-large
+datasets:
+- SALT-NLP/CultureBank
+---
+
+# 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](https://github.com/AnikMallick/norm-classifier)
+
+---
+
+## 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` | TAPT checkpoint | Fine-tuned on top of the TAPT checkpoint |
+
+---
+
+## Usage — `roberta-base-classifier-v01`
+
+### Load the model
+
+```python
+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
+
+```python
+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 |
+
+---
+
+## 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.
+
+---
+
+## 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
+
+---
+
+## Citation
+
+If you use these models in your work, please cite this repository:
+
+```bibtex
+@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}},
+}
+```