Improve model card: Add pipeline tag, library name, and relevant tags (#1)

- Improve model card: Add pipeline tag, library name, and relevant tags (3cd0fdaa10cb6b4ce123561e54be797f13f6592b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,19 +1,28 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-14B-Instruct
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- honesty-alignment
+- confidence-calibration
+- lora
+- peft
+- llm-alignment
 ---
+
 # Introduction
 
 This is the official repo of the paper [Annotation-Efficient Universal Honesty Alignment](https://arxiv.org/abs/2510.17509)
 
 This repository provides modules that extend **Qwen2.5-14B-Instruct** with the ability to generate accurate confidence scores *before* response generation, indicating how likely the model is to answer a given question correctly across tasks. We offer two types of modules—**LoRA + Linear Head** and **Linear Head**—along with model parameters under three training settings:
 
-1. **Elicitation (greedy):** Trained on all questions (over 560k) using self-consistency-based confidence annotations.
-2. **Calibration-Only (right):** Trained on questions with explicit correctness annotations.
-3. **EliCal (hybrid):** Initialized from the Elicitation model and further trained on correctness-labeled data.
+1.  **Elicitation (greedy):** Trained on all questions (over 560k) using self-consistency-based confidence annotations.
+2.  **Calibration-Only (right):** Trained on questions with explicit correctness annotations.
+3.  **EliCal (hybrid):** Initialized from the Elicitation model and further trained on correctness-labeled data.
 
 For both **Calibration-Only** and **EliCal** settings, we provide models trained with different amounts of annotated data (1k, 2k, 3k, 5k, 8k, 10k, 20k, 30k, 50k, 80k, 200k, 560k+). Since **LoRA + Linear Head** is the main configuration used in our paper, the following description is based on this setup.
 
@@ -131,4 +140,6 @@ base_model = AutoModel.from_pretrained(args.model_path)
 
 /mlp
 ...
-```
+```
+
+For more details, visit the [GitHub repository](https://github.com/Trustworthy-Information-Access/Annotation-Efficient-Universal-Honesty-Alignment).