salitahir/roberta-esg-relevance-green-guard-v1

🟢 Green-Guard — RoBERTa ESG Relevance Classifier (v1)

Task: Sentence-level classification — determine if a sentence is Sustainability-Related (Yes / No).
Base model: roberta-base, fine-tuned on a labeled ESG corpus from the Green-Guard dataset.
Repository: GitHub → Green-Guard Project

---

📊 Metrics (Test Set)

Metric	Value
Accuracy	0.90
Macro F1	0.89
Weighted F1	0.90

Metrics computed on a held-out test split (data/processed/splits/)
using the JSON logs → reports/relevance_metrics_v1.json

---

🧩 Labels

{ "0": "No", "1": "Yes" }

🚀 Quick Inference

You can load and run the model directly:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "salitahir/roberta-esg-relevance-green-guard-v1"
tok = AutoTokenizer.from_pretrained(model_id)
mod = AutoModelForSequenceClassification.from_pretrained(model_id).eval()

text = "We reduced Scope 2 emissions by 24% in 2024."
inputs = tok(text, return_tensors="pt", truncation=True)
pred = torch.softmax(mod(**inputs).logits, dim=-1)
label_id = pred.argmax(-1).item()
label = mod.config.id2label[str(label_id)]
print(label, float(pred[0][label_id]))

---

✅ Expected output:

Yes 0.94

---

🧠 Intended Use

This model acts as Stage 1 in the two-stage Green-Guard ESG classifier, filtering sustainability-related sentences before ESG-type categorization.

---

⚖️ License

MIT License — open for research and commercial reuse with attribution.