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---
base_model:
- meta-llama/Llama-3.2-3B-Instruct
datasets:
- snap-stanford/stark
metrics:
- recall
pipeline_tag: question-answering
library_name: transformers
license: mit
---
# MoR
This model card for our paper [Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases](https://arxiv.org/pdf/2502.20317).
Code: https://github.com/Yoega/MoR
# Running the Evaluation and Reranking Script
## Installation
To set up the environment, you can install dependencies using Conda or pip:
### Using Conda
```bash
conda env create -f mor_env.yml
conda activate your_env_name # Replace with actual environment name
```
### Using pip
```bash
pip install -r requirements.txt
```
## Inference
To run the inference script, execute the following command in the terminal:
```bash
bash eval_mor.sh
```
This script will automatically process three datasets using the pre-trained planning graph generator and the pre-trained reranker.
## Training (Train MoR from Scratch)
### Step1: Training the planning graph generator
```bash
bash train_planner.sh
```
### Step2: Train mixed traversal to collect candidates (note: there is no training process for reasoning)
```bash
bash run_reasoning.sh
```
### Step3: Training the reranker
```bash
bash train_reranker.sh
```
## Generating training data of Planner
### We provide codes to generate your own training data to finetune the Planner by using different LLMs.
#### If you are using Azure API
```bash
python script.py --model "model_name" \
--dataset_name "dataset_name" \
--azure_api_key "your_azure_key" \
--azure_endpoint "your_azure_endpoint" \
--azure_api_version "your_azure_version"
```
#### If you are using OpenAI API
```bash
python script.py --model "model_name" \
--dataset_name "dataset_name" \
--openai_api_key "your_openai_key" \
--openai_endpoint "your_openai_endpoint"
``` |