First README file.

README.md

@@ -1,3 +1,92 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- microsoft/deberta-v3-base
+pipeline_tag: zero-shot-classification
+tags:
+- smart
+- city
+- classifier
+- genai
+---
+# GenAI Smart City Classifier (DeBERTa v3 Base Fine-Tune)
+
+Binary transformer classifier detecting whether a text describes a Generative AI (GenAI) application in a smart city context.
+
+## Labels
+- 0: GenAI used for smart city application
+- 1: Not related
+
+`id2label = {0: "GenAI used for smart city application", 1: "Not related"}`
+
+## Model Card Summary
+- Base: microsoft/deberta-v3-base
+- Tokenizer: DebertaV2Tokenizer (same as base)
+- Max length used in training batches: 512 (inference examples use 256)
+- Loss: Custom focal loss (γ=2) + label smoothing (0.1)
+- Scheduler: Cosine, warmup 10%
+- Epochs: 8, batch size 8 (train) / 16 (eval)
+- Calibration: Temperature scaling (optimal ≈ 0.602)
+
+## Quick Start
+```python
+import torch
+from transformers import DebertaV2Tokenizer, AutoModelForSequenceClassification
+
+MODEL_ID = "joaocarlosnb/genai-smartcity-classifier"  # replace with actual repo id
+TEMP = 0.602
+id2label = {0: "GenAI used for smart city application", 1: "Not related"}
+
+tokenizer = DebertaV2Tokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+def predict(text, max_length=256, apply_temp=True):
+    inputs = tokenizer(text, truncation=True, padding="max_length",
+                       max_length=max_length, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        if apply_temp:
+            logits = logits / TEMP
+        probs = torch.softmax(logits, dim=-1)[0]
+    top = int(probs.argmax())
+    return {
+        "label": id2label[top],
+        "probabilities": {id2label[i]: float(p) for i, p in enumerate(probs)}
+    }
+
+print(predict("We apply a diffusion model to simulate traffic for urban planning."))
+```
+
+## Installation
+```bash
+pip install transformers torch
+```
+
+## Input Guidance
+Short technical sentences or abstract fragments (English). Truncate >512 tokens automatically.
+
+## Limitations
+- Binary only (no “mentioned, not used” middle class)
+- English academic / technical domain bias
+- Not evaluated for adversarial or multilingual robustness
+
+## Intended Use
+Research, corpus analysis, and exploratory filtering. Human review is recommended before operational deployment.
+
+## Dataset
+Training data hosted separately (same namespace). Contains augmented, adaptive, contrastive, and diagnostic subsets.
+
+## Reproducibility Notes
+Set `seed=42`. Use DebertaV2Tokenizer with max_length=512 for full retraining.
+
+## Citation (Placeholder)
+> Bittencourt, J. C. N., Flores, T. K. S., Jesus, T. C., & Costa, D. G. (2025). On the Role of AI in Building Generative Urban Intelligence. In Review. https://doi.org/10.21203/rs.3.rs-7131966/v1
+> 
+## License
+See repository LICENSE (ensure compatibility with upstream model license).
+
+## Security
+Do not hard-code Hugging Face tokens. Use environment variable: `export