Update README.md

README.md

@@ -10,9 +10,9 @@ import torch
 from transformers import AutoTokenizer
 from modeling_reward import BERTRewardModel
 
-model_name = DarianNLP/modernbert-nl-sql
-tokenizer = AutoTokenizer.from_pretrained(DarianNLP/modernbert-nl-sql, trust_remote_code=True)
-model = BERTRewardModel(model_name="answerdotai/ModernBERT-base")
+model_name = "DarianNLP/modernbert-nl-sql"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = BERTRewardModel(model_name=model_name)
 state_dict = torch.load("model.safetensors")  # or use safetensors.torch.load_file
 model.load_state_dict(state_dict)
 model.eval()
@@ -34,5 +34,5 @@ For convenience, `modeling_reward.py` exposes `load_finetuned_model(model_dir)`
 - The reward target is bounded `[0, 1]` and already penalizes copied NL or incorrect reasoning.
 - The model uses mean pooling instead of CLS to better leverage long ModernBERT contexts.
 - Tokenizer files are saved from the finetuned run; no extra special tokens were introduced.
-- If you upload to the Hub and want `AutoModel.from_pretrained(..., trust_remote_code=True)` support, keep `modeling_reward.py` in the repo root.
+