Update README.md

README.md

@@ -5,17 +5,18 @@ tags:
 metrics:
 - f1
 - accuracy
+- roc_auc
 model-index:
 - name: distilbert-base-uncased_research_articles_multilabel
   results: []
+language:
+- en
+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. -->
-
 # distilbert-base-uncased_research_articles_multilabel
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased).
 It achieves the following results on the evaluation set:
 - Loss: 0.1956
 - F1: 0.8395
@@ -24,15 +25,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a multilabel classification model of the topics included in research articles.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multilabel%20Classification/Research%20Articles-Multilabel%20clf.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/shivanandmn/multilabel-classification-dataset
 
 ## Training procedure
 
@@ -63,4 +66,4 @@ The following hyperparameters were used during training:
 - Transformers 4.21.3
 - Pytorch 1.12.1
 - Datasets 2.4.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1