Update README.md

README.md

@@ -1,6 +1,20 @@
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+tags:
+- text-classification
+- spam
+- english
+---
 # Fine-tuned BERT-base-uncased pre-trained model to classify spam SMS.
 
-Github: https://github.com/fzn0x/bert-sms-classification
+Check Github for Eval Results logs: https://github.com/fzn0x/bert-sms-classification
 
 My second project in Natural Language Processing (NLP), where I fine-tuned a bert-base-uncased model to classify spam SMS. This is huge improvements from https://github.com/fzn0x/bert-indonesian-english-hate-comments.
 
@@ -62,4 +76,4 @@ See [`citations.bib`](./citations.bib) for full BibTeX entries.
 - [scikit-learn](https://scikit-learn.org/stable/) – metrics and preprocessing
 - Logging silencing inspired by Hugging Face GitHub discussions
 - Dataset from [UCI SMS Spam Collection](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset)
-- Inspiration from [Kaggle Notebook by Suyash Khare](https://www.kaggle.com/code/suyashkhare/naive-bayes)
+- Inspiration from [Kaggle Notebook by Suyash Khare](https://www.kaggle.com/code/suyashkhare/naive-bayes)