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Updated intro and added syntax highlights for misc
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mkranzlein committed Aug 20, 2023
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A work-in-progress modified implementation of HiPool to support experiments on [CuRIAM](https://arxiv.org/abs/2305.14719).

This is **not the original repo for HiPool** and I am not an author on the HiPool paper. Please see that repo [here](https://github.com/irenezihuili/hipool).
HiPool is described in the paper "[HiPool: Modeling Long Documents Using Graph Neural Networks](https://aclanthology.org/2023.acl-short.16/)" from ACL 2023.

This is **not the original repo for HiPool and I am not an author on the HiPool paper**. Please see that repo [here](https://github.com/irenezihuili/hipool).

## Links
- HiPool paper: https://aclanthology.org/2023.acl-short.16/
- Original HiPool repo: https://github.com/irenezihuili/hipool
- HiPool's implementation is based on: https://github.com/helmy-elrais/RoBERT_Recurrence_over_BERT/blob/master/train.ipynb

## Setup
1. Create conda environment.
1. Create conda/mamba environment.
```
conda env create -f environment.yml
conda activate hipool
mamba env create -f environment.yml
mamba activate hipool
```
2. Install hipool locally.
```
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This repo uses [jaxtyping](https://github.com/google/jaxtyping) and [typeguard](https://typeguard.readthedocs.io/) to enforce correct tensor dimensions at runtime. If you see an unfamiliar type annotation or decorators like in the example the below, it's for type checking.
```
```python
@jaxtyped
@typechecked
def some_function(x: Float[torch.Tensor, "10, 768"]):
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