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additional documentation

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2025-07-02 14:28:34 -07:00
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README.md
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@@ -25,6 +25,15 @@ The code is organized into four main directories: `utils`, `analyses`, `data`, a
- The `model_training` directory contains the code necessary to train and evaluate the brain-to-text model. See the README.md in that folder for more detailed instructions.
- The `language_model` directory contains the ngram language model implementation and a pretrained 1gram language model. Pretrained 3gram and 5gram language models can be downloaded [here](https://datadryad.org/dataset/doi:10.5061/dryad.x69p8czpq) (`languageModel.tar.gz` and `languageModel_5gram.tar.gz`). See the `README.md` in this directory for more information.
## Data
The data used in this repository consists of various datasets for recreating figures and training/evaluating the brain-to-text model:
- `t15_copyTask.pkl`: This file contains the online Copy Task results required for generating Figure 2.
- `t15_personalUse.pkl`: This file contains the Conversation Mode data required for generating Figure 4.
- `t15_copyTask_neuralData.zip`: This dataset contains the neural data for the Copy Task. There are more than 11,300 sentences from 45 sessions spanning 20 months. The data is split into training, validation, and test sets. Data for each session/split is stored in `.hdf5` files. An example of how to load this data using the Python `h5py` library is provided in the `model_training/evaluate_model_helpers.py` file in the `load_h5py_file()` function.
- `t15_pretrained_rnn_baseline.zip`: This dataset contains the pretrained RNN baseline model checkpoint and args. An example of how to load this model and use it for inference is provided in the `model_training/evaluate_model.py` file.
Please download these datasets from [Dryad](https://datadryad.org/stash/dataset/doi:10.5061/dryad.dncjsxm85) and place them in the `data` directory. Be sure to unzip both datasets before running the code.
## Dependencies
- The code has only been tested on Ubuntu 22.04 with two NVIDIA RTX 4090 GPUs.
- We recommend using a conda environment to manage the dependencies. To install miniconda, follow the instructions [here](https://docs.anaconda.com/miniconda/miniconda-install/).