Add pipeline tag and library name metadata

README.md

@@ -1,37 +1,39 @@
----
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
-datasets:
-- infosense/yield
-language:
-- en
-license: llama3.1
----
-
-# YIELD Fine-Tuning Adapters
-
-This repository contains the persona adapter models presented in the paper [YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents](https://doi.org/10.48550/arXiv.2604.10968).
-
-## Model Information
-
-All adapters in this repository are LoRA adapters trained on top of the `Llama-3.1-8B-Instruct`, `Llama-3.2-3B-Instruct`, and `DeepSeek-R1-Distill-Llama-8B` models. All models are fine-tuned using both the Supervised Fine-Tuning (SFT) and Offline Reinforcement-Learning (ORL) pipelines detailed in the paper.
-
-## Resources
-
-- **Code Repository**: [GitHub - infosenselab/yield](https://github.com/infosenselab/yield)
-- **Dataset**: [infosense/yield](https://huggingface.co/datasets/infosense/yield)
-- **Paper**: [arXiv:](https://doi.org/10.48550/arXiv.2604.10968)
-
-## Citing YIELD
-
-If you use this resource in your projects, please cite the following paper:
-
-```bibtex
-@misc{De_Lima_YIELD_A_Large-Scale_2026,
-author = {De Lima, Victor and Yang, Grace Hui},
-doi = {10.48550/arXiv.2604.10968},
-title = {{YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents}},
-url = {https://arxiv.org/abs/2604.10968},
-year = {2026}
-}
+---
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+datasets:
+- infosense/yield
+language:
+- en
+license: llama3.1
+library_name: peft
+pipeline_tag: text-generation
+---
+
+# YIELD Fine-Tuning Adapters
+
+This repository contains the persona adapter models presented in the paper [YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents](https://huggingface.co/papers/2604.10968).
+
+## Model Information
+
+All adapters in this repository are LoRA adapters trained on top of the `Llama-3.1-8B-Instruct`, `Llama-3.2-3B-Instruct`, and `DeepSeek-R1-Distill-Llama-8B` models. All models are fine-tuned using both the Supervised Fine-Tuning (SFT) and Offline Reinforcement-Learning (ORL) pipelines detailed in the paper. These models are designed to act as Information Elicitation Agents (IEAs), which aim to elicit information from users to support institutional or task-oriented objectives.
+
+## Resources
+
+- **Code Repository**: [GitHub - infosenselab/yield](https://github.com/infosenselab/yield)
+- **Dataset**: [infosense/yield](https://huggingface.co/datasets/infosense/yield)
+- **Paper**: [Hugging Face Papers](https://huggingface.co/papers/2604.10968)
+
+## Citing YIELD
+
+If you use this resource in your projects, please cite the following paper:
+
+```bibtex
+@misc{De_Lima_YIELD_A_Large-Scale_2026,
+author = {De Lima, Victor and Yang, Grace Hui},
+doi = {10.48550/arXiv.2604.10968},
+title = {{YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents}},
+url = {https://arxiv.org/abs/2604.10968},
+year = {2026}
+}
 ```