Merge branch 'rebase-txt2kg' of https://github.com/pyg-team/pytorch_g…

…eometric into rebase-txt2kg
pyg-team · Jan 8, 2025 · 857d35a · 857d35a
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diff --git a/examples/llm/g_retriever_utils/README.md b/examples/llm/g_retriever_utils/README.md
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 # Examples for LLM and GNN co-training
 
-| Example                                                          | Description                                                                                                                                                               |
-| ---------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [`benchmark_model_archs_rag.py`](./benchmark_model_archs_rag.py) | Script for running a GNN/LLM benchmark on GRetriever while grid searching relevent architecture parameters and datasets.                                                  |
-| [`minimal_demo.py`](./minimal_demo.py) | Minimal demo for WebQSP dataset comparing GNN+LLM vs LLM                                                |
+| Example                                                          | Description                                                                                                              |
+| ---------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ |
+| [`benchmark_model_archs_rag.py`](./benchmark_model_archs_rag.py) | Script for running a GNN/LLM benchmark on GRetriever while grid searching relevent architecture parameters and datasets. |
+| [`minimal_demo.py`](./minimal_demo.py)                           | Minimal demo for WebQSP dataset comparing GNN+LLM vs LLM                                                                 |
 
 NOTE: Evaluating performance on GRetriever with smaller sample sizes may result in subpar performance. It is not unusual for the fine-tuned model/LLM to perform worse than an untrained LLM on very small sample sizes.