How to get 'sbert_x.pt' and other files for new dataset ?

[naskk1]

If I want to train model on other dataset, how can I get model parameter file such as 'sbert_x.pt' in the dataset, it seems that there are no code for this.

[ChenRunjin]

Hi, you can find the code to generate sbert embedding in utils/data_process.py get_sbert_embedding() function.

[naskk1]

Thank you for your reply, but what is the input text template for embedding? I think this is different from the paradigm for Q&A. I have tried some, but it doesn't work well.

[ManuelSerna]

Hi,

May I ask how one would obtain the .jsonl files? Also the file processed_data.pt for a new dataset?

Thank you for your time.

[ChenRunjin]

Hi, due to variations in the raw data formats across different datasets, we don't have a single unified function for generating processed_data.pt. To create processed_data.pt for new datasets, you only need to generate a Data instance in PyG format, ensuring that edge_index is included in this instance. And ensuring data.label_texts to include all label name, data.raw_texts to include node text feature if you want to train on node description task.

In general, we follow the guidelines from this repo to generate the edge_index.

To create the *.jsonl file, the main task is to generate the node sequence that represents the structure surrounding each node using template in *.jsonl. You can use our get_fix_shape_subgraph_sequence_fast function in utils/data_process.py to generate the node sequence.

[honey0219]

After obtaining the processed_data.pt, sampled_2_10_test.jsonl, sampled_2_10_train.jsonl, and sampled_2_10_val.jsonl files for a new dataset, could you please let me know what additional steps I should take to run experiments in the "single focus" setting for the node classification task on the new dataset?
Thank you for your time!