Update README.md

README.md

@@ -5,6 +5,9 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: dit-base-Business_Documents_Classified_v2
   results:
@@ -21,11 +24,11 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.826
+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. -->
-
 # dit-base-Business_Documents_Classified_v2
 
 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
@@ -44,15 +47,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a classification model of 16 different types of documents.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Real%20World%20Documents%20Collections/Real%20World%20Documents%20Collections_v2.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/shaz13/real-world-documents-collections
 
 ## Training procedure
 
@@ -93,10 +98,9 @@ The following hyperparameters were used during training:
 | 0.7993        | 16.99 | 531  | 0.6718          | 0.825    | 0.8259      | 0.825    | 0.8234   | 0.825           | 0.825        | 0.8227       | 0.8306             | 0.825           | 0.8282          |
 | 0.7954        | 17.86 | 558  | 0.6715          | 0.826    | 0.8272      | 0.826    | 0.8242   | 0.826           | 0.826        | 0.8237       | 0.8327             | 0.826           | 0.8293          |
 
-
 ### Framework versions
 
 - Transformers 4.28.1
 - Pytorch 2.0.0+cu118
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3