Update README.md

README.md

@@ -23,7 +23,6 @@ There are several models that were trained on the ClotSimNet dataset:
 The EfficientNet and ResNet models were chosen due to their precedence in the biomedical imaging field. ConvNeXt-Tiny was selected to explore a new architecture based on vision transformers to assess its performance relative to the more conventional CNN architectures.
 
 
-
 - **Developed by:** Josh Gregory
 
 
@@ -51,11 +50,11 @@ As of this publication, these models are not intended to treat, diagnose, or pre
 
 Each model was exported in [ONNX](https://onnx.ai/) (`.onnx`) and [Safetensors](https://huggingface.co/docs/safetensors/index) (`.safetensors`).
 
-See the `inference_examples` directory for how to inference these models in safetensors format. For futher examples in ONNX, refer to the GitHub repository, with examples located in the path `/poresimnet/ml/inference`.
+See the `inference_examples` directory for how to inference these models in Safetensors format. For futher examples in ONNX, refer to the GitHub repository, with examples located in the path `/poresimnet/ml/inference`.
 
 ### Loading the Models
 
-See the `inference_examples` directory. Models in the safetensors format were used the most for internal inference work.
+See the `inference_examples` directory. Models in the Safetensors format were used the most for internal inference work.
 
 ## Training Details
 
@@ -83,7 +82,7 @@ Refer to the Dataset card to find the relevant testing data, as different kinds
 
 #### Summary
 
-The `base` directory contains all trained models with base hyperparameters. The `tuned` directory contains the models after hyperparameter tuning. The `hyperparameters` folder itself contains each model's tuned hyperparameters in a JSON file, and the `inference_examples` directory contains examples for how to inference our models in safetensors format.
+The `base` directory contains all trained models with base hyperparameters. The `tuned` directory contains the models after hyperparameter tuning. The `hyperparameters` folder itself contains each model's tuned hyperparameters in a JSON file, and the `inference_examples` directory contains examples for how to inference our models in Safetensors format.
 
 ## Model Examination
 
@@ -115,7 +114,7 @@ Several architectures were considered. Both MLPs and CNNs were selected due to t
 
 ### Compute Infrastructure
 
-This work utilized the Alpine high performance computing resource at the University of Colorado Boulder. Alpine is jointly funded by the University of Colorado Boulder, the University of Colorado Anschutz, and Colorado State University and with support from NSF grants OAC-2201538 and OAC-2322260
+This work utilized the Alpine high performance computing resource at the University of Colorado Boulder. Alpine is jointly funded by the University of Colorado Boulder, the University of Colorado Anschutz, and Colorado State University and with support from NSF grants OAC-2201538 and OAC-2322260.
 
 #### Hardware
 
@@ -139,7 +138,7 @@ All models were written in PyTorch Lightning.
 
 ## Model Card Authors
 
-Josh Gregory
+Josh Gregory: josh.a.gregory42@gmail.com
 
 ## Model Card Contact