Add more scientific skills

scientific-packages/transformers/scripts/quick_inference.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+"""
+Quick inference script using Transformers pipelines.
+
+This script demonstrates how to use various pipeline tasks for quick inference
+without manually managing models, tokenizers, or preprocessing.
+
+Usage:
+    python quick_inference.py --task text-generation --model gpt2 --input "Hello world"
+    python quick_inference.py --task sentiment-analysis --input "I love this!"
+"""
+
+import argparse
+from transformers import pipeline, infer_device
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Quick inference with Transformers pipelines")
+    parser.add_argument(
+        "--task",
+        type=str,
+        required=True,
+        help="Pipeline task (text-generation, sentiment-analysis, question-answering, etc.)",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="Model name or path (default: use task default)",
+    )
+    parser.add_argument(
+        "--input",
+        type=str,
+        required=True,
+        help="Input text for inference",
+    )
+    parser.add_argument(
+        "--context",
+        type=str,
+        default=None,
+        help="Context for question-answering tasks",
+    )
+    parser.add_argument(
+        "--max-length",
+        type=int,
+        default=50,
+        help="Maximum generation length",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default=None,
+        help="Device (cuda, cpu, or auto-detect)",
+    )
+
+    args = parser.parse_args()
+
+    # Auto-detect device if not specified
+    if args.device is None:
+        device = infer_device()
+    else:
+        device = args.device
+
+    print(f"Using device: {device}")
+    print(f"Task: {args.task}")
+    print(f"Model: {args.model or 'default'}")
+    print("-" * 50)
+
+    # Create pipeline
+    pipe = pipeline(
+        args.task,
+        model=args.model,
+        device=device,
+    )
+
+    # Run inference based on task
+    if args.task == "question-answering":
+        if args.context is None:
+            print("Error: --context required for question-answering")
+            return
+        result = pipe(question=args.input, context=args.context)
+        print(f"Question: {args.input}")
+        print(f"Context: {args.context}")
+        print(f"\nAnswer: {result['answer']}")
+        print(f"Score: {result['score']:.4f}")
+
+    elif args.task == "text-generation":
+        result = pipe(args.input, max_length=args.max_length)
+        print(f"Prompt: {args.input}")
+        print(f"\nGenerated: {result[0]['generated_text']}")
+
+    elif args.task in ["sentiment-analysis", "text-classification"]:
+        result = pipe(args.input)
+        print(f"Text: {args.input}")
+        print(f"\nLabel: {result[0]['label']}")
+        print(f"Score: {result[0]['score']:.4f}")
+
+    else:
+        # Generic handling for other tasks
+        result = pipe(args.input)
+        print(f"Input: {args.input}")
+        print(f"\nResult: {result}")
+
+
+if __name__ == "__main__":
+    main()