chore: import upstream snapshot with attribution

doc/source/data/doc_code/working-with-llms/classification_example.py

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+"""
+Classification batch inference with Ray Data LLM.
+
+Uses sequence classification models for content classifiers and sentiment analyzers.
+"""
+
+# Dependency setup
+import subprocess
+import sys
+
+subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "ray[llm]"])
+subprocess.check_call(
+    [sys.executable, "-m", "pip", "install", "--upgrade", "transformers"]
+)
+subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy==1.26.4"])
+
+
+# __classification_example_start__
+import ray
+from ray.data.llm import vLLMEngineProcessorConfig, build_processor
+
+# Configure vLLM for a sequence classification model
+classification_config = vLLMEngineProcessorConfig(
+    model_source="nvidia/nemocurator-fineweb-nemotron-4-edu-classifier",
+    task_type="classify",  # Use 'classify' for sequence classification models
+    engine_kwargs=dict(
+        max_model_len=512,
+        enforce_eager=True,
+    ),
+    batch_size=8,
+    concurrency=1,
+    chat_template_stage=False,
+    detokenize_stage=False,
+)
+
+classification_processor = build_processor(
+    classification_config,
+    preprocess=lambda row: dict(prompt=row["text"]),
+    postprocess=lambda row: {
+        "text": row["prompt"],
+        # Classification models return logits in the 'embeddings' field
+        "edu_score": float(row["embeddings"][0])
+        if row.get("embeddings") is not None and len(row["embeddings"]) > 0
+        else None,
+    },
+)
+
+# Sample texts with varying educational quality
+texts = [
+    "lol that was so funny haha",
+    "Photosynthesis converts light energy into chemical energy.",
+    "Newton's laws describe the relationship between forces and motion.",
+]
+ds = ray.data.from_items([{"text": text} for text in texts])
+
+if __name__ == "__main__":
+    try:
+        import torch
+
+        if torch.cuda.is_available():
+            classified_ds = classification_processor(ds)
+            classified_ds.show(limit=3)
+        else:
+            print("Skipping classification run (no GPU available)")
+    except Exception as e:
+        print(f"Skipping classification run due to environment error: {e}")
+# __classification_example_end__
+
+