grte's Introduction

Graph Receptive Transformer Encoder for Text Classification

This repository contains the code for the "Graph Receptive Transformer Encoder for Text Classification" paper published in IEEE Transactions on Signal and Information Processing over Networks. Please cite the following paper if you use this code in your research:

@article{grte2024,
  author   = {Aras, Arda Can and Alikaşifoğlu, Tuna and Koç, Aykut},
  journal  = {IEEE Transactions on Signal and Information Processing over Networks},
  title    = {Graph Receptive Transformer Encoder for Text Classification},
  year     = {2024},
  volume   = {10},
  number   = {},
  pages    = {347-359},
  doi      = {10.1109/TSIPN.2024.3380362}
}

Installation

This codebase utilizes Poetry for package management. To install the dependencies, in the root directory run:

poetry install

or if you do not want to use Poetry, or use the exact pinned versions, you can install the dependencies provided in requirements.txt using pip or conda, e,g.,

pip install -r requirements.txt

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running process

Hello, how does your project run? Can you give me a running process? In the util_fncs.py file, def get_encoder_outputs(dataset_name):
emb_path = f"/Users/ardaaras/Documents/finetune-text-graphs/generator-output/{dataset_name}_embeddings.pth"
cls_logits_path = f"/Users/ardaaras/Documents/finetune-text-graphs/generator-output/{dataset_name}_logits.pth"
x = torch.load(emb_path)
cls_logits = torch.load(cls_logits_path)
return x, cls_logits How the emb_path and cls_logits_path paths in this function are generated is not given in the two path projects.

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grte's Introduction

Graph Receptive Transformer Encoder for Text Classification

Installation

grte's People

Contributors

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grte's Issues

running process

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