Update README.md

README.md

@@ -100,5 +100,55 @@ BERT Natural Language Outputs:
 (2) Classification probability for the designated religious affiliations (and classification probability)  
 (3) Classification probability for whether the organisation is religious or not (and probability)
 
-
+# 5\. Example usage
+
+This is a minimal-code example of how to use the to classify texts as related to a religion (or none)
+This model is public and doesn't require login to download. Use `snapshot_download` to get the full model, latest version.
+```
+repo_id = 'GivingTuesday/religious_org_v1'
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id)
+
+# snapshot_download will return / print a location for the model. Pass that into the function below.
+from transformers import pipeline
+# 1. Define the local path to your downloaded model directory
+# Replace './my_local_model_directory' with the actual path on your machine
+model_path = './my_local_model_directory' # copied from output of previous cell
+
+# 2. Load the text classification pipeline from the local directory
+# The 'pipeline' function automatically loads the model, tokenizer, and config
+# from the specified local path.
+try:
+    classifier = pipeline(
+        task="text-classification",
+        model=model_path,
+        tokenizer=model_path
+    )
+    print(f"Model loaded successfully from {model_path}")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    print("Please ensure the directory contains the model weights, config, and tokenizer files.")
+    exit()
+
+# 3. Define the string(s) you want to classify
+sentences = [
+    """St. John's Episcopal is a really beautiful community that preaches "faith in action" -- they welcome and assist migrants, partner with community organizations, run a housing first apartment building for unhoused folks, and they are part of the Together Colorado coalition of faith-based organizations that lobbies for anti-poverty legislation. Bonus: the cathedral is gorgeous and historic.""",
+    """I’m atheist but rehearse in a community choir at Christ Church United Methodist and they have pro LGBTQ+ stuff everywhere. They even do a reconciliation Sunday service every year in June during pride month""",
+]
+
+# 4. Run the classification on the input string(s)
+results = classifier(sentences)
+
+# 5. Print the results
+for i, result in enumerate(results):
+    print(f"\nText: '{sentences[i]}'")
+    print(f"Classification Results:")
+    # Results can be a list of dictionaries if top_k is used
+    if isinstance(result, list):
+        for label_info in result:
+            print(f"- Label: {label_info['label']}, Score: {label_info['score']:.4f}")
+    else:
+        print(f"- Label: {result['label']}, Score: {result['score']:.4f}")
+
+```