Add automatic token detection for HF authentication

- Use get_token() to auto-detect token from huggingface-cli login
- Check HF_TOKEN env var and --hf-token argument
- Provide clear error messages when no token is found

classify-dataset.py

@@ -40,7 +40,7 @@ from typing import List, Dict, Any, Optional
 
 import torch
 from datasets import load_dataset, Dataset
-from huggingface_hub import HfApi
+from huggingface_hub import HfApi, get_token
 from vllm import LLM, SamplingParams
 from vllm.sampling_params import GuidedDecodingParams
 
@@ -140,8 +140,8 @@ def parse_args():
     parser.add_argument(
         "--hf-token",
         type=str,
-        default=os.environ.get("HF_TOKEN"),
-        help="Hugging Face API token (default: HF_TOKEN env var)"
+        default=None,
+        help="Hugging Face API token (default: auto-detect from HF_TOKEN env var or huggingface-cli login)"
     )
     parser.add_argument(
         "--split",
@@ -341,12 +341,25 @@ def main():
     success_rate = (valid_texts / total_texts * 100) if total_texts > 0 else 0
     logger.info(f"Classification success rate: {success_rate:.1f}%")
     
+    # Get token for authentication
+    token = args.hf_token
+    if not token:
+        # Try to get token from environment or huggingface-cli login
+        token = os.environ.get("HF_TOKEN") or get_token()
+    
+    if not token:
+        logger.error("No authentication token found. Please either:")
+        logger.error("1. Run 'huggingface-cli login'")
+        logger.error("2. Set HF_TOKEN environment variable")
+        logger.error("3. Pass --hf-token argument")
+        sys.exit(1)
+    
     # Save to Hub
     logger.info(f"Pushing dataset to Hub: {args.output_dataset}")
     try:
         dataset.push_to_hub(
             args.output_dataset,
-            token=args.hf_token,
+            token=token,
             commit_message=f"Add classifications using {args.model} with structured outputs"
         )
         logger.info(f"Successfully pushed to: https://huggingface.co/datasets/{args.output_dataset}")