Update README.md

README.md

@@ -9,31 +9,55 @@ base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
 model-index:
 - name: sft_trained_woaqa_mixtral
   results: []
+datasets:
+- jiazhengli/Rationale_MCTS
+- jiazhengli/Synthetic_Rationale
+language:
+- en
+metrics:
+- accuracy
+- f1
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Mixtral-8x7B-Instruct-v0.1-QLoRA-Assessment-Rationale-sft
 
-# sft_trained_woaqa_mixtral
+The model trained with w/o private data from the EMNLP 2024 Paper: Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 
 
-This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the sft_wo_aqa_mistral dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8062
-
-## Model description
-
-More information needed
+- **Paper:** [Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring](https://arxiv.org/abs/2406.19949) (EMNLP 2024 Findings)
+- **GitHub Repository:** [Thought Tree Assessment Repository](https://github.com/lijiazheng99/thought_tree_assessment)
 
 ## Intended uses & limitations
 
-More information needed
+This model offers a valuable resource for research in explainable AI within educational technology. The model is trained with **noisy** response-level rationales. This makes them **unsuitable** for direct application in high-stakes assessments without additional verification.
 
 ## Training and evaluation data
 
-More information needed
+We trained and evaluated the model on the [Synthetic Rationale data](https://huggingface.co/datasets/jiazhengli/Synthetic_Rationale), which was generated from the [Rationale MCTS data](https://huggingface.co/datasets/jiazhengli/Rationale_MCTS).
+
+To extract scores from rationales, please use the [jiazhengli/deberta-v3-large-Rationale-to-Score](https://huggingface.co/jiazhengli/deberta-v3-large-Rationale-to-Score).
+
+## Citation
+
+Please cite the following work if you utilize this model:
+
+**BibTeX:**
+
+```bibtex
+@misc{li2024calibratingllmspreferenceoptimization,
+      title={Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring}, 
+      author={Jiazheng Li and Hainiu Xu and Zhaoyue Sun and Yuxiang Zhou and David West and Cesare Aloisi and Yulan He},
+      year={2024},
+      eprint={2406.19949},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2406.19949}, 
+}
+```
 
 ## Training procedure
 
+Please refer to our [paper](https://arxiv.org/abs/2406.19949).
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: