| # TEG Datasets | |
| ## Dataset Format | |
| Each dataset is a [PyG Data object](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Dataset.html#torch_geometric.data.Dataset) and is stored in the `processed` subdir following a unified format, with each attribute defined as follows: | |
| - `edge_index`: Graph connectivity in COO format with shape [2, num_edges] and type `torch.long`. | |
| - `text_nodes`: `List` contains textual information for each node in the graph. | |
| - `text_edges`: `List` contains textual information for each edge in the graph. | |
| - `node_labels`: labels or classes for each node in the graph. | |
| - `edge_labels`: labels or classes for each edge in the graph and type `torch.long`. | |
| ## Embedding Data Format | |
| The embedding data is thrived from `text_nodes` and `text_edges` through PLM including: | |
| - [GPT](https://platform.openai.com/docs/guides/embeddings) | |
| - [BERT-base](https://huggingface.co/bert-base-uncased) | |
| - [BERT-large](https://huggingface.co/bert-large-uncased) | |
| **We will provide more TEG datasets and PLM embedding in the futrue!** | |