Update README.md

README.md

@@ -1,4 +1,57 @@
-Usage Instructions: https://github.com/jbdel/StructEval/tree/main
+Usage:
+
+```python
+import json
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+from datasets import load_dataset
+import requests
+
+# Configuration
+MODEL_PATH = "StanfordAIMI/SRR-BERT-Upper"
+MAPPING_URL = "https://raw.githubusercontent.com/jbdel/StructEval/refs/heads/main/structeval/upper_mapping.json"
+MAX_LENGTH = 128
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Fetch mapping from GitHub
+resp = requests.get(MAPPING_URL)
+resp.raise_for_status()
+label_map = resp.json()
+idx2label = {v: k for k, v in label_map.items()}
+
+# Load tokenizer & model
+tokenizer = BertTokenizer.from_pretrained("microsoft/BiomedVLP-CXR-BERT-general")
+model = BertForSequenceClassification.from_pretrained(MODEL_PATH, num_labels=len(label_map))
+model.to(DEVICE).eval()
+
+# Grab one test sentence
+dataset = load_dataset("StanfordAIMI/StructUtterances", split="test_reviewed")
+sentence = dataset[35]["utterance"]
+
+# Tokenize and infer
+inputs = tokenizer(
+    sentence,
+    padding="max_length",
+    truncation=True,
+    max_length=MAX_LENGTH,
+    return_tensors="pt"
+).to(DEVICE)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+    preds = (torch.sigmoid(logits)[0].cpu().numpy() > 0.5).astype(int)
+
+pred_labels = [idx2label[i] for i, flag in enumerate(preds) if flag]
+
+print(f"Sentence: {sentence}")
+print("Predicted labels:", pred_labels)
+```
+Output:
+```
+Sentence: Patchy consolidation in the left retrocardiac area, suggestive of atelectasis or early airspace disease.
+Predicted labels: ['Consolidation', 'Air space opacity']
+```
+
 
 Label Mapping:
 ```