'prettier'

.github/workflows/pre-commit.yaml

@@ -7,10 +7,9 @@ concurrency:
 on: [pull_request]
 
 jobs:
-
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
-    - uses: pre-commit/[email protected]
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+      - uses: pre-commit/[email protected]

.pre-commit-config.yaml

@@ -8,7 +8,7 @@ ci:
   autoupdate_branch: "master"
   autoupdate_commit_msg: "[pre-commit.ci] pre-commit autoupdate"
   autoupdate_schedule: quarterly
-  skip: [ ]
+  skip: []
   submodules: false
 
 repos:
@@ -29,22 +29,20 @@ repos:
       - id: check-case-conflict
       - id: forbid-new-submodules
       - id: pretty-format-json
-        args: [ "--autofix", "--no-sort-keys", "--indent=4" ]
+        args: ["--autofix", "--no-sort-keys", "--indent=4"]
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     rev: v0.4.8
     hooks:
       - id: ruff
-        args: [ "--ignore=E402,E501,F401", "--fix" ] #, --exit-non-zero-on-fix, ]
+        args: ["--ignore=E402,E501,F401", "--fix"] #, --exit-non-zero-on-fix, ]
       - id: ruff
         name: ruff lint data notebooks
-        args: [ "--fix", "--preview", "--select=NPY201" ]
+        args: ["--fix", "--preview", "--select=NPY201"]
       - id: ruff
-        args: [ "check", "--select", "I", "--fix"]
+        args: ["check", "--select", "I", "--fix"]
       - id: ruff-format
-        types_or: [ python, pyi, jupyter ]
-
-
+        types_or: [python, pyi, jupyter]
 
   - repo: https://github.com/codespell-project/codespell
     rev: v2.3.0
@@ -54,7 +52,7 @@ repos:
           - --ignore-words-list=metadat,splitted,meaned,wil,whats,additionals,alle,alot,bund,currenty,datas,farenheit,falsy,fo,haa,hass,iif,incomfort,ines,ist,nam,nd,pres,pullrequests,resset,rime,ser,serie,te,technik,ue,unsecure,withing,zar,MAPE,mape
           - --skip="./.*,*.csv,*.json,*.ambr"
           - --quiet-level=2
-        exclude_types: [ csv, json ]
+        exclude_types: [csv, json]
         exclude: ^tests/|generated/^.github
 
   - repo: https://github.com/asottile/blacken-docs

README.md

@@ -1,28 +1,39 @@
 # About
+
 Code for ["General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data"](https://openreview.net/forum?id=HwDQH0r37I)
 
 # Getting Started
+
 ## 0. Install the requirements
+
 To install the requirements, run the following command:
+
 ```bash
 pip install -r requirements.txt
 ```
+
 ## 1. Download and preprocess the data
+
 Download the NMT data from [here](https://ilabel.ai/datasets/Nust-Millitary-Hospital-TUKl-NMT-EEG-Dataset/NMT-Scalp-EEG.zip) and extract it to the `data` folder. or you can use the following command:
+
 ```bash
 wget https://ilabel.ai/datasets/Nust-Millitary-Hospital-TUKl-NMT-EEG-Dataset/NMT-Scalp-EEG.zip
 
 unzip NMT-Scalp-EEG.zip -d data
 ```
+
 or you can use the following command:
+
 ```bash
 gdown 'https://drive.google.com/uc?id=1jD_AcmfoaIfkOiO5lSU4J6IxHZtalnTk'
 
 unzip NMT.zip -d data/NMT/
 ```
 
 ## 2. Preprocess the data
+
 To preprocess the data, run the following command:
+
 ```bash
 python ./data/preprocess.py \
     --dataset nmt \
@@ -31,10 +42,13 @@ python ./data/preprocess.py \
     --exp_path ./data/NMT/NMT_dl/ \
     --nmt_raw_path ./data/NMT/nmt_scalp_eeg_dataset/
 ```
+
 It will preprocess the data and save it as .arrow files in the `data/NMT/nmt_dl/` folder.
 
 ## 3. Train the model
+
 To train the model, run the following command:
+
 ```bash
 accelerate launch bfm/train/train.py \
     --config bfm/configs/bfm-t5-base-nmt.yaml \
@@ -49,21 +63,27 @@ accelerate launch bfm/train/train.py \
     --n-gpus 4 \
     --max-steps 2000
 ```
+
 This will train the model on the NMT dataset using the T5-base model. You can modify the config file to use a different model or dataset.
 
 ## 4. Evaluate the model
+
 To evaluate the model, run the following command:
+
 ```bash
 CUDA_VISIBLE_DEVICES=0 python bfm/evaluate/evaluate.py \
     --config_path "bfm/configs/bfm-inference.yaml" \
     --directory_path "./bfm/Experiments/bfm-base_nmt" \
     --seed 2024 \
     --device "cuda"
 ```
+
 [Note:] You can also use 'data/download_moabb_datasets.py' to download the MOABB datasets. Then you can use 'data/preprocess_moabb.py' to preprocess the MOABB datasets and evaluate the model on them.
 
 # Citation
+
 If you find this code useful, please consider citing our paper:
+
 ```
 @inproceedings{
 bayazi2024generalpurpose,

bfm/configs/bfm-t5-base-nmt.yaml

@@ -1,7 +1,7 @@
 training_data_paths:
-- ./data/NMT/NMT_dl/nmt_dl/train/
+  - ./data/NMT/NMT_dl/nmt_dl/train/
 validation_data_paths:
-- ./data/NMT/NMT_dl/nmt_dl/val/
+  - ./data/NMT/NMT_dl/nmt_dl/val/
 dataset: "nmt"
 project_dir: "./bfm/"
 experiment_name: "test_experiment_v0"