Create README.md

README.md

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+---
+language: en
+tags:
+- roberta
+- multilabel-classification
+- policy-analysis
+- huggingface
+datasets:
+- custom
+license: apache-2.0
+---
+
+# RoBERTa for Multi-label Classification of Policy Instruments
+
+This model fine-tunes `roberta-base` for multilabel classification of policies, targets, and themes.
+
+## Model Details
+- Base model: roberta-base
+- Max length: 512
+- Output: 47 multilabel classes (PI - Policy Instrument, TG - Target Group, TH - Theme). There are three main classes that have further sub-categories in them.
+- Threshold: 0.25
+
+## Intended Use
+Classify policy documents descriptions into thematic categories.
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+import joblib
+import requests
+
+model_path = "toqeerehsan/multilabel-indicator-classification"
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+mlb_url = "https://huggingface.co/toqeerehsan/multilabel-indicator-classification/resolve/main/mlb.pkl"
+mlb_path = "mlb.pkl"
+
+with open(mlb_path, "wb") as f:
+    f.write(requests.get(mlb_url).content)
+mlb = joblib.load(mlb_path)
+
+text = "This program supports clean technology and sustainable development in industries."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+model.eval()
+with torch.no_grad():
+    logits = model(**inputs).logits
+    probs = torch.sigmoid(logits).squeeze().numpy()
+
+# Threshold
+binary_preds = (probs > 0.25).astype(int)
+predicted_labels = [label for i, label in enumerate(mlb.classes_) if binary_preds[i] == 1]
+
+print("Predicted Labels:", predicted_labels)
+
+# Predicted Labels: ['PI007', 'PI008', 'TG20', 'TG21', 'TG22', 'TG25', 'TG29', 'TG31', 'TH31']