Update README.md

README.md

@@ -3,20 +3,21 @@ license: apache-2.0
 base_model: bert-base-uncased
 tags:
 - generated_from_trainer
+- Multilabel
 metrics:
 - f1
 - accuracy
+- roc_auc
 model-index:
 - name: bert-base-uncased-Research_Articles_Multilabel
   results: []
+pipeline_tag: text-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. -->
-
 # bert-base-uncased-Research_Articles_Multilabel
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).
+
 It achieves the following results on the evaluation set:
 - Loss: 0.2039
 - F1: 0.8405
@@ -25,15 +26,15 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+Here is the link to my code for this model: https://github.com/DunnBC22/NLP_Projects/blob/main/Multilabel%20Classification/Research%20Articles/Research%20Articles%20-%20Multilabel%20Classification%20-%20Bert-Base-Uncased.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model could be used to read labels with printed text. You are more than welcome to use it, but remember that it is at your own risk/peril.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/shivanandmn/multilabel-classification-dataset
 
 ## Training procedure
 
@@ -56,10 +57,9 @@ The following hyperparameters were used during training:
 | 0.1739        | 2.0   | 4194 | 0.1986          | 0.8348 | 0.8926  | 0.7072   |
 | 0.1328        | 3.0   | 6291 | 0.2039          | 0.8405 | 0.8976  | 0.7082   |
 
-
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.4
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3