Improve model card: Add paper link and refine description (#2)

- Improve model card: Add paper link and refine description (2c8c0ba506f7c5576909d1fd19627d656b496a91)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,4 +1,11 @@
 ---
+base_model:
+- meta-llama/Meta-Llama-3-8B
+datasets:
+- future7/CogniBench
+- future7/CogniBench-L
+language:
+- en
 library_name: transformers
 pipeline_tag: text-generation
 tags:
@@ -7,19 +14,11 @@ tags:
 - RAG evaluation
 - cognitive statements
 - factual consistency
-datasets:
-- future7/CogniBench
-- future7/CogniBench-L
-language:
-- en
-base_model:
-- meta-llama/Meta-Llama-3-8B
 ---
 
-
 # CogniDet: Cognitive Faithfulness Detector for LLMs
 
-**CogniDet** is a state-of-the-art model for detecting **both factual and cognitive hallucinations** in Large Language Model (LLM) outputs. Developed as part of the [CogniBench](https://github.com/FUTUREEEEEE/CogniBench) framework, it specifically addresses the challenge of evaluating inference-based statements beyond simple fact regurgitation.
+**CogniDet** is a state-of-the-art model for detecting **both factual and cognitive hallucinations** in Large Language Model (LLM) outputs. Developed as part of the [CogniBench](https://github.com/FUTUREEEEEE/CogniBench) framework, it specifically addresses the challenge of evaluating inference-based statements beyond simple fact regurgitation. The model is presented in the paper [CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models](https://huggingface.co/papers/2505.20767).
 
 ## Key Features ✨
 1. **Dual Detection Capability**  
@@ -55,7 +54,9 @@ model = AutoModelForCausalLM.from_pretrained(model_id)
 
 def detect_hallucinations(context, response):
     inputs = tokenizer(
-        f"CONTEXT: {context}\nRESPONSE: {response}\nHALLUCINATIONS:",
+        f"CONTEXT: {context}
+RESPONSE: {response}
+HALLUCINATIONS:",
         return_tensors="pt"
     )
     outputs = model.generate(**inputs, max_new_tokens=100)