Update README.md

README.md

@@ -1,10 +1,16 @@
 ---
 base_model:
 - autogluon/chronos-t5-large
+tags:
+- General-Purpose
+- Brain
+- Foundation Models
+- electroencephalogram 
+- signal processing
 ---
-General-Purpose Brain Foundation Models is trained on electroencephalogram signal data.
+General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data
 
-[[paper]()]
+[[General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data](https://openreview.net/forum?id=HwDQH0r37I)]
 ## Model Details
 
 ### Model Description
@@ -13,14 +19,13 @@ General-Purpose Brain Foundation Models is trained on electroencephalogram signa
 
 - **Model type:** T5-large
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** Chronos-t5-large
+- **Finetuned from model:** Chronos-t5-large
 
 ## How to Get Started with the Model
 
-Here is [colab](https://colab.research.google.com/drive/1Dy6oUJeYuoi0cAo9imlfA2gZgueMun9h?usp=sharing) notebook for inference of Moabb dataset.
+Here is [colab](https://colab.research.google.com/drive/1Dy6oUJeYuoi0cAo9imlfA2gZgueMun9h?usp=sharing) notebook for inference of the Moabb dataset.
 
 ### Training Data:
-Training data used was NMT EEG dataset. NMT is an open-source, annotated dataset comprising healthy and pathological EEG recordings. It consists of 2,417 recordings from unique participants, providing multichannel EEG data along with labels indicating the participants' pathological state, classified as normal or abnormal. Each EEG channel is treated as an independent time series, which is further divided into two segments: a context window for conditioning and a prediction target window.
-
-Hyperparameters are the same as used in Chronos paper.
+The training data used was the NMT EEG dataset. NMT is an open-source, annotated dataset comprising healthy and pathological EEG recordings. It consists of 2,417 recordings from unique participants, providing multichannel EEG data along with labels indicating the participants' pathological state, classified as normal or abnormal. Each EEG channel is treated as an independent time series, which is further divided into two segments: a context window for conditioning and a prediction target window.
 
+Hyperparameters are the same as those used in the Chronos paper.