Upload folder using huggingface_hub

README.md

@@ -84,8 +84,33 @@ For **26 parent subjects**, F1-score improves to **0.934** with full metadata.
 ## 🔍 Example Usage
 
 ```python
-from transformers import pipeline
+from transformers import TextClassificationPipeline, pipeline
+import torch
+
+class ASJCMultiLabelPipeline(TextClassificationPipeline):
+    def __init__(self, *args, **kwargs):
+        self.threshold = kwargs.pop("threshold", None)
+        super().__init__(*args, **kwargs)
+
+        # If no explicit threshold passed → use threshold from config.json
+        if self.threshold is None:
+            self.threshold = getattr(self.model.config, "threshold", 0.3)
+
+    def postprocess(self, model_outputs, **kwargs):
+        scores = torch.sigmoid(torch.tensor(model_outputs["logits"])).tolist()
+        results = []
+
+        for i, score in enumerate(scores):
+            if score >= self.threshold:
+                label = self.model.config.id2label[str(i)]
+                results.append({"label": label, "score": float(score)})
+
+        # Sort by score descending
+        results = sorted(results, key=lambda x: x["score"], reverse=True)
+        return results
+```
 
+```python
 pipe = pipeline("text-classification", model="asjc-classification/scibert_multilabel_asjc_classifier")
 
 text = (