Improve model card: Add paper link and refine description

This PR adds a link to the paper and refines the description of the CogniDet model. It also ensures the model card includes all necessary information, such as usage examples, training data details, limitations, and citation information.

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)