Update README.md

README.md

@@ -30,15 +30,15 @@ model-index:
       value: 0.9375
     - name: Recall
       type: recall
-      value: 1.0
+      value: 1
     - name: Precision
       type: precision
       value: 0.8823529411764706
+language:
+- en
+pipeline_tag: image-classification
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # vit-base-patch16-224-in21k_brain_tumor_diagnosis
 
 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
@@ -51,15 +51,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a binary classification model to distinguish between if the MRI images detect a brain tumor or not.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Binary%20Classification/Brain%20Tumor%20MRI%20Images/brain_tumor_MRI_Images_ViT.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
 
 ## Training procedure
 
@@ -90,4 +92,4 @@ The following hyperparameters were used during training:
 - Transformers 4.25.1
 - Pytorch 1.12.1
 - Datasets 2.8.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1